http://cmb.ornl.gov
Welcome!

The University of Tennessee (UT) / Oak Ridge National Laboratory (ORNL) 

Center for Molecular Biophysics (CMB), was founded in October 2006. With an 

approximately 50/50 UT/ORNL personnel mix, the center has a vibrant research 

atmosphere.

Thematically, our research is heavily influenced by the ‘mission space’ of ORNL 

and DOE. Supercomputing and neutron scattering constitute central toolsets that 

we integrate into our investigations in bioenergy and subsurface biogeochemistry.

At the same time, we also have programs in biomedical sciences, including a 

successful drug discovery program. Our research is strongly interdisciplinary, 

incorporating elements of theoretical physics, quantum chemistry, statistical 

mechanics and simulation methodologies through to molecular and synthetic 

systems biology. Our team of principal investigators comprises myself, three 

other UT professors and three ORNL Staff Scientists. We have two UT Associate 

Professors: Jerome Baudry, who specializes in ligand binding and computational 

biochemistry, and Tongye Shen, who is more physics-oriented. Hong Guo, a Full 

Professor who I first met in 1982 in Martin Karplus’ group at Harvard, specializes 

in enzyme reaction mechanisms.

The first ORNL staff scientist to be hired, in 2008, was Xiaolin Cheng, and two 

more were subsequently appointed, Jerry Parks and Loukas Petridis. Again, these 

three have complementary expertise, with Xiaolin experienced in simulation 

methodologies and ion channels, Loukas coming from polymer physics, and Jerry 

a quantum chemist.

The above team of principal investigators has worked together with our 

postdoctoral fellows, graduate and undergraduate students to produce many 

successful grant proposals and nearly 300 peer-reviewed publications. The 

publications include reports on a number of breakthroughs in fields of research 

of national importance, and some of the corresponding press releases by UT 

or ORNL are reprinted here. Our research, as well as the challenges ahead, is 

discussed here in an informal style from the point of view of the young scientists 

who actually did the work.

I hope you find our booklet a stimulating read!

Jeremy C. Smith, Director, CMB.

CMB is affiliated with the Biosciences Division of the Energy and 

Engineering Sciences Directorate at ORNL and with the Department 

of Biochemistry and Molecular and Cellular Biology in the College of 

Arts and Sciences at the University of Tennessee, and we thank these 

organizations for putting up with us and our griping. Everything we 

have accomplished was enabled by our systems administrators, Michael 

Galloway, Steve Moulton (2013 – 2015), Nathan Grodowitz and David 

Hester, and also our administrative assistants: Julia Cooper (2006 – 

2013), Anita Alton (2014 – 2015) and Lora Davis (2016 to present).
Table of Contents

Principal Investigators

Research Highlights

Bioenergy – The Recalcitrance of Plants

Supercomputing – Towards the Exascale

Neutron Scattering – 

Structure and Dynamics of Biomolecular Systems

Subsurface Biogeochemistry – 

Transport and Transformation of Mercury

Drug Discovery – Get Well Soon

Enzyme Catalysis – Faster Please

Multiscale Methods – Scaling from Atoms Upwards

How does a Physicist Survive in a Biology Department?

ORNL over the Decades

Welcome to the Real World – Undergrads in CMB

Five Germans in Tennessee

CMB Publications

2016 Staff

Alumni

Past and Current Grants

Supercomputer Allocations

Awards

2

4

6

14

19

28

36

46

48

62

64

66

70

76

90

92

96

102

106 1
Principal Investigators

OF THE CENTER FOR MOLECULAR BIOPHYSICS

Jeremy C. Smith

UT/ORNL Governor’s Chair and Director of the Center for Molecular 

Biophysics

Jeremy received a Ph.D. from the University of London in 1985. He was a postdoctoral fellow at Harvard University, 1985-1989. He previously lead research 

groups in biomolecular simulation at the Centre D’Etudes Nucleaires at Saclay, 

France, 1989-1998 and as Chair of Computational Molecular Biophysics at the 

University of Heidelberg, Germany, 1998-2006. He sticks his nose into a lot of 

research performed at CMB including the high-performance computer simulation of biological macromolecules, neutron scattering in biology, the physics of proteins, drug design, bioenergy, subsurface biogeochemistry and the 

analysis of structural change in proteins. As of 2016 Smith had published close to 400 peer-reviewed 

scientific articles.

smithjc@ornl.gov

Tongye Shen

Associate Professor, Department of Biochemistry and Cellular and 

Molecular Biology, University of Tennessee, Knoxville

Tongye received a Ph.D. in physics from the University of California-San Diego 

in 2002. He was a postdoctoral researcher at the Center for Theoretical Biological Physics at UCS D, 2003-2007 and a postdoctoral associate at the Center for 

Nonlinear Dynamics/Theoretical Biology and Biophysics Group, Los Alamos 

National Laboratory, 2007-2009. Tongye has constructed physical models and 

performed theoretical calculations and simulations on various biomolecular 

systems, ranging from the internal conformational dynamics of proteins and 

polysaccharides and protein-ligand association, to larger cellular structures. 

tshen@utk.edu

Jerome Baudry

Associate Professor, Department of Biochemistry and Cellular and 

Molecular Biology, University of Tennessee, Knoxville

Jerome received a Ph.D. in molecular biophysics from the University of ParisVI. After his postdoctoral work in the group of Klaus Schulten at the University 

of Illinois, Dr. Baudry worked in the pharmaceutical industry and as Research 

Faculty in the School of Chemical Sciences at the University of Illinois, UrbanaChampaign. Jerome joined the Center for Molecular Biophysics as tenure-track 

faculty in 2008 and he was tenured in 2014. His group conducts research on 

the biophysics of protein/ligand and protein/protein interactions and develops supercomputing tools to accelerate drug discovery, using these tools in 

specific health and environmental discovery projects. Jerome is also active in obtaining fundamental 

understanding of intermolecular interactions.

Xiaolin Cheng

Staff Scientist and Joint Assistant Professor, ORNL Computer Science 

and Mathematics Division

Xiaolin is a Staff Scientist in the Computer Science and Mathematics Division 

at Oak Ridge National Laboratory. He is also a joint Assistant Professor in the 

Department of Biochemistry & Cellular and Molecular Biology at the University 

of Tennessee, Knoxville. He received his Ph.D. from the State University of New 

York at Stony Brook, and his postdoctoral training at University of California, 

San Diego. Moving to ORNL in early 2008 Xiaolin’s research has been focused 

on developing more scalable and multi-scale algorithms for molecular simulation on emerging computer architectures and the application of molecular 

simulations to understanding biomass recalcitrance, membranes, gating mechanisms in ion channels 

and drug resistance of HIV integrase.

Hong Guo

Professor, Department of Biochemistry and Cellular and Molecular 

Biology, University of Tennessee, Knoxville

Hong obtained his Ph.D. from Harvard University in 1991. He was an International NSERC Fellow at the University of Waterloo, Canada in 1991-1993, a 

Research Associate at CERCA /University of Montreal, 1994-1997 and returned 

to Harvard as a scientist 1998-2001. He has lead a research group at Department 

of Biochemistry & Cellular and Molecular Biology, University of Tennessee 

since 2002. He has performed and directed research in computational studies 

of proteins, the catalytic mechanisms of enzymes, the role of hydrogen bonding and other interactions on protein structure and stability, and structural and vibrational properties 

of small molecules.

Loukas Petridis

Staff Scientist, ORNL Biosciences Division

Loukas received a Ph.D. in physics from the University of Cambridge in 2006. 

He was a postdoctoral fellow at ORNL, 2007-2009. His research focus is computer simulation of biological macromolecules, neutron scattering and polymer theory with emphasis in bioenergy and his current projects include the 

dynamic visualization of lignocellulose, a simulation model of lignocellulosic 

biomass deconstruction, and incorporating molecular-scale mechanisms stapetridisl@ornl.gov bilizing soil organic carbon into terrestrial carbon cycle models.

Jerry M. Parks

Staff Scientist, ORNL and Adjunct Assistant Professor, University of 

Tennessee, Knoxville

Jerry received a Ph.D. in Chemistry in 2008 from Duke University. Previously 

a postdoctoral researcher at ORNL from 2008 to 2009, his research interests 

include using computer simulation to study the structure and dynamics of biomolecules, bioinorganic chemistry of mercury, and enzyme mechanisms.

parksjm@ornl.gov

jbaudry@utk.edu

chengx@ornl.gov

hguo1@utk.edu

2 3
Research Highlights

Folding, Dynamics & Function

Neutron Scattering

Supercomputing

Subsurface Biogeochemistry

Bioenergy

Physical properties and chemical reactions of lignin

Hydrogen-bonding in cellulose deconstruction

Catalytic mechanism of cellulose degradation by a cellulase

Acetate- and ethanol-tolerant biomass-degrading microbe strains

Lignin/cellulose interactions

Mechanisms of pretreatment

Heat capacity maximum in hydrophobic hydration explained

Loop-closure kinetics and structured folding pathways

Accurate peptide partitioning and folding into lipid bilayers

Sugar recognition by ricin-like domains

Single proteins: nonequilibrium fractal time dynamics

Ion channels and the nicotinic acetylcholine receptor

Multiscale

Coarse-graining biomolecular dynamics

Transition networks, metastable states and dynamical

fingerprints of proteins

Treecode fast electrostatics

Multiscale in adaptive biosystems imaging

Bio-membranes

Lateral organization and diffusion dynamics of lipids

Cross-layer coupling in biomembranes

Adaptive Biosystems Imaging

Cell and cell-compartment simulations

Drug Discovery

Rapid docking of ligands on supercomputers and cloud architectures

• 100% success in hit identification in drug discovery for protein targets

Molecular origin of Gerstmann-Sträussler-Scheinker syndrome

Subdiffusion and fractal configuration space

Three classes of motion in the neutron-scattering spectrum of a globular protein

De Gennes narrowing and protein dynamics

Scaling of biological simulations on a petascale supercomputer

Multimillion-atom simulations of biomass

Rapid ligand docking

Catalytic mechanism of an organomercurial lyase

Dynamic mechanisms of bacterial mercury-resistance proteins

Identification of mercury methylation genes and proteins

Why mercury binds thiol groups

4
Bioenergy is of critical national 

importance as we strive to develop 

viable alternatives to fossil fuels. Our 

efforts in computer simulation and 

neutron scattering are aimed at 

understanding “biomass recalcitrance”.

Cellulose as a fuel source

“Biofuels serve as a substitute for a very 

important part of our current technology: combustion driven engines”, says 

Benjamin Lindner, who obtained his 

PhD at CMB and performed simulations of biomass with the ORNL Jaguar supercomputer. “This work is also 

relevant when you consider national 

security, because it ensures that fuel 

will always be available albeit at a limited rate. Energy efficiency is another 

important aspect and a necessity for a 

sustainable economy. Cellulose-based 

biofuels have a significant advantage 

over first-generation biofuels, because 

they are more scalable, don’t compete 

with the food market, and allow the use 

of specially designed energy plants. 

However, it is unlikely that all our energy demands can be met by using biofuels. I see cellulose-based biofuels as an 

important ingredient in a sustainable 

and ecologically friendly energy mix.”

Bioenergy barrier

“Lignin is the major “undesirable” component of biomass in the conversion 

process”, adds Amandeep Sangha, a 

postdoctoral fellow. “The presence of 

lignin, along with other factors, makes 

the breakdown of polysaccharides into 

sugars difficult. Understanding the origin of biomass recalcitrance to hydrolysis is one of the major challenges in 

improving the efficiency of the conversion process.” 

Bioenergy

THE RECALCITRANCE OF PLANTS

CMB model of Lignocellulose. Cellulose 

(green) from plant cell walls binds strongly 

to other cell wall molecules such as lignin 

(in brown). Removal of lignin is essential 

for efficient biomass deconstruction.

Road to energy independence: 

Harnessing the sun to power 

greener vehicles and herald more 

efficient energy production.

According to Barmak Mostofian, who 

also graduated with a PhD with us, one 

of the main issues, besides the frequently mentioned competition with 

food crops for available land and other 

logistics, is the development and proper 

implementation of technologies that 

produce affordable fuel in a more effective way. “While the enzymatic approach exploits the capabilities of natural catalysts to liberate sugars, which 

are then subsequently transformed into 

ethanol in a fermentation/distillation 

process, purely chemical routes do not 

rely on expensive enzymes or on the 

use of genetically altered microbes for 

enhanced alcohol production. Instead 

high-energy organic compounds can 

be synthesized directly from lignocellulose using solid catalysts, for instance. It is conceivable that the different approaches to tackle the natural 

resistance of biomass deconstruction 

will result in a multi-faceted bioenergy 

industry”.

Micholas Dean Smith, a post-doc researcher at the Center of Molecular 

Biophysics further explained, “As we 

move away from a petroleum based 

source of carbon for our chemical/fuel 

industries, it is imperative that we find 

an abundant feedstock for our ever 

growing industrial demands. As lignocellulosic biomass is perhaps the most 

abundant source of carbon (as well as 

environmentally neutral) it is a logical 

replacement for petroleum; however, 

the use of this raw material is limited by 

our limited understanding of its resistance to chemical breakdown. As such, 

it is necessary for us to apply a variety 

of techniques to elucidate the physical 

and chemical properties of lignocellulose. Armed with an enhanced understanding of lignocellulose, we can then 

begin to design chemical processes to 

take full advantage of its abundance.”

ORNL Biofuels Science 

Focus Area and the Bioenergy Science Center

CMB participates in the Bioenergy Science Center (BESC), which integrates 

experts from a wide range of scientific 

disciplines to understand biomass recalcitrance. According to Loukas Petridis, “Most of the chemical data used to 

construct our lignocellulose (biomass) 

models are derived from experiments 

performed at BESC. Also, many fruitful ideas have arisen from interactions 

with experimentalists at BESC. For example, it was during a BESC retreat that 

I first saw beautiful images of lignin aggregates forming after dilute acid pretreatment of biomass. The subsequent 

study of lignin aggregation by computer simulation has been one of the 

main focus areas of bioenergy research 

performed at CMB” (see press release).

6 7
BESC and the ORNL Biofuels Science 

Focus Area aim to provide breakthroughs that will allow viable cellulosic 

biofuel production. Significant steps in 

this direction have been achieved. CMB 

has participated in high-profile studies, commented on by former Secretary 

of Energy Steven Chu, identifying and 

characterizing a single microbial gene 

linked to increased ethanol tolerance. 

Former postdoctoral fellow Amandeep 

Sangha and Jerry Parks carried out 

quantum chemical calculations aimed 

at understanding lignin polymerization, 

and CMB has also performed calculations to understand cellulase catalysis 

and cellulosome function. According 

to Loukas Petridis, “We have leveraged 

unique capabilities in neutron scattering and HPC simulation to address 

key fundamental issues in the thermochemical pretreatment of plant biomass 

for biofuels production. The anticipated 

findings will provide fundamental scientific insight critical in underpinning 

the rational design of next-generation 

plant biomass and the formulation 

of pretreatment protocols tailored to 

achieve desired outcomes.”

Plant cellulose synthesis

Cellulose is the major structural component of plant cell walls and because of 

its abundancy it has great potential as a 

renewable source of energy. The plant 

cellulose synthesis complex (CSC), also 

called a ‘rosette’ because of its hexameric appearance in transmission electron 

microscope (TEM) images, is a large 

multi-subunit transmembrane protein 

complex responsible for synthesis of 

cellulose chains and their assembly into 

microfibrils. Despite the importance 

of cellulose, fundamental properties of 

the CSC remain unclear. The number 

of cellulose synthase (CESA) proteins 

in the CSC and the number of cellulose chains in a microfibril have been 

debated for years. We have recently 

worked with scientists from the ORNL 

Center for Structural Molecular Biology to derive a solution structure of the 

catalytic domain of CESA1 from Arabidopsis thaliana, determined by smallangle neutron scattering, that provides 

the first experimental evidence for the 

self-assembly of CESA into a stable 

trimer. This study strongly supports the 

‘hexamer of trimers’ model for the rosette CSC that synthesizes an 18-chain 

cellulose microfibril as its fundamental 

product.

Structure of CESA trimers calculated from smallangle scattering data represented by semitransparent grey surface envelopes, superposed 

with the computational atomic models in orange. 

The trimer models are arranged in a hexameric 

configuration consistent with the rosette shape 

observed in TEM images. The presented view is 

from the cytosolic side of the membrane. Cellulose 

microfibrils are visible in the apoplastic space.

8
OAK RIDGE, Tenn., June 15, 2011 — A 

first of its kind combination of experiment and simulation at the Department of Energy’s Oak Ridge National 

Laboratory is providing a close-up look 

at the molecule that complicates nextgeneration biofuels.

Lignin, a major component of plant 

cell walls, aggregates to form clumps, 

which cause problems during the production of cellulosic ethanol. The exact 

shape and structure of the aggregates, 

however, have remained largely unknown.

ORNL neutrons, simulations reveal 

details of bioenergy barrier

Source: www.ornl.gov/news/ornl-neutrons-simulations-reveal-details-bioenergy-barrier

New molecular models of lignin aggregates are helping scientists understand a 

limiting factor in the production of ethanol. (Image courtesy of www.scistyle.com)

A team led by ORNL’s Jeremy Smith 

revealed the surface structure of lignin 

aggregates down to 1 angstrom—the 

equivalent of a 10 billionth of a meter 

or smaller than the width of a carbon 

atom. The team’s findings were published in Physical Review E.

“We’ve combined neutron scattering 

experiments with large-scale simulations on ORNL’s main supercomputer 

to reveal that pretreated softwood 

lignin aggregates are characterized 

by a highly folded surface,” said Smith, 

who directs ORNL’s Center for Molecular Biophysics and holds a Governor’s 

Chair at University of Tennessee.

Lignin clumps can inhibit the conversion of biofuel feedstocks—for example, 

switchgrass—into ethanol, a renewable 

substitute for gasoline. When enzymes 

are used to release plant sugars necessary for ethanol production, the lignin 

aggregates bind to the enzymes and 

reduce the efficiency of the conversion.

Lignin’s highly folded surface creates more opportunities to capture the 

passing enzymes than a smooth surface would. An improved understanding of the lignin aggregates will aid 

scientists in efforts to design a more 

effective pretreatment process, which 

in turn could lower the cost of biofuels.

“Nature has evolved a very sophisticated mechanism to protect plants 

against enzymatic attack,” said ORNL 

team member Loukas Petridis. “We’re 

trying to understand the physical basis 

of biomass recalcitrance—resistance of 

the plants to enzymatic degradation.”

The complementary techniques of 

simulation on ORNL’s Jaguar supercomputer and neutron scattering at 

the lab’s High Flux Isotope Reactor enabled Smith’s team to resolve lignin’s 

structure at scales ranging from 1 to 

1,000 angstroms. Smith’s project is the 

first to combine the two methods in 

biofuel research. “This work illustrates 

how state-of-the-art neutron scattering and high-performance supercomputing can be integrated to reveal 

structures of importance to the energy 

biosciences,” Smith said.

The research was supported by DOE’s 

Office of Science and used the resources of the Leadership Computing 

Facility at ORNL under a DOE INCITE 

award. Team members include ORNL’s 

Sai Venkatesh Pingali, Volker Urban, 

William Heller, Hugh O’Neill and Marcus Foston and Arthur Ragauskas from 

Georgia Institute of Technology.

ORNL is managed by UT-Battelle for 

the Department of Energy’s Office of 

Science.

10 11
12 13

Ethanol tolerance in Clostridium thermocellum was 

traced to two mutations in a single gene encoding an 

alcohol dehydrogenase. A model of the enzyme with the 

mutation sites highlighted is shown here.
Supercomputing is a key element 

of the ORNL mission, alluded to by 

President Obama in his 2011 State 

of the Union speech and since then. 

Also, UT joined the elite ranks of NSF 

supercomputing institutions with its 

Kraken machine. As molecular simulation is a CPU-hungry enterprise, 

CMB is keenly involved with the development and application of highly 

parallel codes and we are major users 

of the local supercomputers, having 

received several awards for supercomputing time, including from the 

prestigious DOE INCITE program. 

Here we ask John Eblen, a postdoctoral fellow, and Roland Schulz 

and Sally Ellingson, both of whom 

graduated with PhDs here, about 

their experiences with petaflop supercomputers and prospects as we 

move towards the exascale. Roland 

and John have also worked on porting the molecular dynamics engine 

“GROMACS” to INTEL machines.

What is the most powerful 

computation you have 

ever performed?

Ellingson: I recently ran a high 

throughput docking screen on the 

Jaguar machine that included over 

one million chemical compounds. We 

used an MPI (message passing interface) version of Autodock4 (virtual 

docking software) that distributes the 

docking tasks. Also, using VinaMPI, a 

high-throughput virtual screening 

program developed to utilize a large 

number of cores on Supercomputers, 

I ran a virtual screen that performed 

over 15 million docking calculations. 

We were investigating the use of 

protein conformations obtained from 

simulations to improve the enrichment (increase the number of high 

scoring true positive compounds) 

for protein targets known to perform 

poorly in virtual screenings.

Schulz: As part of our INCITE allocation, I am simulating lignocellulosic 

biomass. A realistic model requires 

several million atoms. Our largest 

model constitutes 22 million atoms 

and runs on 45,000 cores. Additionally, 

I run larger tests to improve software 

performance for current and future 

projects, and the largest of these was 

run on 150,000 cores. As far as I know 

this is a world record for this type of 

calculation.

Supercomputing

TOWARDS THE EXASCALE

With a peak speed of over 20 petaflops (over 20,000 trillion calculations per second), Titan is a supercomputer composed 

of 299,008 AMD Opteron cores supported by additionally 18,688 Nvidia Tesla K20 GPU accelerators located at Oak 

Ridge National Laboratory, is one of the world’s fastest supercomputers for unclassified research. Capable of simulating 

physical systems with heretofore unfeasible speed and accuracy.

D.E. Shaw has made a special 

purpose supercomputer for 

molecular simulation. How does 

ORNL’s TITAN machine compare with it?

Schulz: The Shaw Anton special purpose machine is about 100 times faster 

for simulating the molecular dynamics of small biological systems, such as 

small proteins containing e.g. 20,000 

atoms. This is partly achieved by a network that is significantly faster. Titan 

allows us to run more flexible codes 

and is more suitable for our very large 

simulations.

Eblen: Shaw’s Anton supercomputer 

is quite impressive, being built from 

scratch for MD simulations. Both the 

instruction pipeline inside the custom 

processor and the communication 

patterns between all of the custom 

processors are designed to optimize 

particle force calculations. Thus Anton 

can run simulations about 100 times 

faster than a general-purpose supercomputer. To take advantage of this 

capability, of course, you have to use 

Anton! Simulation codes developed 

for TITAN, on the other hand, will be 

able to run on future HPC machines 

and take advantage of hardware and 

software advances being developed 

by researchers all over the world. This 

includes ideas and techniques learned 

from Anton.

Incidentally, I am not clear on the 

range of operations optimized by Anton. GROMACS, for example, applies 

14
multiple algorithms to improve the 

accuracy of simulations. I’m not sure 

how many of those algorithms are or 

could be optimized by Anton.

Are supercomputers easy to use 

for the average computational 

scientist?

Ellingson: When everything works 

right they are fairly easy to use. The 

hard part is figuring out what went 

wrong when it doesn’t work right.

What tools are you developing 

to help get programs to work on 

supercomputers?

Eblen: I’m focused on improving soft-

ware development on supercomput-

ers, a goal of the Eclipse Parallel Tools 

Platform (PTP) synchronized projects. 

Modern IDEs, such as Eclipse, offer 

many features to speed up software 

development. We want to make sure 

that these tools are available to those 

developing the most complex applica-

tions – those that run on supercom-

puters. Most people are not free to 

work directly on their favorite super-

computer with an IDE. They should be 

able to use the IDE on their personal 

computer, though, and have it work as 

if they were sitting at the supercom-

puter.

Roland, you are a GROMACS 

core developer. How does GRO-

MACS make sure that all the 

additions and improvements to 

the code are self-consistent and 

accurate?

Schulz: We use Gerrit for code review. 

Every change submitted to our server 

has to get two positive reviews from 

other developers. The code review 

helps us improve the software design 

and lets us find correctness issues be-

fore they ever make it into the devel-

opment version. Besides the manual 

code review, we have unit and regres-

sion tests. These automated tests get 

run for every change as soon as it is 

uploaded to the central server. Before 

we release a version for production us-

age, a beta version is made available to 

the community to help in finding any 

remaining bugs.

With funding from INTEL, you 

are now working on getting 

GROMACS working well on the 

Xeon Phi processors. What has 

this mighty struggle taught us?

Schulz: We learned how to make ef-

ficient use of large number of threads 

and wide vector instructions. Moore’s 

law, which predicts a doubling of the 

number of transistors every two years, 

is still going strong. But single CPU 

cores are not getting faster anymore, 

partly because of the energy cost of 

very high frequency cores. Thus the 

majority of performance we can ex-

pect in the next decade from the extra 

transistors will be in form of extra 

parallelism. The Xeon Phi with its 240 

threads and 16 wide vector register 

has this large parallelism which will 

be common in the future. Thus the 

performance optimizations we made 

for Xeon Phi will be required for most 

future hardware.

Eblen: In my opinion, the most valu-

able lesson is the importance of 

good code design. GROMACS has a 

nice abstraction layer for SIMD op-

erations, and as a result, adding Xeon 

Phi-specific SIMD instructions was 

something we were able to finish fairly 

early. Also, GROMACS neatly orga-

nizes the different high-level opera-

tions, allowing them to be profiled and 

optimized separately. This has been 

a great help in optimizing different 

sections of the code. GROMACS is not 

perfect, of course. We have struggled 

quite a bit with our offload implemen-

tation, where GROMACS begins run-

ning on a host computer and offloads 

data and computations to the Xeon 

Phi. GROMACS uses rather large and 

complex data structures, which are 

passed around all over the code, mak-

ing it hard to know what data is actu-

ally read or written by any specific 

section. Additionally, some data may 

only be used in certain modes of op-

eration, because GROMACS is highly 

configurable. This problem is again a 

design problem. Ideally, we would have 

narrowly-defined interfaces between 

different parts of the code, rather than 

passing around these monstrous data 

structures. Developing code this way, 

though, requires a great deal of disci-

pline, because data structures always 

start out small and grow incremen-

tally. I suspect that large, catch-all data 

structures are quite common in scien-

tific programs built for supercomput-

ers. Unfortunately, once you have such 

structures, they become like global 

data, with all of the well-known prob-

lems associated with global data.

What have you done so far with 

the UT/ORNL supercomputers 

and what is the future for su-

percomputers in biomolecular 

simulation?

Schulz: We have performed enormous 

simulations of biomass that wouldn’t 

have been possible any other way. 

Analyzing the results take quite some 

time... 

16 17
Ellingson: I have done many smaller 

drug-design screenings to prepare 

for the million compound screen we 

recently completed. I think that we 

may see more special purpose computers, such as Anton, for biomolecular simulations. However, since special 

purpose computers are built around 

the code that runs on them, they will 

not be able to handle everything, and 

multi-purpose supercomputers will 

still be very important. Many multipurpose supercomputers are going 

toward hybrid architectures which 

include GPUs for part of the processing power. It will be important to learn 

how to correctly utilize these architectures to improve the speed of simulation code. 

How can we advance the state 

of software development, especially for supercomputing?

Eblen: Historically, leaps in what humans can do with computers come 

from newer and better tools. The 

purpose of a tool is to take care of the 

mundane activities, so that we can 

focus on the bigger issues. What has 

changed over time is what is considered “mundane.” Assemblers automate 

the mundane task of having to translate operations to bits and bytes. Compilers automate the mundane task of 

translating common operations to a 

series of assembly commands, such as 

adding multi-byte numbers or creating loops. IDEs automate still more 

complex operations. Each new class 

of tools frees us to tackle bigger, more 

ambitious projects. Unfortunately, 

this process has some inertia. People 

become familiar with their tools and 

aren’t eager to learn new ways of doing things. For example, some of our 

programming languages are long 

overdue for a remake. Now I love C++. 

It is my favorite of the commonly 

used languages. What other language 

allows both raw, system-level access 

and tons of useful, high-level features, 

such as classes and well-developed 

standard libraries? C++ is a good example of a tool that pushed the industry ahead. It suffers from its C heritage, 

though, which makes the language 

overly complex and error prone. Now 

there is a wonderful, relatively new 

language, called the D programming 

language, which has been carefully 

designed and well-engineered over 

about a decade now. It is a systems 

language that is just as powerful and 

feature-rich as C++ while fixing many 

of its problems. But I don’t know anyone trying to use it for supercomputing. So that is one example of a possible initiative that could advance our 

tools and increase even more of what 

we humans can do with our computers.

Mere mortals find computers 

extremely annoying when they 

don’t do what they want. Is it 

the same with you hot-shots?

Ellingson: Getting computers to do 

what you want is the fun part. If they 

always did exactly what you wanted 

the first time you tried, it wouldn’t 

be as rewarding when you finally get 

your programs working right.

Eblen: Of course! Often, I know why 

it’s not working like I want. So as a 

developer myself, depending on my 

mood and whatever the problem is, I 

may feel sympathetic to the poor programmer or feel... displeased because 

he or she should have known better!

18

Neutrons are unique probes of condensed materials, furnishing both structural and dynamic information, and 

neutron scattering has been a sustained 

interest of Jeremy Smith’s since he published his first papers on the subject as 

a Ph.D. student at the Institut Laue-Langevin in Grenoble, France in 1986. The 

advent of the Spallation Neutron Source 

at ORNL promises to take neutron scattering research to new heights, and we 

have therefore established a program 

aiming at developing methodologies 

for neutron research, integrating highperformance simulation with neutron 

scattering, and applying a range of neutron techniques to systems of interest in 

biology and the energy biosciences. 

The methodological work has produced 

a number of breakthroughs. Among 

these is the first calculation of the lattice 

dynamics of a protein crystal at atomic 

resolution, which we hope can at some 

point be tested experimentally using 

triple-axis instrumentation. Work performed principally by former postdoctoral fellow Liang Hong, in collaboration 

with Alexei Sokolov, another Governor’s 

Chair, demonstrated how the dynamic 

neutron susceptibility of a protein can 

be simply interpreted in terms of three 

classes of motion (see press release).

Complementary theoretical work by former graduate student graduate student 

Thomas Neusius demonstrated that the 

subdiffusive behavior of peptide dynamics has a fractal origin. Later work 

provided a clear demonstration of the 

propagation of solvent frictional effects 

into a protein core, and showed how 

protein inter-domain motion can be 

described in terms of “De Gennes Narrowing”.

Also, former postdoctoral fellows Yi 

Zheng and Yinglong Miao worked with 

Jerome Baudry and Nitin Jain, an Associate Professor in the UT Department 

of Biochemistry and Cellular and Molecular Biology, to perform and interpret neutron scattering experiments on 

cytochrome P450 – this work has led to 

a new method for analyzing elastic scattering that yields not only the average 

displacement of hydrogen atoms in a 

protein but also the variance.

Two growth areas for the future have 

been identified. One of these is the 

application of neutron spin-echo spectroscopy to characterize functional domain motions of biomolecules, an area 

that former postdoctoral fellow Nikolai 

Smolin concentrated on, and the second is the application of neutrons to the 

energy biosciences, and, in particular, 

the structure and dynamics of lignocellulosic biomass. In the latter respect 

our collaboration with experimental 

neutron scattering researchers here at 

ORNL provided a physical mechanism 

behind steam-explosion biomass pretreatment has had considerable impact. 

Finally, Jeremy would like to realize a 

vision of unifying exascale supercomputing with high-performance neutron 

scattering, in which molecular simulations, performed using the full power 

of the exascale supercomputer, are 

used to plan and interpret experiments 

at SNS in real time. We are quite a way 

from achieving that goal, but a first step 

was taken by the former graduate student and postdoctoral fellow Benjamin 

Lindner, who has efficiently parallelized software for scattering calculations. 

Given the resources and further developments in both computational and experimental techniques, this unification 

can be realized in the foreseeable 

future.

Neutron Scattering

STRUCTURE AND DYNAMICS OF BIOMOLECULAR SYSTEMS

19
OAK RIDGE, Tenn., Sep. 30, 2011 — Molecular motion in proteins comes in 

three distinct classes, according to 

a collaboration by researchers at the 

Department of Energy’s Oak Ridge 

National Laboratory and the University 

of Tennessee, in research reported in 

Physical Review Letters.

The research team, directed by ORNLUT Governor’s Chairs Jeremy Smith 

and Alexei Sokolov, combined highperformance computer simulation 

with neutron scattering experiments to 

understand atomic-level motions that 

underpin the operations of proteins.

High-performance simulation, neutrons 

uncover three classes of protein motion

Source: www.ornl.gov/news/high-performance-simulation-neutrons-uncover-three-classes-protein-motion

Lysozyme (shown in blue) -- a natural enzyme found in 

tears, saliva and egg whites -- can break down bacterial cell 

walls (shown in pink). ORNL researchers have combined 

computational simulation and neutron experiments to 

clarify the complicated motions of proteins such as lysozyme into three distinct classes.

“The analysis and interpretation of 

neutron scattering spectra are always 

difficult for complex molecules such 

as proteins,” said Smith, who directs 

ORNL’s Center for Molecular Biophysics. “We’ve performed experiments and 

then shown that simulation can provide a clear view of them. It allows us 

to see through the complexity and find 

out what motions are going on.”

Defining the motions present -- localized diffusion, methyl group rotations 

and jumps -- is important as it allows 

scientists to think about how the motions determine the functions of proteins that are critical to all life.

“First, we found that experiment and 

simulation agreed perfectly with each 

other, which is remarkable,” Smith said. 

“Second, the simulations told us that 

this type of neutron scattering can be 

interpreted in a very simple way.”

Although the team performed its research on a particular protein called 

lysozyme, a natural antibacterial enzyme found in tears, saliva and egg 

whites, the researchers anticipate the 

technique will have a much broader 

impact in the neutron scattering community, aiding research in areas such 

as biofuel design or environmental 

remediation.

The combined simulation and neutron 

scattering approach should also be 

of use in the characterization of nonbiological materials such as polymers. 

Smith notes that approximately half 

the neutron scattering experiments at 

ORNL’s Spallation Neutron Source involve the study of motions in materials.

“These methods are of general applicability,” Smith said. “Many experimentalists can now come to the ORNL’s 

Spallation Neutron Source, measure 

a spectrum of whatever sample they 

have, and then apply this analysis in 

terms of three classes of motion to interpret their results.”

The research was primarily conducted 

by ORNL’s Liang Hong, with the support of Benjamin Lindner and Nikolai 

Smolin from ORNL. They performed 

neutron scattering experiments at 

ORNL’s Spallation Neutron Source on 

the BASIS instrument and at the National Institute of Standards and Technology Center for Neutron Research. 

The work was published as “Three 

classes of motion in the dynamic 

neutron scattering susceptibility of a 

globular protein.”

The simulation component of the work 

was supported by ORNL’s Laboratory 

Directed Research and Development 

program, while the neutron scattering component was supported by an 

Experimental Program to Stimulate 

Competitive Research (EPSCOR) grant 

to the University of Tennessee from 

the DOE Office of Science.

ORNL is managed by UT-Battelle for 

the Department of Energy’s Office of 

Science.

20 21
OAK RIDGE, Tenn., Oct 7, 2015 — Plants 

and other biomass can be converted 

into a variety of renewable high-value 

products including carbon fibers, plastics, and liquid fuels such as ethanol 

and biodiesel that are beneficial for 

reducing petroleum use and vehicle 

emissions. Breaking down plants in 

order to release energy can require 

many steps and harsh chemicals, so 

researchers are seeking efficient natural catalysts, specifically enzymes, to 

deconstruct plant material.

Scientists at the U.S. Department of Energy’s Oak Ridge National Laboratory 

are using neutron crystallography to 

understand enzymes and learn how to 

bioengineer those enzymes for largescale improvements in the efficiency of 

biomass processing. Using the MaNDi 

instrument at ORNL’s Spallation Neutron Source (a DOE Office of Science 

User Facility), the LANSCE Protein 

Crystallography Station in Los Alamos, 

N.M., and the FRMII BioDiff instrument 

in Munich, Germany, they determined 

the structure of xylanase, an enzyme 

used to digest hemicellulose during 

biofuel production, at unprecedented 

detail.

When processing plant-based biomass, 

hemicellulose—an abundant polysaccharide in plant cell walls—must first 

be degraded to monomeric sugars that 

can be converted to high-value products such as biofuels. Current nonharsh methods to pretreat biomass 

result in very basic (high pH) conditions. Native enzymes are not very 

efficient in such conditions, however, 

preferring an acidic (low pH) environment for maximum activity. By re-engineering hemicellulose-hydrolyzing 

enzymes to increase their activity at 

high pH, researchers can improve the 

process, but that requires researchers 

to understand the intricate details of 

how the enzymes work.

“We need to look deeper into their 

structures than what X-rays usually can provide,” said ORNL’s Andrey 

Kovalevsky, the senior author of the 

study. “That is, we have to know where 

all of the hydrogen atoms are before, 

during and after a chemical reaction 

has occurred in an enzyme’s active site. 

Neutrons can give us this information.”

In fact, using neutrons, the team directly and unequivocally visualized 

hydrogen atoms and hydrogen bonding in xylanase at different stages of 

the catalytic reaction.

“No one has ever observed hydrogen 

atoms in a glycoside hydrolase enzyme, 

and until now we did not know how 

the catalytic glutamic acid residue is 

protonated,” said Kovalevsky.

Kovalevsky and his colleagues are 

interested in protonation because 

they need to know how protons move 

Neutrons help understand enzymes 

that could produce improvements in 

biomass processing

Source: www.ornl.gov/news/neutrons-help-understand-enzymes-could-produce-improvements-biomass-processing

during catalysis. For example, to start 

the hemicellulose hydrolysis reaction, the catalytic glutamic acid must 

be protonated and the catalytic base 

must be deprotonated. Understanding 

the acid/base chemistry of enzymatic 

biomass hydrolysis is key to rationally 

engineering enzymes that improve 

biomass processing.

Kovalevsky and his colleagues determined five neutron structures of 

xylanase at various pH values and in 

complex with a ligand. The structures 

showed how hydrogen atoms are arranged in the active site of xylanase, 

where they move and how hydrogen 

bonding is altered due to pH changes 

and ligand binding. The low-pH structure, obtained from data collected on 

MaNDi, helped them understand how 

the enzyme functions.

“This enzyme, used in biofuels production, is a target for enzyme design to 

improve its performance in an industrial setting,” said Kovalevsky. “Exact 

knowledge of its mechanism will improve protein engineering efforts.”

The team has discovered that the catalytic glutamic acid can orient itself in 

two different conformations that have 

very different affinities for a proton. 

When the glutamate side chain rotates 

down and away from a substrate, it is 

a weaker acid than when it adopts an 

upward orientation. As a result, the 

catalytic group obtains a proton from 

water only when it faces downward, 

but can be an efficient proton donor to 

the substrate to initiate the hydrolysis 

reaction when it is in the upward conformation.

“This is a big revelation for glycoside 

hydrolases, and specifically for xylanases, because we now 

know where to make amino acid 

substitutions in order to improve the 

enzyme,” said Kovalevsky.

The combination of neutron diffraction experiments with highperformance computing is a powerful approach for understanding how 

enzymes function. The researchers 

were curious to know how easily the 

glutamate side chain switches conformations. To answer that question, they 

turned to computer simulations.

“Using neutron structures as a starting 

point for molecular dynamics simulations, we showed that the glutamate 

can readily cycle between the two conformations,” said Jerry Parks, an ORNL 

researcher and co-author of the study. 

“With another computational approach, 

we also found that the acidity of the 

glutamate changes significantly based 

on how it is oriented, which agrees 

nicely with the neutron structures.”

This research was published in the 

Proceedings of the National Academy of Sciences (www.pnas.org/content/112/40/12384.abstract), and is the 

first user publication of the MaNDi 

instrument, which was commissioned 

at SNS in 2014. The research was supported by DOE’s Office of Science.

ORNL is managed by UT-Battelle for 

the Department of Energy’s Office of 

Science.

DOE’s Office of Science is the single 

largest supporter of basic research in 

the physical sciences in the United 

States, and is working to address some 

of the most pressing challenges of our 

time. For more information, please 

visit science.energy.gov.

22 23
24 25

Proteins are the molecular level machines carry out virtually all vital biological functions in the cells. They are 

dynamical entities that are jiggling and 

wiggling at all time. In fact, this unique 

flexibility within a yet well-defined, 

folded structure is what enables proteins to perform all sorts of important 

functions, such as catalyzing biochemical reactions, opening and closing ion 

channels, etc.

For a long time, a common assumption 

has been that the structural fluctuations of the globular proteins (i.e. the 

jiggling and wiggling) driven by the 

thermal energy are Brownian motions, which are completely random 

and uncorrelated. More importantly, 

these motions were assumed to be in 

thermodynamic equilibrium. In other 

words, over a sufficiently long observation time, the structural fluctuation of 

individual proteins behave on average 

the same as the average behavior of a 

large ensemble of proteins. Therefore, 

one could draw conclusions about the 

behavior of single proteins by measuring the behavior of a sample containing a large number of proteins, as in 

typical experiments in laboratories.

However, the results from a series of 

massive computer simulations carried out by graduate student Xiaohu 

Hu at CMB on various supercomputers, 

including DOE’s TITAN and HOPPER, 

as well as the ANTON supercomputer 

provided by D. E. Shaw research at the 

Pittsburgh Supercomputing Center, 

revealed that the thermal structural dynamics of proteins are not equilibrium 

Brownian motions, but rather out of 

equilibrium and non-ergodic, contrary 

to the traditional assumptions. This 

means that there is not a characteristic 

“average time” of the motion, but rather, 

the observed timescale of the motion 

increases with the length of the observation itself. In other words, the longer 

one watches, the slower the motion 

becomes, and this may go on beyond 

the typical lifespan of proteins. This 

is often referred to as an “aging effect”. 

Furthermore, the motion appears selfsimilar in time, meaning if a stochastic 

trajectory from the protein motion is 

displayed without time units, one will 

not be able to distinguish, for example, 

whether the trajectory has the length 

of 1 nano-second or maybe 1 microsecond.

One important consequence of such 

non-equilibrium behavior is that two 

proteins with identical primary amino 

acid sequences and same folded structure will not exhibit the same dynamical behavior, and, in fact, they can 

deviate substantially from each other. 

The average behavior of a group of a 

protein no longer reflects the behavior 

of each individual proteins. This potential for different behavior within the 

group raises many interesting questions that call for more future studies: 

such as, what is the biological implication of this non-ergodic behavior? Or 

how does the cell cope with a group of 

largely differently efficient enzymes 

carrying out a vital biological function? 

The answers to these questions will 

further advance our understanding of 

the complexity of biological systems 

from a single cell to entire organisms.

OAK RIDGE, Tenn., Nov. 23, 2015—Supercomputing simulations at the 

Department of Energy’s Oak Ridge 

National Laboratory could change how 

researchers understand the internal 

motions of proteins that play functional, structural and regulatory roles in all 

living organisms. The team’s results 

are featured in Nature Physics.

“Proteins have never been seen this 

way before,” said coauthor Jeremy 

Smith, director of ORNL’s Center for 

Molecular Biophysics and a Governor’s 

Chair at the University of Tennessee 

(UT). “We used considerable computer 

power to provide a unified conceptual 

picture of the motions in proteins over 

a huge range of timescales, from the 

very shortest lengths of time at which 

atoms move (picoseconds) right up 

to the lifetimes of proteins in cells 

(roughly 1000 seconds). It changes 

what we think a protein fundamentally 

is.”

Studying proteins—their structure and 

function—is essential to advancing 

understanding of biological systems 

relevant to different energy and medical sciences, from bioenergy research 

and subsurface biogeochemistry to 

drug design.

Results obtained by Smith’s UT graduate student, Xiaohu Hu, revealed that 

the dynamics of single protein molecules are “self-similar” and out of 

equilibrium over an enormous range 

of timescales.

With the help of Titan— the fastest 

supercomputer in the U.S., located 

at the DOE Office of Science’s Oak 

Ridge Leadership Computing Facility—

Smith’s team developed a complete 

picture of protein dynamics, revealing 

that the structural fluctuations within 

any two identical protein molecules, 

even if coded from the same gene, turn 

out to be different.

“A gene is a code for a protein, producing different copies of the protein that 

should be the same, but the internal 

fluctuations of these individual protein 

molecules may never reach equilibrium, or converge,” Smith said. “This 

is because the fluctuations themselves 

are continually aging and don’t have 

enough time to settle down before the 

protein molecules are eaten up in the 

cell and replaced.”

New supercomputer simulations

enhance understanding of protein

motion and function

Source: www.ornl.gov/news/new-supercomputer-simulations-enhance-understanding-protein-motion-and-function

Non-equilibrium, fractal and 

self-similar dynamics in proteins
26 27

Understanding the out-of-equilibrium 

phenomenon has biological implications because the function of a protein 

depends on its motions. Two individual protein molecules, even though 

they come from the same gene, will 

not function precisely the same way 

within the cell.

“You may have, for example, two identical enzyme molecules that catalyze the 

same reaction,” said Smith. “But due 

to the absence of equilibrium, the rate 

at which the catalysis happens will be 

slightly different for the two proteins. 

This affects the biological function of 

the protein.”

The team also discovered that the dynamics of single protein molecules are 

self-similar, or fractal over the whole 

range of timescales. In other words, 

the motions in a single protein molecule look the same however long you 

look at them for, from picoseconds to 

hundreds of seconds.

“The motions in a protein, how the bits 

of the protein wiggle and jiggle relative 

to each other, resemble one another on 

all these timescales,” Smith said. “We 

represent the shape of a protein as a 

point. If it changes its shape due to 

motions, it goes to a different point, 

and so on. We joined these points, 

drawing pictures, and we found that 

these pictures are the same when you 

look at them on whatever timescale, 

whether it’s nanoseconds, microseconds, or milliseconds.”

By building a more complete picture of 

protein dynamics, the team’s research 

reveals that motions of a single protein 

molecule on very fast timescales resemble those that govern the protein’s 

function.

To complete all of the simulations, the 

team combined the power of Titan 

with two other supercomputers—Anton, a specialty parallel computer built 

by D.E. Shaw Research, and Hopper, 

the National Energy Research Scientific Computing Center’s Cray XE6 supercomputer located at Lawrence Berkeley 

National Laboratory.

“Titan was especially useful for us to 

get accurate statistics,” Smith said. “It 

allowed us to do a lot of simulations in 

order to reduce the errors and get more 

confident results.”

The title of the Nature Physics paper 

is “The Dynamics of Single Protein 

Molecules is Non-Equilibrium and 

Self-Similar Over Thirteen Decades in 

Time.”

This research was supported by the 

DOE Office of Science through an Advanced Scientific Computing Research 

(ASCR) Leadership Computing Challenge (ALCC) allocation and funded in 

part by a DOE Experimental Program 

to Stimulate Competitive Research (EPSCoR) award. The Oak Ridge Leadership Computing Facility and National 

Energy Research Scientific Computing 

Center are DOE Office of Science User 

Facilities.

ORNL is managed by UT-Battelle for 

the Department of Energy’s Office of 

Science. DOE’s Office of Science is the 

single largest supporter of basic research in the physical sciences in the 

United States and is working to address 

some of the most pressing challenges 

of our time. For more information, 

please visit science.energy.gov.

—By Miki Nolin

Illustration of the structure of a phosphoglycerate kinase protein that was subjected to molecular dynamics 

simulations. The relative motions of the red and blue domains of the proteins are highly complex, and can be 

described in terms of motion of a configurational point on a rough energy landscape (illustrated). The transitions of the structure between energy minima on the landscape can be described in terms of a network (illustrated), which is found to be fractal (self-similar) on every timescale. Image credit: Thomas Splettstoesser; 

www.scistyle.com
Binding of Hg2+ to MerR induces conformational changes required to initiate transcription of Mer genes, which 

encode proteins and enzymes involved in mercury resistance. Two other major components of the Mer system 

are the organomercurial lyase, MerB, which converts methylmercury to Hg2+, and the mercuric reductase, MerA, 

which reduces Hg to Hg(0).

spheric currents and later returning to 

the surface in rainwater. Water again 

finds it back into the earth, but not 

before transporting mercury throughout earth’s vast, interconnected waterways. Over the course of its journey, 

mercury can undergo a countless 

series of oxidation, methylation, demethylation and reduction events. 

Mercury also enters the global cycle 

naturally by weathering of minerals 

into the groundwater or by volatilization during volcanic eruptions, which 

are collectively responsible for half 

of all atmospheric mercury. Humans 

are responsible for the other half. The 

onset of elevated mercury levels globally coincides with the dawn of the 

Industrial Revolution and has continued onward because of our use of coal 

for fuel. The little mercury that is not 

vaporized during combustion leaches 

into ground water from coal ash piles. 

Its “stickiness” toward other metals has 

led to its prevalent use in gold mining 

as a way to separate the wheat from the 

chaff in refining processes. Leaching 

from legacy chemical disposal sites is 

another significant source of mercury 

contamination. 

Mercury Speciation. Mercury 

strongly binds to ligands when traveling through natural and contaminated 

waterways. The chemical composition 

of the water system and the oxidation 

state of mercury determines which 

ligands it will bind. For example, mercury prefers to bind to decomposing 

plant matter in freshwater streams but 

to chloride in the ocean. Former postdoc Demian Riccardi developed an 

accurate quantum mechanical (QM) 

approach to calculate the strength 

of mercury-ligand binding. Current 

postdoc Ryne Johnston is actively 

extending that approach to account 

for pH-depended redox processes and 

to investigate how different binding 

partners compete for mercury under a broader set of environmentally 

relevant conditions. These calculations will help to understand and map 

aquatic mercury transport from local 

to global scales.

The fate of mercury as a subsurface 

contaminant is of particular interest 

at ORNL because of legacy contamination from cold war activities at the 

nearby Y-12 plant in Oak Ridge in the 

1950s and 1960s. It turns out that following mercury in the environment 

has some fascinating aspects. Mercury interacts with and is transformed 

by organic matter and particulates 

in the streams and sediments. Anaerobic bacteria are able to methylate inorganic mercury, rendering it 

more toxic, but aerobic bacteria can 

demethylate methylmercury. Biotic 

and abiotic transport and transformation of mercury in stream systems is 

a subject of intense interest for CMB 

in the framework of a DOE-funded 

Science Focus Area (SFA) project.

Among the methods applied are 

semiempirical quantum mechanical/molecular mechanical (QM/MM) 

calculations, which the former postdoctoral fellow Demian Riccardi was 

working at streamlining, MD simulations and subsequent structural analysis, which Jerry Parks, postdoctoral 

fellow Ryne Johnston and graduate 

student Jing Zhou have further developed.

Work done in the ORNL mercury SFA project led to a major discovery in 2013: solving the 

decades old question of how 

bacteria methylate mercury.

Why is mercury research 

important?

Mercury (Hg) — the liquid quicksilver 

at the bottom of old thermometers — 

is a curious heavy metal that readily 

binds to many other elements. The elemental form of mercury is not readily 

absorbed by humans, so its toxicity is 

relatively low. Unfortunately, mercury 

can exist in more toxic forms. Oxidized mercury, Hg2+, is toxic because it 

binds very tightly to sulfur-containing 

compounds, which are essential to life, 

thereby disrupting cellular processes. 

The methylated form, methylmercury, 

is a potent neurotoxin that readily 

crosses the blood-brain barrier, causing debilitating and often fatal neurological diseases.

All organisms consume trace amounts 

of Hg over the course of their lives with 

negligible ill effect. However, because 

mercury binds so strongly to biological compounds it is rarely excreted. In 

this way, prey exact revenge on their 

predators by passing on the mercury 

in their tissue. As a result, mercury 

climbs up the food web through the 

process of biomagnification. Because 

significant mercury concentrations 

can be found in apex predators such as 

swordfish and tuna, limited consumption of these species is recommended 

for humans. 

Why is mercury a global concern?

Mercury constantly cycles from the 

earth below, evaporating into the sky 

to be transported globally by atmoSubsurface Biogeochemistry

TRANSPORT AND TRANSFORMATION OF MERCURY

28 29
Whereas some bacteria detoxify 

mercury-containing compounds, 

others do the opposite. Certain 

anaerobic microorganisms convert 

inorganic mercury to methylmercury. 

The genetic and biochemical basis 

for the reaction, however, remained 

elusive for more than four decades. 

In the early to mid-1990s, mercury 

methylation was shown to be an 

enzyme-catalyzed process involving 

cofactor B12 and biochemical reaction 

pathways. However, these pathways 

are shared by numerous bacteria but 

only a small fraction of which were 

known to be Hg methylators, and 

31

Fig 1: Model of HgcA showing the predicted bond between 

cobalt (purple sphere) and sulfur (yellow).

Bacterial Mercury Resistance.

Some bacteria are adapted to survive 

in environments with high mercury 

concentrations. These organisms owe 

their mercury resistance to mer genes, 

which encode a set of “Mer” proteins 

and enzymes responsible for detoxifying the cell of mercury. We have combined neutron and X-ray scattering 

experiments with molecular dynamics 

(MD) simulations to study the structures and functions of some of the 

Mer proteins and enzymes. We applied 

these methods to understand how the 

MerR sentry protein flips the switch to 

produce the rest of the mer machinery when it encounters mercury. MerR 

looks like a sort of hinge that clasps 

onto and holds the mer operator DNA 

strand taut with the two DNA-binding 

domains at each end. Mercury binding induces a conformational change 

in MerR, which then induces the DNA 

strand to underwind and balloon out. 

This regulator protein initiates transcription of the mer proteins and enzymes only when they are needed. Understanding how one of these enzymes, 

MerB, breaks down methylmercury to 

its inorganic form was an early success story from the SFA project. Two 

cysteine amino acids coordinate methylmercury through their sulfur atoms, 

and these interactions lengthen and 

therefore weaken the mercury-carbon 

bond. A third amino acid, aspartic acid, 

delivers a proton to the carbon atom 

in methylmercury, which cleaves the 

bond We have recently embarked on a 

study to identify the factors that control passive diffusion of mercury compounds through the lipid bilayers of 

bacterial cell membranes.

The Great Methylation 

Discovery
33

Fig 2: Concept of the redox-mediated HgcA methylation mechanism. The bond between cobalt (purple) and sulfur 

(yellow) promotes methyl anion (H3C:–) transfer to inorganic mercury (grey), producing methyl mercury. Reduction of 

cobalt by HgcB starts a new cycle.

there was no obvious phylogenetic 

pattern to suggest which organisms 

could and could not perform the 

reaction. Therefore, it was unclear 

which specific protein or proteins 

were needed to methylate mercury.

Jerry tackled this problem by using an 

unconventional approach to identify 

the genes and corresponding proteins 

responsible for Hg methylation by 

considering the chemical aspects 

and structural elements in proteins 

that would be required for the 

methylation reaction. The project 

led to the discovery of two genes, 

hgcA and hgcB, that are required 

for methylmercury production 

by bacteria and archaea. The 

proteins encoded by these two 

genes are a cobalamin-dependent 

methyltransferase and an electrondonating ferredoxin. He predicted 

that a unique cobalt-sulfur bond in 

HgcA enables the transfer of a methyl 

anion (H3C:–) to mercury, rather than 

methyl radical (H3C•) or methyl cation 

(H3C+) transfer, which are ubiquitous 

across all life. Such a reaction is 

unprecedented in biology, as neither 

this unique bonding

pattern nor methyl anion transfer has 

ever been observed for any cobalamincontaining protein.

A major implication stemming from 

this research is the prediction that any 

microorganism encoding hgcA and 

hgcB genes in its genome sequence 

will be able to produce methylmercury. 

To date, all of the bacteria and 

archaea that have been tested have 

methylated mercury, confirming this 

prediction. With this major discovery 

in environmental science, we can 

now start to understand and detect 

bacterial methylmercury production 

worldwide, which hopefully will lead to 

strategies to limit potential harm that 

can be caused by the methylmercury 

produced by these microbes. 

Bioinorganic Chemistry of HgcA 

Methylation. Cobalamin (vitamin 

B12) and its chemical relatives are 

biological workhorses that can use 

their central, redox-active cobalt 

atom to transfer methyl groups. The 

electronic properties of the lower 

ligand affect the chemistry of the 

upper ligand; on this basis was Jerry’s 

prediction founded. Jing Zhou sought 

to quantify and compare the energies 

of methyl radical and methyl anion 

transfer to mercury with different 

lower ligands and model corrinoids. 

She found that anionic sulfur 

coordination promotes methyl anion 

transfer, whereas the typical neutral 

nitrogen coordination promotes 

methyl radical transfer. Subsequent 

mutagenesis experiments by 

collaborators bolster Jing’s simulations 

and Jerry’s prediction. Jing and 

Ryne are extending the simulations 

a step further to investigate exactly 

how different lower ligands affect 

the electrochemistry of cobalt. 

Understanding the electrochemistry 

in HgcA provides insights into its 

biophysical mechanisms and cellular 

functions. Their robust methodology 

to tackle large, inorganic complexes 

is widely extensible to other systems 

and can also help develop accurate 

geochemical speciation models. 
OAK RIDGE, Tenn., Feb. 7, 2013 — By 

identifying two genes required for 

transforming inorganic into organic 

mercury, which is far more toxic, scientists today have taken a significant 

step toward protecting human health.

The question of how methylmercury, 

an organic form of mercury, is produced by natural processes in the environment has stumped scientists for 

decades, but a team led by researchers 

at Oak Ridge National Laboratory has 

solved the puzzle. Results of the study, 

published in the journal Science, provide the genetic basis for this process, 

known as microbial mercury methylation, and have far-reaching implications.

“Until now, we did not know how the 

bacteria convert mercury from natural 

and industrial processes into methylmercury,” said ORNL’s Liyuan Liang, a co-author and leader of a large 

Department of Energy-funded mercury research program that includes 

researchers from the University of 

Missouri-Columbia and University of 

Tennessee.

“This newly gained knowledge will allow scientists to study proteins responsible for the conversion process and 

learn what controls the activity,” said 

Liang, adding that it may lead to ways 

of limiting methylmercury production 

in the environment.

For some 40 years scientists have 

known that when mercury is released 

into the environment certain bacteria can transform it into highly toxic 

methylmercury. Exactly how bacteria 

make this happen has eluded scientists. 

The challenge was to find proteins that 

can transfer a certain type of methyl 

group and to identify the genes responsible for their production.

Ultimately, by combining chemical 

principles and genome sequences, the 

team identified two genes, which they 

named hgcA and hgcB. Researchers 

experimentally deleted these genes 

one at a time from two strains of 

bacteria, which caused the resulting 

mutants to lose the ability to produce 

methylmercury. Reinserting these 

genes restored that capability, thus 

verifying the discovery.

The researchers found that this twogene cluster is present in all known 

mercury-methylating bacteria, and 

they predicted that more than 50 other 

microorganisms may methylate mercury because they have a pair of similar genes.

Another key to the development was 

the collection of talent assembled to 

work on this problem.

“This discovery was made possible 

by our diverse team, which includes 

scientists with expertise in chemistry, 

ORNL scientists solve mercury mystery

Source: www.ornl.gov/content/ornl-scientists-solve-mercury-mystery

computational biology, microbiology, 

neutron science, biochemistry, and 

bacterial genetics,” said Liang, who 

rated this paper as one of the most satisfying of her career.

Mercury is a toxin that spreads around 

the globe mainly through the burning of coal, industrial use, and through 

natural processes such as volcanic 

eruptions. The chemical element bioaccumulates in aquatic food chains, 

especially in large fish. Various forms 

of mercury are widely found in sediments and water.

In a report just released by the United 

Nations Environmental Programme, 

Achiim Steiner, UN under-secretary 

general and executive director of 

UNEP, notes that “mercury remains a 

major global, regional, and national 

challenge in terms of threats to human 

health and the environment.”

This research was funded by DOE’s 

Office of Science. Other ORNL co-authors are Jerry Parks, Alexander Johs, 

Mircea Podar, Richard Hurt, Stephen 

Tomanicek, Yun Qian, Steven Brown, 

Craig Brandt, Anthony Palumbo, Jeremy Smith, and Dwayne Elias. Podar, 

Brown, Smith, and Elias hold joint appointments at the University of Tennessee. Authors from the University of 

Missouri are Romain Bridou, Steven 

Smith, and Judy Wall.

34 35
Using the ORNL and UT 

supercomputers to design new drugs 

is an important and exciting field.

Drug Discovery 

and Drug Design

The drug discovery and drug design 

projects in the Center for Molecular 

Biophysics integrate technological, 

fundamental advances in many 

aspects of computational biology and 

apply these advances to contemporary, 

industrial-scale projects. The Center’s 

expertise in supercomputing and 

the dynamics of biomolecules has 

led us to develop extremely powerful 

computational approaches to drug 

discovery on supercomputers. This 

success allows us to investigate 

biomedical problems that are quite 

complex in nature, beyond proteindrug interactions, such as modulating 

protein-protein interactions, or 

characterizing the effect of small 

molecules on biochemical pathways. 

The Center addresses fundamental 

questions relevant to the mechanisms 

of conformational selection, to the 

characterization of interactome 

structures and dynamics, relevant 

to fundamental chemical biology 

perspectives of biological control. In 

addition, the Center is fully engaged in 

collaborative research with academic 

and industrial groups on specific drug 

discovery endeavors that have all 

led to novel molecular entities being 

discovered against diverse targets. 

The following descriptions detail these 

successes for selected projects. The 

Center’s groups are now developing 

a fully integrated, supercomputerbased technological approach that 

predicts the specificity and toxicity of 

drug candidates, resulting in a virtual 

platform for structure-based preclinical and clinical essays, cutting the 

time and cost of molecular discovery 

and changing the shape of the drug 

discovery field.

Supercomuputer 

Assisted Drug Discovery

A traditional view of protein-ligand 

or protein-drug binding can be 

understood conceptually as a lockand-key model. The protein is the lock 

or a static structure that provides a 

complementary shape for a ligand (the 

key) to fit into and result in a biological 

function. However, this model is not 

an accurate description of reality. 

Proteins are dynamic molecules that 

can exist in an abundance of different 

conformational states representing 

an ensemble. Ligand binding can 

then be understooand through 

the lens of a new model known as 

conformational selection. In this new 

model, a given ligand or drug may 

select for a certain conformation of a 

protein that precedes binding. This 

presents a challenge for traditional 

computational drug discovery 

methods which typically only use one 

structure, such as a crystal structure 

or homology model, to screen or dock 

many different potential drugs. As a 

result, our group has developed an 

open-source software known as 

VinaMPI that allows for thousands 

of potential drugs to be docked onto 

many different conformations of 

proteins in parallel using leadershipclass supercomputing facilities such as 

the Titan supercomputer. 

The software and the ensemble 

docking method have demonstrated 

an improved enrichment, ability 

to identify known ligands, over 

docking to only the crystal structure 

for a number of different proteins. 

In addition, our group has recently 

applied VinaMPI and/or the ensemble 

docking method to a number of 

different projects that have all led 

to the discovery of novel inhibitors. 

First, docking of 80 compounds to 

three conformations of the Z variant 

of alpha-1-antitrypsin led to the 

discovery of a novel inhibitor for the 

polymerization of the protein, which 

is associated with fatal symptoms. 

Second, we have used VinaMPI and 

ensemble docking to identify 11 

inhibitors of the efflux pump in E.coli, 

a tripartite protein complex that 

ejects numerous antibiotics out of the 

cell contributing to resistance. Our 

new inhibitors discovered using the 

ensemble docking approach could 

help to tackle the problem of multidrug 

resistance in bacteria. The final 

application completed, thus far, in our 

group involved the fibroblast growth 

factor 23(FGF23), which is discussed 

below.The binary complex forms a 

receptor that interacts with a hormone 

known as FGF23 (fibroblast growth 

factor 23). FGF23 is responsible for 

keeping blood phosphate levels within 

a normal range. 

Detailed analysis of interactions within proteins - 

methyl groups probe local 

environments.

Using the power 

of supercomputers 

to accelerate drug 

discovery.

37

Drug Discovery

GET WELL SOON
The binary complex composed of the 

alpha-klotho and fibroblast growth 

factor receptor exists as an ensemble 

of representative structures. FGF23 

can exist as two cleaved fragments 

known as the N-terminal and 

C-terminal fragments or as the fulllength hormone. When the C-terminal 

fragment binds the binary receptor, a 

specific conformation of that complex 

is selected for to bind the C-terminal 

fragment. This conformation becomes 

known as an active state leading to an 

increase in blood phosphate levels. On 

the other hand, when the full-length 

FGF23 hormone binds the receptor 

complex, a different conformation is 

selected for that leads to a decrease 

in phosphate levels. Diseases such 

as hypophosphatemia (low blood 

phosphate) and hyperphosphatemia 

(high blood phosphate) result from 

different genetic mutations that 

disrupt the function of FGF23 and the 

binary receptor to regulate phosphate. 

As a result, new small molecules are 

needed to restore phosphate levels to 

within a normal range during these 

diseased states. Our group is able to 

use computer simulations to generate 

and identify different conformations 

of the FGFR/alpha-klotho complex 

and then screen thousands of small 

molecules to each conformation in 

order to discover new inhibitors and 

binding sites. If a small molecule is 

able to bind to a given conformation 

of the receptor complex, it would 

stabilize that conformation leading 

to an increase or decrease in blood 

phosphate levels restoring them to a 

normal range. 

Supercomputing and Docking 

CMB uses molecular modeling 

and computational chemistry to 

investigate how medically relevant 

biomolecules interact with each 

other. We are particularly interested in 

molecular discovery, i.e. how to select 

and/or design small molecules, like 

pharmaceuticals, that will interact in 

a specific and potent way with much 

larger molecules, like proteins. Small 

molecules may sometimes enhance, 

or sometimes inhibit, the functioning 

of the proteins to which they bind. To 

discover or design a new drug against 

38 39

a disease, we must understand a great 

deal about what the target proteins 

look like and how they function, such 

as where possible binding cavities are 

located in the protein, how these cavities 

change their shapes with time, and how 

the atoms in the proteins interact with 

those of the pharmaceuticals. 

The availability of thousands of 

processors, either localized together in 

a supercomputer, or delocalized as in 

cloud computing, can be used to perform 

virtual screening of massive databases 

of chemicals against protein targets. To 

take advantage of these giant computers, 

Jerome Baudry, postdoctoral fellow 

Barbara Collignon, Roland Schulz and 

Sally Ellingson have developed efficient, 

well validated computer programs 

for docking (see press release). Sally 

Ellingson is continuing this work and 

developing it for Cloud architectures 

and to allow multiple protein targets 

to be used efficiently in the docking 

process. It is now possible to investigate 

computationally how millions of 

compounds would bind in a given 

protein, or in multiple proteins of a 

biochemical pathway. These approaches 

are used in collaboration with 

experimental laboratories to discover 

novel classes of molecules against 

several endocrine cancers and infectious 

diseases. With the advent of the exascale 

in supercomputing, it may become 

possible to screen essentially complete 

ligand databases against all known 

classes of protein in about one day.

To improve the efficiency of drug design, 

it is very important to understand how 

drugs and proteins interact with each 

other at the atomistic scale. We are developing new views on how 

non-bonded interactions control the 

dynamics and the energetics of protein/

ligand complexes through facilitation 

of molecular rotations and anion/πinteractions. We are also characterizing 

how medically and 

pharmaceutically important proteins are 

behaving, to understand how drugs may 

affect them: we are building the “protein 

skyscrapers” that control how bacteria 

look for food and we are following each 

and every water molecule that flushes a 

P450 enzyme’s active site to help detoxify 

drugs.

Design of smart anticoagulant 

drugs using ensemble-based highthroughput virtual screening

Karan Kapoor (who is now a postdoc researcher after graduating from 

Jerome Baudry’s lab at the University of 

Tennessee) has developed and applied 

computational approaches in aiding the 

drug-discovery efforts. He was working 

on a project in collaboration with 

Shifa Biomedical Corporation (Shifa), a 

structure-based drug discovery company 

located in Malvern, Pennsylvania. The 

project was funded by the NIH Small 

Business Innovation Research (SBIR) 

program, that supports early-stage 

small businesses engaged in research 

and development (R&D) with a strong 

potential for commercialization.

The prothrombinase (PTase) enzymatic 

complex, consisting of the enzyme 

factor Xa (FXa) and a protein cofactor 

factor Va (FVa), catalyzes the cleavage 

of prothrombin (PT) leading to the 

formation of thrombin and clotformation. The risk of serious bleeding, 

particularly at high dosage, is a major 

liability of anticoagulant drugs that 

are active-site competitive inhibitors 

targeting the FXa-PT binding site. 

Superimposed conformers of FGF23 identify new binding sites used in ensemble docking(left). Binding sites are 

indicated by blue, red, and green balls. Docking shows that many small molecules can bind to these identified 

binding sites(right). In the image on the right, small molecules are shown in red, green, and blue. 
The goal of this project was to target 

FXa within the PTase complex, but 

instead of seeking another activesite directed inhibitor, a novel 

computational approach utilizing 

MD simulations and ensemble-based 

high-throughput virtual screening 

was used to identify compounds that 

can potentially alter the interaction 

between FXa and FVa. This lead to 

the successful identification of ten 

compounds, represented by three 

small-molecule families of inhibitors, 

that achieve dose-independent partial 

inhibition of PTase activity in a nonactive site dependent and self-limiting 

mechanism.

Identification of successful leads 

is only the first step in the drugdiscovery pipeline, which usually takes 

12–15 years. A major challenge facing 

the pharmaceutical industry today is 

finding innovative ways of reducing 

the high-attrition rates associated with 

the drugs, especially in the clinical 

trials stage. ‘Phase 0’ clinical trials 

have been suggested by the Food and 

Drug Administration (FDA) that seeks 

metabolism, toxicology, and other 

data from micro-doses administered 

to very small (10-15) groups of patients. 

Innovative computational techniques 

can be developed that will complement 

this data by predicting the metabolic 

pathways and off-target effects of the 

potential drug candidates. This will 

need to be complemented by policy 

changes that makes it profitable 

for companies to develop naturally 

occurring compounds like curcumins 

and catechins that have been shown 

to target different cancers. At the end 

of the day, the goal of this research 

is to provide safe and sustainable 

treatments for the major diseases to 

everyone in a cost-effective manner.

AIDS Drug Design

HIV-1 integrase is an important enzyme in viral HIV replication with apparently no human counterpart, making it an attractive therapeutic target 

for the clinical treatment of HIV/AIDS. 

Despite two decades of tremendous 

effort leading to promising integrase 

inhibitors, their binding modes remain 

elusive. The goal of this project, led by 

Xiaolin Cheng, is to understand at the 

molecular level the binding of potent 

inhibitors in the HIV-1 integrase active 

site, and the structural mechanisms for 

drug-resistant viral mutants. Cheng is 

also using computer simulation to understand and predict from a statistical 

perspective the combinatorial mutation patterns responsible for HIV drug 

resistant mutant

s.

40

Interactions in Proteins

The Baudry group is also interested in 

the effect of nonbonded interactions in 

protein-protein and protein-ligand complexes. Undergraduate student William 

Hembree used quantum chemistry to 

investigate how the rotational dynamics 

of methyl groups, an important marker 

in chemistry, is affected by their microenvironment. In collaboration with the 

groups of Dr. Howell and Dr. Hinde at the 

University of Tennessee, the group, along 

with former graduate student Jason Harris, was also investigating how anion/πinteractions in proteins and proteinligand complexes contribute to protein 

stability and dynamics.

Chemotaxis - 

Ligand based signaling pathways

Postdoctoral researcher Derek Cashman worked on a collaborative project 

involving the groups of Jerome Baudry 

and Igor Zhulin that involves the integration of bioinformatics with biophysical 

studies. The first step involves creating 

a natural classification of chemotaxis 

proteins based on phylogenetic analysis and identifying conserved residues 

within evolutionarily related subgroups, 

and co-variance analysis of co-evolving 

residues. This information was then 

integrated with machine learning analysis of surface patches on each protein to 

predict potential sites for protein-protein 

interactions, leading to molecular docking and computational simulations to test 

the protein-protein interactions of each 

model. These results were then used to 

drive further experimental and systems 

biology research by our collaborators and 

other laboratories. The principles learned 

through these studies will provide insight 

into about signal transduction mechanisms, and will aid in the design of new 

therapeutics targeting the signaling pathways that control virulence in human 

pathogens.

41

The structural basis of bacterial chemotaxis. A hypothetical model of the bacterial 

chemoreceptor (MCP, shown in green) interacting with the scaffolding protein, CheW, and 

histidine kinase, CheA (both shown in blue). These proteins are part of a signal

 transduction cascade that responds to stimuli in the environment and 

 transfers a signal to the bacterial flagellar apparatus to control the 

cell’s swimming behavior in response to that stimulus.

Active site of the HIV protein PFV integrase. 
June 22, 2011 - Jeremy Smith, Governor’s Chair for Molecular Biophysics at 

the University of Tennessee, Knoxville, 

has helped reveal a key trigger of Gerstmann–Sträussler–Scheinker (GSS) syndrome, a rare but deadly neurodegenerative disease. The finding could have 

far-reaching implications for the treatment of other neurodegenerative diseases such as Alzheimer’s, Huntington’s, 

and Parkinson’s.

Smith conducted his research with two 

collaborators in Italy: Isabella Daidone, 

a former postdoctoral researcher of his 

who is now at the University of L’Aquila, 

and Alfredo Di Nola of Sapienza – Università di Roma.

Most GSS patients begin developing 

symptoms in their late fifties. Symptoms include loss of memory, difficulty 

speaking, and unsteadiness and lead to 

progressive dementia, and then death 

within a few months or years. There 

is presently no cure or treatment. The 

disease results from a single, tiny mutation in a protein, resulting in it having a wrong shape—through “misfolding”—then aggregating to form amyloid 

plaques in the brain.

“Ever since the ‘mad cow’ scare in Britain 

in the 1990s, which led to several hundred human deaths and 4.4 million cattle 

being destroyed, I’ve been interested in 

finding out more about these fascinating diseases of wrongly shaped proteins,” 

said Smith, who was born in England.

The team 

compared 

high-performance 

computer 

simulations of the 

structures 

of the normal and the 

GSS–mutant proteins. They found the 

GSS protein looks dramatically different 

from the normal form and revealed how 

its shape is primed for plaque formation.

“This research shows how computer simulation can be used to pinpoint changes 

in molecular structure that lead directly 

to disease,” said Smith. “We think that a 

similar line of investigation should prove 

beneficial in understanding the origins 

of other amyloid diseases such as Alzheimer’s, Parkinson’s, and rheumatoid 

arthritis. Once the origin is understood 

at molecular detail, strategies to rationally prevent and cure a disease can be 

conceived.”

The findings can be found in the article, “Molecular Origin of Gerstmann–

Sträussler–Scheinker Syndrome: Insight 

from Computer Simulation of an Amyloidogenic Prion Peptide” in this month’s 

edition of the Biophysical Journal.

The research was funded in part by a Marie Curie grant from the European Union.

UT Scientist Uncovers Trigger to Fatal 

Neurodegenerative Disease

Source: www.utk.edu/tntoday/2011/06/22/jeremy-smith-gss-protein

42 43

Simulation system with the human α7 nAChR 

receptor (ribbon) inserted into a lipid bilayer

(licorice).

Solvation of Active Sites

P450 proteins are very important enzymes responsible in the human body 

that are responsible for processing 

many pharmaceuticals. Jerome Baudry 

and Postdoctoral researcher Yinglong 

Miao have used computer simulation 

to reveal the highly dynamic nature of 

CYP101 P450 hydration. Water molecules enter and leave the active site on 

the nanosecond timescale, sustaining 

the efficiency of the enzyme.

Channel gating and ligand 

recognition in pentameric 

ligand gated ion channels

The nicotinic acetylcholine receptor 

(nAChR) is a ligand-gated ion channel. Binding of neurotransmitter molecules to nAChR induces structural 

rearrangements of the membranespanning domain, which permits the 

influx of cations and leads to message 

propagation. Due to their essential 

roles in synaptic transmission, nAChRs 

have emerged as attractive therapeutic 

targets for the treatment of pain, cognitive impairment, neurodegenerative 

disease, schizophrenia, epilepsy, anxiety, and depression. Fundamental steps 

in receptor activation include neurotransmitter recognition, coupling 

of recognition to opening of the ion 

pore, and passive flow of ions through 

the pore. Xiaolin Cheng has performed 

extensive molecular dynamics simulations to understand the molecular 

mechanisms underlying all three of 

these fundamental steps. Cheng’s 

calculations probe the energy barriers 

to ion conduction and origins of ion 

selectivity in the channel. 

Detoxifying-enzymes cytochrome P450s “flush” their 

active site to ensure catalytic efficiency.
A team led by Jerome Baudry of the 

University of Tennessee-ORNL Center for Molecular Biophysics adapted a 

widely used existing software to allow 

supercomputers such as ORNL’s Jaguar to sift through immense molecular 

databases and pinpoint chemical compounds as potential drug candidates.

The research was published in the 

Journal of Computational Chemistry 

as “Task-parallel MPI implementation 

of Autodock4 for docking of very large 

databases of compounds using High 

Performance Super-Computers.”

Supercomputing Research Opens 

Doors for Drug Discovery

Source: www.sciencedaily.com/releases/2010/12/101209164146.htm

A quicker and cheaper technique to scan molecular databases developed at the 

Department of Energy’s Oak Ridge National Laboratory could put scientists on the 

fast track to developing new drug treatments.

Supercomputers could help speed up the drug discovery process by identifying suitable 

chemicals (seen as gray spheres) that can dock onto a designated target in the body, such 

as a protein (seen as red ribbons). (Credit: Image courtesy of DOE/Oak Ridge National 

Laboratory)

“Our research is the missing link between supercomputers and the huge 

data available in molecular databases 

like the Human Genome Project,” 

Baudry said. “We have an avalanche of 

data available to us, and now we need 

to translate that data into knowledge.”

Such translation is critical for the first 

stages of drug development, in which 

researchers look for appropriate chemicals that interact with a target in the 

body, typically a protein. If the chemical is suitable, it attaches onto the 

protein and produces a desirable effect 

in the cell.

But with thousands of known proteins 

and millions of chemicals as potential 

drugs, the number of possible combinations is astronomical.

“It is very expensive and time-consuming to measure these interactions 

experimentally,” Baudry said. “But with 

supercomputers, we can process millions of molecules a day.”

The quick and efficient processing of 

molecules offers scientists an opportunity to take risks on previously unexamined drug candidates, which could 

lead to diverse and innovative classes 

of drugs.

“Before, we threw away a lot of information because molecules did not 

have a preferred profile,” Baudry said. 

“Now, every molecule can be examined 

without worrying about wasting resources.”

The researchers have already started 

work to launch the research into reality through a new collaboration supported by the National Institutes of 

Health. The project team plans to put 

the computational development to 

work on ORNL supercomputers to look 

for chemicals that could treat prostate cancer. The research is funded by 

a NIH Clinical Translational Science 

Award, which was awarded to Georgetown and Howard Universities and 

includes ORNL, Med/Star Health and 

the Washington D.C. Veterans Affairs 

Medical Center as key partners.

“Our development work is the computational equivalent of building the 

Saturn V rocket,” Baudry said. “Now we 

want to fly it to the moon.”

Funding for the initial development 

work was provided by ORNL’s Laboratory Directed Research and Development program. The University of 

Tennessee and the Joint UT/ORNL 

Genome Sciences and Technology 

graduate program also supported the 

work. The research team included 

Barbara Collignon, Roland Schulz and 

Jeremy Smith of the UT-ORNL Center 

for Molecular Biophysics. The three 

researchers as well as Baudry are also 

affiliated with the University of Tennessee’s Department of Biochemistry 

and Cellular and Molecular Biology.

44 45
Understanding enzyme catalysis is an important part of our work, with two principal 

investigators, Hong Guo and Jerry Parks and senior visiting scientist Toyokatsu Ishida 

from the Japanese National Institute of Advanced Industrial Science and Technology 

(AIST) specializing in this field. Enzymes accelerate chemical reactions that are of 

critical importance in bioenergy, such as the hydrolysis of cellulose, and subsurface 

biogeochemistry, such as mercury detoxification. Although enzymologists can find 

out much useful information experimentally, only computer simulation, and, in particular quantum chemistry, can determine complete reaction mechanisms, producing ‘molecular movies’ of reactions happening with the corresponding energetics.

Hong Guo has a general interest 

in understanding the origin of the 

high catalytic efficiency and selectivity of enzymes. In addition 

to being of fundamental scientific 

importance, these studies also improve the basis for designing inhibitors, efficient drugs and enzyme 

mimics. He normally studies several 

systems at once, including recent 

work on protein lysine methyltransferases, RNA polymerases, serinecarboxyl peptidases, chorismate 

mutase, cytidine deaminase, and 

adenosine deaminase. 

He has also participated in DOE 

work understanding the mechanism of action of a mercuric reductase and cellulases.

Jerry Parks came to CMB from the 

renowned group of Weitao Yang at 

Duke University, and is now spearheading research into catalysis involving mercury. Here, he discusses 

recent achievements and methodological roadblocks facing mixed 

quantum mechanical/molecular 

mechanical (QM/MM) methods.

What is the challenge of QM/MM 

calculations? Is it the accuracy? 

Is it the many possible reactions 

that need to be considered?

Parks: The issue is that there’s not just 

one challenge — there are several. For 

example, there’s always a trade-off 

between accuracy and affordability of 

the calculations. A given method needs 

to be assessed carefully to make sure 

it’s accurate enough for the questions 

you’re trying to address. There are 

often many potential reaction pathways that need to be considered, and 

it is important not to introduce bias 

when selecting reaction coordinates. 

Describing the electrostatic effects 

correctly right can be a challenge, and 

achieving converged statistical sampling isn’t easy either. The simulations 

are not simple. It’s really a bit of an 

art to do things correctly, and we’re 

definitely still learning. Also, you don’t 

always need to use QM/MM calculations. You can greatly simplify your life 

sometimes by just using a QM-only 

approach.

Enzyme Catalysis

FASTER PLEASE

Quantum mechanical description of organomerucrial protonolysis. Two cysteine side chains in 

the active site of MerB coordinate with methylmercury, weakening the Hg-C bond. An aspartic 

acid side chain then protonates the -CH3 leaving group, breaking the bond and forming Hg(II) 

and methane. 

What characteristics of mercury catalysis have you learned 

from your calculations?

Parks: Using a QM-only active site 

model of the enzyme MerB, we learned 

how the enzyme breaks mercurycarbon bonds in methylmercury. 

Two cysteine side chains coordinate 

very strongly with methylmercury, 

which makes the mercury-carbon 

bond a bit longer and weaker. Then, a 

nearby aspartic acid side chain delivers a proton to the carbon atom. 

The result is that the enzyme produces inorganic Hg(II) and methane, and gets rid of methylmercury.

What’s the chemistry behind the 

microbial methylation of mercury?

Binding of Hg2+ by two Cys thiolates 

is thermodynamically extremely favorable. Hg2+ and its associated species have extremely high affinities for 

thiols and form very tightly bound 

complexes. However, these complexes 

can undergo rapid exchange between 

thiols, that is, one of the thiolates can 

readily dissociate, provided that a third 

thiolate first coordinates to Hg. 

Thus, a transient, trigonal complex 

species of mercury ion and three thiol 

groups is expected to be important in 

Hg2+ transfer reactions.

Also, acid-base chemistry in which 

thiols are deprotonated to generate 

nucleophilic thiolates, or coordinated 

thiolates are protonated to generate 

neutral leaving groups, can enhance 

the rates of Hg2+ transfer among pairs 

of thiols. QM/MM calculations were 

used to identify a possible mechanism 

for the intramolecular Hg2+ transfer 

in MerA, which can be considered a 

prototype for Hg transfer. Specifically, 

an X-ray crystal structure of the catalytic core of MerA with Hg2+ was used 

as a starting point for simulating Hg2+

transfer from the surface of the protein 

to the buried, inner pair of Cys residues in the active site.

From the computed Hg2+ transfer pathway, we note that Hg2+ is always paired 

with two or more thiolates. As Hg2+ is 

transferred from the solvent-exposed 

protein surface to the buried catalytic 

site, a proton is transferred in the opposite direction. The key mechanistic 

insight from the simulations is that 

Hg2+ transfer is facilitated by coupling 

the competitive binding of pairs of 

Cys residues with the proton affinities 

of the thiolates. These principles are 

expected to be general to other proteins and enzymes of the mer operon 

and metal ion trafficking in biological 

systems. 

46 47
Poplar: a potential biofuel feedstock investigated at ORNL.

The simultaneous representation 

of biological processes at different length- and time-scales is a 

fervent area of research at present, 

and comes from the realization that 

coarse-graining of atomistic interactions is necessary to allow the simulation of processes at the cellular, and 

eventually organismal level. We are 

particularly interested in developing multiscale concepts that will be 

able to be used on exascale capability supercomputers. Work in this 

area spearheaded by Xiaolin Cheng 

has involved finding ways to treat 

solvent implicitly, rather than explicitly, using “treecode” electrostatics. Cheng, together with graduate 

student Xiaohu Hu has developed 

an Adaptive Fast Multipole PoissonBoltzmann (AFMPB) solver for the 

linearized Poisson-Boltzmann (LPB) 

equation, achieving an overall order 

N complexity for both computational 

speed and memory usage. The parallel version is still under development and will be optimized for running on DOE leadership computers.

Coarse graining work performed by 

postdoctoral fellow Goundla Srinivas 

and graduate student Dennis Glass, 

in collaboration with the Ames National Laboratory in Iowa, involves 

the development and application of 

Boltzmann inversion techniques and 

of the “REACH” (Realistic Extension 

Algorithm via Covariance Hessian) 

methodology developed by Kei Moritsugu of the RIKEN National Laboratory in Tokyo with Jeremy Smith, which 

maps results obtained from atomistic MD simulations onto models for 

larger-scale, coarse-grained MD. 

Applications of CMB multiscale methodology have been directed toward 

understanding plant cell-wall deconstruction. Hydrolysis of cell-wall cellulose is the critical, rate-limiting step 

in cellulosic biofuel production. The 

physical properties of lignocellulosic 

biomass thus derived serve as a basis 

for interpreting an array of biophysical experiments, and, in particular, 

the simulation models derived will 

be used to calculate and interpret a 

variety of neutron-scattering properties. This combination of simulation 

and experiment will eventually lead 

to a description of the physicochemical mechanisms of biomass recalcitrance to hydrolysis, and thus will aid 

in developing a strategy as to how 

rationally to overcome the resistance. 

Multiscale Methods

SCALING FROM ATOMS UPWARDS

49
‘Fingerprints’ match molecular simulations with reality

Source: www.ornl.gov/news/fingerprints-match-molecular-simulations-reality

February 22, 2011 - A theoretical technique developed at the Department of 

Energy’s Oak Ridge National Laboratory 

is bringing supercomputer simulations 

and experimental results closer together 

by identifying common “fingerprints.”

ORNL’s Jeremy Smith collaborated on 

devising a method -- dynamical fingerprints --that reconciles the different 

signals between experiments and computer simulations to strengthen analyses 

of molecules in motion. The research 

will be published in the Proceedings 

of the National Academy of Sciences.

“Experiments tend to produce relatively 

simple and smooth-looking signals, as 

they only ‘see’ a molecule’s motions at 

low resolution,” said Smith, who directs 

ORNL’s Center for Molecular Biophysics and holds a Governor’s Chair at the 

University of Tennessee. “In contrast, 

data from a supercomputer simulation 

are complex and difficult to analyze, as 

the atoms move around in the simulation in a multitude of jumps, wiggles and 

jiggles. How to reconcile these different views of the same phenomenon 

has been a long-standing problem.”

The new method solves the problem 

by calculating peaks within the simulated and experimental data, creating distinct “dynamical fingerprints.” 

The technique, conceived by Smith’s 

former graduate student Frank Noe, 

now at the Free University of Berlin, can then link the two datasets.

Supercomputer simulations and modeling capabilities can add a layer 

of complexity missing from many 

types of molecular experiments.

“When we started the research, we had 

hoped to find a way to use computer 

simulation to tell us which molecular 

motions the experiment actually sees,” 

Smith said. “When we were finished we 

got much more - a method that could 

also tell us which other experiments 

should be done to see all the other motions present in the simulation. This 

method should allow major facilities 

like the ORNL’s Spallation Neutron 

Source to be used more efficiently.”

Combining the power of simulations and 

experiments will help researchers tackle 

scientific challenges in areas like biofuels, drug development, materials design 

and fundamental biological processes, 

which require a thorough understanding 

of how molecules move and interact.

“Many important things in science depend 

on atoms and molecules moving,” Smith 

said. “We want to create movies of molecules in motion and check experimentally 

if these motions are actually happening.”

View a supercomputer simulation of 

a protein in motion here:http://www.

ornl.gov/ornlhome/hg_mer.htm

“The aim is to seamlessly integrate supercomputing with the Spallation 

Neutron Source so as to make full use 

of the major facilities we have here 

at ORNL for bioenergy and materials science development,” Smith said.

The collaborative work included researchers from L’Aquila, Italy, Wuerzburg 

and Bielefeld, Germany, and the University of California at Berkeley. The research was funded in part by a Scientific 

Discovery through Advanced Computing 

grant from the DOE Office of Science.

ORNL is managed by UT-Battelle for the 

Department of Energy’s Office of Science.

50 51

Dynamical fingerprints, calculated from discrete states obtained from high-performance simulation, permit 

spectra to be calculated that can be directly compared with equivalent experimentally derived quantities.
Row

Chain

... i+1 i i-1...

< non-reducing end reducing end >

1

2

3

...

M

Hydrogen Bond 

Networks in Cellulose

A major cause of biomass recalcitrance 

to deconstruction is the high structural 

ordering of natural cellulose fibrils, 

which arises largely from an extensive 

hydrogen-bond network between and 

within the cellulose polymers. Tongye 

Shen, Xiaolin Cheng and Jeremy Smith 

have worked with Heinrich Klein, an 

undergraduate student at the University of Heidelberg, to derive a latticebased model of hydrogen bonding 

in cellulose Iα. The plasticity of the 

hydrogen bond network as evidenced 

by two competing hydrogen bond 

patterns leads to an entropic contribution stabilizing the crystalline fibril at 

intermediate temperatures. At these 

temperatures, an enhanced probability 

of hydrogen bonding causes increased 

resistance of the entire fibril to deconstruction, before the final disassembly 

temperature is reached. The results 

thus provide a microscopic explanation for the physical origin of recalcitrance arising from the frustration of 

the hydrogen bond network.

Coarse-Graining Cellulose

A systematic method has been developed by postdoctoral researcher 

Goundla Srinivas for generating and 

representing both crystalline and 

amorphous cellulose states. The developed CG models allow the exploration 

of cellulose fibril structures for lengthand time-scales beyond the reach of 

atomistic simulations. Srinivas has 

also been developing a CG force-field 

for cellulose fibrils in explicit water.

Transition of cellulose fibril from crystalline to amorphous structures.

Lignin molecule

An illustration of the sheet structure of cellulose Iα and the hydrogen bond network.

52 53
Biomembrane research at CMB

54 55

Cell membranes display remarkable 

organization. In the transverse 

dimension, they are compositionally 

asymmetric, while in the lateral 

dimension, they are believed to 

contain nanoscopic domains (“lipid 

rafts”) critical to their function. In 

this regard, questions arise about 

the interplay between lipid rafts and 

compositional asymmetry, including 

how asymmetry is maintained, 

whether rafts bridge the two halves 

(leaflets) of asymmetric bilayers, 

and if so, how. To resolve these 

questions, Xiaolin Cheng and Jianhui 

Tian, a post-doc researcher at CMB 

are combining high-performance 

computer simulations, bottomup assembly of asymmetric model 

membrane systems and neutron 

scattering experiments. Neutron 

scattering is used to study the effects 

of compositional asymmetry, while 

atomistic molecular dynamics 

simulations of the experimental 

systems have been performed on 

ORNL’s TITAN supercomputer using 

innovative, scalable enhanced 

sampling algorithms. The system will 

comprise ~60 million atoms, and 

span time scales ≥10 μs, making it 

among largest atomistic biological 

simulations to date. These efforts 

will culminate in the development 

of a synthetic protocell displaying 

key hallmarks of a living cell - active 

membrane transport; dynamic, 

asymmetric membrane structure; 

and respiratory energy metabolism. 

In this way, it is planned to address 

long-standing questions in membrane 

biology regarding asymmetry and the 

transverse coupling of lipid domains.

Adaptive Biosystems Imaging (ABI)

Although diverse tools are available to 

image biological systems, each provides only narrow windows of spatial, 

temporal and chemical data and, by 

itself, no systems-level knowledge. 

Therefore, the overarching problem 

is how to assemble intracellular, extracellular and phenotypic data, collected across multiple platforms 

and over many orders of magnitude 

of length and time, into a coherent 

understanding of coupled and interdependent biological processes.

Xiaolin Cheng and his collaborators 

at various other research institutions 

are developing and implementing an 

Adaptive Biosystems Imaging (ABI) 

capability that integrates new ways of 

acquiring chemical image data with 

a hierarchical computational framework in order to realize correlated 

measurements and interpretations 

of biological processes at extended 

spatial and temporal scales. By adaptive, we mean an imaging approach 

that involves the coordinated, mutual 

evolution of observation and systems models. Observations, derived 

from multiple imaging techniques, 

structural biology and omics-based 

measurements, are animated and 

explained through multiscale simulations. On the computational front, 

effort is focused on developing and 

coupling multiscale computational 

techniques and algorithms (e.g., molecular dynamics, Brownian dynamics 

and whole-cell simulations) to bridge 

resolution gaps, to integrate structural 

and functional data from multiple 

sources and to embed complex structures into their biological context.
56 57

The 2013 Nobel Prize in Chemistry has 

been awarded to Martin Karplus, Michael Levitt, and Arieh Warshel for their 

work on developing computational 

methods to study complex chemical 

systems. Their work has led to mechanistic critical insights into chemical 

systems both large and small and has 

enabled progress in a number of different fields, including structural biology.

Structure determines function! For decades, this has been the mantra of biologists worldwide. For molecular biologists, 

since the solving of the quintessential 

double helix, the serial revelations of 

awe-inspiring atomic-detail architectures have done little to dissipate this 

structure-function frenzy. And yet, even 

from the epoch of the first protein structures from X-ray crystallography in the 

1960s, experimentalists have always 

known that their static structures are not 

enough. Structure can only ever serve as 

a starting point to understanding biology. It has long been obvious to all that 

biomolecules must move to perform 

their functions (‘‘Eppur si muove!’’ [And 

yet it does move! (Galileo Galilei)]). Energies and forces are what form structure 

and drive the thermodynamics and dynamical motions that underlie biological function. Thus, to understand and 

comprehend how macromolecules work, 

one must ultimately be able to ‘‘visualize’’ the manner in which these complex 

nanoscale ‘‘molecular machines’’ move 

and change their shape atom-by-atom 

as a function of time as they perform 

their activities. The problem, however, is 

that the three-dimensional architectures 

provided by the major atomic-resolution 

structural techniques tell us nothing 

about the energies and forces involved. 

In order to quantify dynamics, some way 

to associate energies and forces with 

molecular geometry was urgently needed. The solution to this problem, largely 

built around the pioneering work of the 

three 2013 Nobel Laureates in Chemistry, 

has been to reconstruct a virtual reality 

in the computer to simulate molecular 

dynamics.

Transformative research can best be 

appreciated with long hindsight, and 

although this luxury is not often afforded at the time of the Nobel award, this 

year’s prize gives us this pleasure. The 

‘‘origins’’ of the field are, of course, arbitrary to define, but might pragmatically 

be placed in the 1940s–1950s, with the 

first computational molecular dynamics simulations and the development of 

spectroscopic force fields to interpret 

infrared and Raman spectra. Also progressing was the field of the previous 

chemistry ‘‘theory’’ Nobel prize, awarded 

in 1998, of quantum chemistry. Although 

not always yielding quantitatively correct results, quantum chemistry does 

provide a relatively consistent framework for looking at molecules, and, in 

contrast to biomolecular simulation, the 

associated computer programs, with 

their limited functionality, are, in the 

main, arguably hard to use incorrectly. 

However, accurate quantum chemistry 

scales atrociously with the number of 

electrons involved, a challenge that may 

never be overcome without discovering 

an alternative, computationally-tractable, 

theoretical route for electronic structure calculations. Hence, to tackle large 

interesting biomolecular systems, a large 

helping of empiricism was needed, and 

‘‘molecular mechanics’’ was born.

The middle and late 1960s found all three 

future Nobelists influenced by Schneior 

Lifson at the Weizmann Institute, who 

was developing ideas for using molecular mechanics empirical functions to 

calculate the energies of large molecules. 

The novel idea was to use a functional 

form that could serve not only for calculating vibrational frequencies, as 

did the spectroscopic force fields using expansions of the potential about a 

minimum-energy structure, but also for 

determining that structure. The so-called 

‘‘consistent force field’’ (CFF) of Lifson 

and his coworkers, particularly Warshel, 

included nonbonded interaction terms 

so that the minimum-energy structure 

could be found after the energy terms 

had been appropriately calibrated. The 

possibility of using such energy functions for larger systems was becoming 

apparent at that time, and Levitt and 

Lifson pioneered the calculation of the 

energy of a protein from atomic coordinates (Levitt and Lifson, 1969).

The 2013 Nobel citation specifically 

focused on a form of multiscale modeling in which calculation of the energy 

of a real system, such as an enzyme, is 

performed by combining molecular 

mechanics modeling of the environment 

with quantum chemical modeling of the 

core region (such as the active site) in 

which the chemically interesting action 

takes place. An important step was taken 

when Warshel visited Karplus at Harvard in the beginning of the 1970s. They 

constructed a computer program that 

used a hybrid method combining classical and quantum mechanics to describe 

the p-electron and vibrational spectra of 

a number of planar molecules (Warshel 

2102 Structure 21, December 3, 2013 

a2013 Elsevier Ltd All rights reserved and 

Karplus, 1972). Later, in 1976, Warshel 

and Levitt constructed a more general 

scheme for a partitioning between electrons that are included in the classical 

modeling and those that are explicitly 

described by a quantum chemical model. 

They reported this in their study of the 

‘‘Dielectric, Electrostatic and Steric Stabilisation of the Carbonium Ion in the 

Reaction of Lysozyme’’ (Warshel and 

Levitt, 1976). These techniques initiated 

what, today, is known as the ‘‘QM/MM’’ 

approach, which combines quantum 

mechanics with molecular mechanical 

modeling and is widely used to understand enzyme reactions. The original 

work at Harvard involved calculating 

the vibronic spectra of retinal and related molecules. Retinal, of course, is the 

chromophore in the light-driven proton 

pump protein, bacteriorhodopsin, and 

this work illustrates nicely the convergence of broad goals in structural biology and computational chemistry. At 

around the same time as the pioneering 

QM/MM polyene calculations, efforts 

were being made to obtain the first highresolution structure of a 

Eppur Si Muove!

The 2013 Nobel Prize in Chemistry

Source: www.cell.com/structure/abstract/S0969-2126(13)00439-5

By Jeremy C. Smith and Benoît Roux

Martin Karplus, the former post-doc mentor of 

Jeremy C. Smith and Ph.D. advisor of Hong Guo

Photo credit: Bengt Nyman
58 59

membrane protein, using none other 

than bacteriorhodopsin itself. 

QM/MM was one of many creative ideas 

in biomolecular modeling, and simulation from the three laureates in the 1960s 

and 1970s seeded much of what is possible today. However, although important, QM/MM is only one area of activity 

of the three winners, and the 2013 Nobel 

prize is therefore seen by many also as 

recognition of the numerous other 

critical contributions of the three as well 

as recognition of the field of biomolecular simulation as a whole. In large part, 

the conceptual contribution of biomolecular simulation relates to dynamics. In 

a CECAM workshop at Orsay in 1976, 

calculations were performed by Andrew 

McCammon, leading to the first publication with Bruce Gelin and Martin Karplus 

of a molecular dynamics (MD) simulation of a protein (McCammon et al., 1977). 

These CECAM workshops were opportunities for very fertile discussion and 

exchange among scientists, and it is 

clear that a number of people at the time 

contributed to the ideas that led to ‘‘molecular dynamics’’ simulations of biological macromolecules. One name that 

particularly comes to mind is Aneesur 

Rahman, who had been the first to carry 

out MD simulations of liquids using 

realistic models (Rahman, 1964). The 

subject of the 1976 calculation was the 

small protein BPTI (Figure 1A), simulated 

in vacuum for a fleeting 9 ps. Although 

crude and short by modern standards, 

this simulation arguably changed mindsets, ushering in the era of the dynamic 

protein. One of the major conclusions 

was that the internal motion of the 

protein is fluid-like at room temperature, 

and much subsequent research has 

concentrated on the comparison between this fluid-like physiological state 

and the glass-like state of proteins at low 

temperatures. Even to this day, the mere 

suggestion that proteins might display 

any fluid- or liquidlike dynamics on 

such a short timescale and lengthscale 

provokes the ire of some crystallographers. 

However, MD suffers from statistical 

convergence problems and force field 

errors, and, in years ensuing from the 

first simulation, its use in structure 

prediction was found to be equivocal. 

Levitt himself referred to the use of 

molecular mechanics in structure refinement of homology models as ‘‘the central 

embarrassment of molecular mechanics, 

namely that energy minimization or 

molecular dynamics generally leads to a 

model that is less like the experimental 

structure’’ (Koehl and Levitt, 1999). Many 

of the calculations performed were, and 

often are, wrong, inconsistent, and 

biased and disagree with experiment. 

Such shortcomings were assailed by 

solid, dependable structuralists. ‘‘It’s all 

rubbish!’’ was the opinion of biomolecular modeling and simulation expressed 

in the 1990s to one of the present authors by the director of a prestigious 

experimental structural biology laboratory. Although clearly rather a generalization, this opinion was perhaps forgivable, given that modeling papers had just 

been published on the critic’s own favorite system in which the active site structure had been completely massacred. To 

this day, harsh opinions about the field 

are still encountered, although perhaps 

to a lesser extent. And certainly, all three 

of the winners have had to endure their 

share of heavy professional criticism 

from different segments of the scientific 

community over the years.

One problem is that a lot can go wrong 

in an MD simulation of a biomolecular 

system, and authors publishing the 

results can end up looking like chumps. 

For a start, the ‘‘model system,’’ i.e., the 

atoms included in the calculation, can be 

incomplete; the experimental structures 

from which simulations started can be 

too inaccurate, or can be missing bits. 

Moreover, the environment, i.e., the 

solvent, including the water molecules 

and counterions must be represented 

reasonably well, even if some of this 

information is not known from experiments. Furthermore, the interaction 

potential (or ‘‘force field’’) used in most 

simulations is an empirical compromise 

that is subject to several approximations. 

Finally, there is often not enough computing power to exhaustively sample 

what one is trying to look at, and simulators have been all too often tempted to 

read significance into isolated anecdotal 

events.

Even this year’s winners have not been 

immune to simulation gremlins. The 

Warshel and Levitt study folding BPTI 

(Levitt and Warshel, 1975), was later 

found to use overly-permissive criteria 

for success; a structure superficially 

resembling that of native BPTI was 

found from a sequence containing only 

alanines and glycines (Hagler and Honig, 

1978). Levitt’s early papers did not Figure 

1. Biomolecular Simulation Systems in 

the 1970s and Now (A) Bovine pancreatic 

trypsin inhibitor: the subject of the first 

published MD simulation of a protein 

(McCammon et al., 1977). (B) Modern-day 

multimillion-atom simulation model of 

lignocellulosic biomass with cellulose 

(green), lignin (brown), and hemicellulose (green strands). Structure explicitly 

include electrostatic interactions, because they were too expensive to compute. His paper on the simplicity of the 

prediction of stability and activity of a 

protein core (Lee and Levitt, 1991) was 

later criticized; comparable, if not better, 

agreement with the experimental data 

was reached using much simpler models 

based on straightforward structural 

considerations, which do not even 

require calculations on a computer (van 

Gunsteren and Mark, 1992). Exacerbating 

this type of problem was, and is, the 

public in-fighting between experts about 

which approach is best and the 1970s 

opinions of some theoretical chemistry 

purists that simulation is a cop-out, 

because we’re not bright enough to 

figure out an appropriate analytical 

theory. The field of biomolecular simulations has been, and continues to be, 

animated by numerous debates and 

controversies regarding the relative 

value of different approximations and 

the significance of various approaches. 

Computations are an ‘‘artifact’’ in the 

true sense of the term—they are the 

product of human craft—so there is an 

unavoidable element of subjectivity in 

judging them. Getting the ‘‘right’’ answer 

is nice, but it’s not enough; one must get 

it for the right reason, most theoreticians 

would argue (and argue they sure do!). 

Perhaps, then, we can understand why it 

took the Nobel committee 40 years to 

recognize the field!

The 2013 prize recognizes the sustained, 

profound effect that computational 

modeling and simulation has had on 

structural biology. Harnessing statistical 

mechanics to connect with macroscopic 

experiments, simulations have gone 

much further than simply describing 

internal motions. Indeed, these models 

provide a formal link between microscopic interactions and thermodynamics. Using computer ‘‘alchemy’’ has made 

it possible to calculate differences in free 

energies, entropies, and enthalpies on 

changing a ligand binding to a protein 

or on mutating an active site; these 

methods—pioneered by Arieh Warshel, 

Andrew McCammon, and William Jorgensen in the early 1980s—have been 

used in the initial stages of the design of 

drugs currently on the market. There is 

real promise—with improved force fields 

and increased computational power— 

that free energy calculations will play an 

increasingly important role in the design 

of drugs in the long term. Simulations 

can be used to suggest 
60 61

novel mechanisms or hypotheses about 

complex processes. Another of the wonderful features of MD is that one can 

calculate so many different things from a 

single simulation. For example, because 

of the weak coupling between experimental radiation probes (X-rays, neutrons, microwaves, etc.) and molecular 

systems, one doesn’t have to explicitly 

include the probes in the simulation 

itself. That means that one can calculate, 

using correlation functions, many different scattering and spectroscopic quantities from a single simulation. Simulations therefore play a role in unifying 

different experimental observations in a 

single self-consistent physical model. 

Methodologies improving simulation 

accuracy and speed have proliferated. 

Conformational sampling, the difficulty 

of which increases exponentially with 

chain length, provides grist for the 

theoretician’s mill. Techniques such as 

simulated annealing (in the 1980s) and 

parallel tempering and adaptive biasing 

(nowadays) were adopted. Furthermore, 

the massive increase in computer power 

since the 1970s and the development of a 

new generation of highly performing 

simulation programs such as NAMD 

(Phillips et al., 2005) have enabled the 

useful atomic-detail simulation of large 

proteins and protein complexes, protein: 

DNA interactions, membranes, receptors, 

and ion channels. The outer MD limits, 

which were a few picoseconds for ~100 

atoms in 1975, are now about one microsecond for 100 million atoms on a highlyparallel supercomputer (Figure 1B), and 

when the exascale of computing power 

is reached, we will, in principle, have the 

capacity to simulate a whole living cell at 

atomic detail. Work will also be performed sitting on the Cloud. The special 

purposebuilt Anton supercomputer 

(Shaw et al., 2009) has extended MD 

capabilities to a millisecond, and thus a 

further raft of atomic detail biological 

phenomena moves into view. In another 

form of multiscale, coarse-graining 

potentials allow further spatiotemporal 

extension, and feelers are being extended to systems biology tools such as 

metabolic network and cell-compartment simulations. 

A profound mechanistic understanding 

of biomolecular systems will be recognized by our ability to make quantitatively accurate and reliable predictions of 

structure, dynamics, and function from 

computational models. While simplified 

‘‘toy’’ models and back-of-the-envelope 

theories have played and continue to 

play an important role in formulating 

new concepts or elaborating new strategies, there is an increasing need for the 

‘‘virtual reality’’ provided by simulations 

to quantitatively match, with some 

reasonable accuracy, what can happen 

in the real world. Thus, even if the simulations are not perfect, we would like to 

be sure that the ‘‘correct’’ answer falls 

reliably and predictably within some 

interval around the computational result. 

Achieving such reliability is critically 

important for consolidating the usefulness of molecular simulation in the 

biomedical sciences. Confidence that 

simulations provide true, genuine information about the system under study 

has important implications. For example, 

when the result of a computation does 

not match some experimental measurement, then one should be able to conclude that it is not the calculation but 

some underlying hypothesis about the 

system that is wrong. This situation is 

similar to the case in which two types of 

experiments, e.g., solution NMR and 

X-ray crystallography, appear to disagree 

with each other. In this case, one does 

not immediately conclude that one of 

them is ‘‘wrong,’’ but rather that the 

structure in solution must be different 

than that in the crystalline form. This 

becomes possible when one knows that 

the computation, while not perfect, has 

systematic reliability in accomplishing a 

specific task. Confidence in the general 

correctness of simulations does exist for 

some types of calculations for which 

there have been extensive previous 

experience and validation. In those cases, 

the results of computer simulations can 

already be used to unequivocally confirm or refute specific hypotheses that 

resist other modes of investigation. 

But the field of simulations is still rough 

around the edges, a work in progress. 

Simulation inaccuracies have dogged for 

decades the relationship between simulation and experiment in structural 

biology. As a result, the field has been 

plagued by a distrust of predictions. 

Whereas an elegantly constructed theory 

in physics often triggers interest prior to 

experimental testing, wariness of theory 

is prevalent in biology. Evolution away 

from this situation has been a very slow 

process. As noted by Karplus in his 

autobiographical review (Karplus, 2006), 

if a theory agrees with experiment, it is 

not interesting because the result is 

already known, whereas if one is making 

a prediction, then it is not publishable 

because there is no experimental evidence that the prediction is correct. The 

mindset that must be adopted to achieve 

systematic reliability, so that prediction 

can stand on its own two feet, is akin to 

sculpture or engineering, requiring 

systematic efforts at chipping away, 

cleaning, and polishing. We must improve force fields, establish standardized 

best practices in simulation methodology and free energy computations, 

develop effective sampling strategies, etc.

Perhaps, though, the singular ability of 

numerical simulation to furnish a firm 

energetic and thermodynamic foundation for the formation and functional use 

of three-dimensional structure meant 

that it was inevitable that this field would 

slowly but surely take a hold in molecular biophysics. At any rate, there’s no 

turning back. Computations are at the 

forefront of modern-day 

scientific planning, and simulation is 

now firmly established as the third pillar 

of science, linking experiment to theory 

for complex systems resisting the back 

of an envelope calculations. For all their 

infuriating aspects, maybe accurate 

computer simulations are indeed the 

only way to unlock a deep understanding of how a biological system works.

References

Hagler, A.T., and Honig, B. (1978). Proc. 

Natl. Acad. Sci. USA 75, 554–558. 

Karplus, M. (2006). Annu. Rev. Biophys. 

Biomol. Struct. 35, 1–47. 

Lee, C., and Levitt, M. (1991). Nature 352, 

448–451. 

Levitt, M., and Lifson, S. (1969). J. Mol. 

Biol. 46, 269–279. 

McCammon, J.A., Gelin, B.R., and Karplus, M. (1977). Nature 267, 585–590. 

Koehl, P., and Levitt, M. (1999). Nat. Struct. 

Biol. 6, 108–111. 

Levitt, M., and Warshel, A. (1975). Nature 

253, 694–698. 

Phillips, J.C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, 

C., Skeel, R.D., Kalé, L., and Schulten, K. 

(2005). J. Comput. Chem. 26, 1781–1802. 

Rahman, A. (1964). Phys. Rev. A 136, 

405–411. 

Shaw, D.E., Dror, R.O., Salmon, J.K., 

Grossman, J.P., Mackenzie, K.M., Bank, 

J.A., Young, C., Deneroff, M.M., Batson, 

B., Bowers, K.J., et al. (2009). MillisecondScale Molecular Dynamics Simulations 

on Anton. Proceedings of the Conference on High Performance Computing, 

Networking, Storage and Analysis (SC09) 

(ACM Press). 

van Gunsteren, W.F., and Mark, A.E. 

(1992). J. Mol. Biol. 227, 389–395. 

Warshel, A., and Karplus, M. (1972). J. Am. 

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Warshel, A., and Levitt, M. (1976). J. Mol. 

Biol. 103, 227–249
How does a physicist survive in 

a biology department?

Shen: That is a tough question, and 

I am still figuring it out. You need so 

many things to be just right, such as 

luck and curiosity, and to ask the correct type of question that a physicist 

can answer. Physics always focuses on 

pure, ideal, and neat problems, while 

biological systems are much more 

complex. You can ask a lot of questions 

in biology and get a thousand different answers, but you don’t know which 

one is correct.

How does one overcome the inevitable communications problems that must exist between 

biologists and physicists?

Shen: I don’t see this as a huge problem. You just have to be open-minded, 

patient, and find common ground by 

using simple terms to explain what 

you mean. It is very rewarding to learn 

from for researchers from other fields 

and everybody is intrinsically curious.

Many experts think that the 

most innovative research is that 

which crosses disciplines. Why 

do you think this is so?

Shen: Not sure. But, assuming that 

each field has 10 good and trendy ideas, 

three fields may have 20 good ideas. If 

you cross several fields, you may be familiar with a lot more ideas than if you 

only know what is going on in your 

field alone. And of course 20 ideas can 

be a lot better than 10 ideas in solving a 

particular problem.

Tell us about your research on 

cellulose.

Shen: We are currently focused on 

understanding the structural stability 

of different phases of cellulose. I am 

surprised that not a lot more physical 

science researchers study this – it is 

really a very interesting problem. Just 

like ice having multiple phases due to 

extensive hydrogen bonding possibilities, polysaccharides also have a lot of 

bonding opportunities intramolecuHow does a Physicist Survive 

in a Biology Department?

Tongye Shen is an Associate Professor 

in the UT Department of Biochemistry, 

Cellular and Molecular Biology. However, he looks at biological phenomena from a very physical standpoint, 

and his training differs substantially 

from that of most other faculty in the 

department.

larly and with their neighbors. Cellulose has many polymorphs as a result. 

To figure out how cellulose can transform from one form to another is very 

interesting. Postdoctoral researcher 

Xianghong “Hanna” Qi, a physicist and 

self-proclaimed expert on everything 

(Editorial note: said with a big grin), 

uses statistical physics to study this 

problem. We also examine the stochastic dynamics of the cellulose degradation by enzymes. This system is an 

excellent bridge to many other biological problems.

Why are lectins interesting and 

what have you discovered about 

them?

Shen: My early work was 100% on 

proteins, and then I moved on to study 

polysaccharides such as cellulose. So 

lectins are kind of a natural follow-up. 

Lectins have a bit of both, how proteins 

interact with sugar. With postdoctoral 

fellow Ricky Nellas, who is a chemist, 

and expert on nucleation theory, we 

are starting to look at how proteins effectively recognize sugars. This work is 

of importance in biology. In particular, 

we look at the cooperativity of proteinsugar recognition. That is, often lectins have multiple binding sites. This 

common feature indicates a certain 

enhancement of signaling. Cooperative binding will give an amplification 

effect on recognition.

You are adept at analytical physical theory. Are you working on 

applying analytical techniques 

to understanding biological systems at the moment? 

Shen: I like analytical models, probably 

because my first study area was the 

quantum mechanics of the excitation 

of heavy elements. For biological systems, analytical models can be tough 

for many reasons and I don’t particularly want to force it. I focus on the 

physical results and often have to settle 

for numerical results. Right now, we do 

have some very small analytical models: one for active assembly of swimming cells, another for the degradation 

of biopolymer chains. More often one 

cannot find many analytically solvable 

problems in biology, but more likely 

there is a specific condition of a system preventing solution. But analysis 

can nevertheless be useful in several 

ways. For example, say we use simulation/numerical methods to study F(x), 

with x in [0, 1]. Now we cannot solve it 

generally, but if we use an independent 

analytical method to get the solution at 

the end points, of F(0) and F(1), at least 

we get a sense of whether our numerical results are correct, and at those limiting cases, of what is happening.

62 63
ORNL over the Decades

CMB functions smoothly due in large 

part to our Administrative Assistants. 

Julia Cooper was CMB’s first admin 

from 2006 to 2013. She worked at 

ORNL for nearly four decades since 

the 1960s and we found it interesting 

to hear her thoughts on life at the lab 

in earlier times.

Past years at ORNL were very different 

from the present. Union Carbide had 

the contract from the late 1940s until 

1984. Other contractors before UTBattelle were Lockheed Martin and 

Martin Marietta.

Before September 2001, Bethel Valley 

Road was open to all cars without 

restriction and there were no guard 

gates at either end as there are now. 

There was an approximately 10-foot 

fence with barbed wire on the top 

around the entire perimeter of the 

laboratory and guards were at each 

main entrance only for certain hours 

of the day, usually during regular 

working hours. Cars were required 

to be backed into the angled parking 

space – no pulling forward into it. 

The management structure was 

much different. There were Group 

Leaders, Section Heads and Division 

Directors for each division. Section 

heads did general supervision while 

division directors got the programs 

from DOE. There were more big 

programs in those days like Coal 

Conversion, and the Biology Division 

was internationally known for their 

genetics work. The big programs drew 

together a cross-section of expertise 

from across the whole lab. We had 

more immediate presence of the craft 

people, but also more support for the 

experimental scientists in the form 

of technicians. There are probably 

fewer technicians now since benchtype science has been replaced with 

more computer-based research.

People with seniority were generally 

Julia Cooper

left alone to do their work, but there 

also was perhaps less openness. 

Safety has always been emphasized, 

but was not as micro-managed in 

years past. The complexity of lab 

regulations has grown considerably, 

which in some ways made things 

easier administratively. We didn’t use 

computers in the same ways we do 

now.

Women were regarded very differently 

in those days – there was less equality. 

The lab even had a “Miss ORNL” at 

some point – possibly in the early 60s! 

I’m not sure how she was chosen or 

the years this took place. Also, life in 

general was more formal. Both men 

and women wore more “dress” clothes 

to work – women often wore heels and 

men typically wore coats and ties. Very 

few wore jeans or casual attire. The 

nurses in medical wore starched white 

uniforms – all provided by ORNL! 

Bethel valley before ORNL

The old ORNL visitors center, with cars 

parked, presciently, “en battelle”.

Nurses sitting around a table with starched 

white uniforms

64 65
Jeremy growing young undergraduate scientists (actually, he admits, 

most of the cultivation is done by the graduate 

students and postdocs)

Welcome to the Real World

UNDERGRADS IN CMB

CMB has had dozens of more-or-less ephemeral undergraduate researchers 

working together with the graduated researchers. Some seem to visit us for a few 

hours then go AWOL, whereas other form working relationships with us that last 

years. We asked them for their views on working in research.

Why do you like science?

Kate Armstrong: Science is amazing! 

It’s the study of the world and how 

things work, put simply. How could 

that not be interesting? Plus, I really 

like to wear lab goggles. I chose a 

scientific career path so I don’t have to 

explain that to people. 

Brian Simon: My entire life I’ve 

wondered how and why things are the 

way they are. Science either provides 

the answers for these questions or 

enables me to find the answers myself.

Madison Leonard: I find comfort 

in the objectivity of science and its 

perpetual relevance in the evolution of 

literally everything.

Jordan Finley: I love how science ties 

into the real world and the challenge 

it presents when trying to understand 

the unknowns. Additionally, I like how 

it relates to bodily functions and how 

it can create cures for diseases.

Megan Landon: Through science I can 

learn about the principles underlying 

everyday phenomena and uncovering 

those principles is always fascinating. 

Bailey Humphreys: Science can 

provide an understanding of 

everything around us. I like how that 

information can be used to answer 

natural curiosities and create solutions 

for numerous problems.

After all those years of studystudy-study, what does it feel 

like to be doing real research? 

Kate: It’s really fun! Except sometimes 

it makes me cry.

Brian: It feels like the light at the 

end of the tunnel. It’s completely 

refreshing to be applying some of the 

knowledge I spent so much time and 

effort gaining.

Madison: There’s more pressure to 

become independent, along with the 

daunting realization that you have to 

rely on everything you’ve ever learned 

ever.

Jordan: It feels like all those years 

of studying finally are paying off. 

It is rewarding to be able to apply 

my knowledge to certain problems 

presented in science and having the 

potential to help solve some of those 

problems.

Megan: It’s nice to be able to apply 

the concepts I’ve been studying in 

my classes. Using those concepts also 

makes it easier to better understand 

them.

Bailey: I like being able to apply what I 

have learned in school. However, I feel 

like there is even more to study now 

than before.

Do taking exams and writing 

essays etc really prepare you for 

research?

Kate: Exam preparation helps you 

understand what you don’t know. 

The skills for pursuing a better 

understanding of coursework are 

essential to doing research well. But 

caffeine prepares you better.

66 67
The fruits of our supervisory labor!

Brian: I wouldn’t say exams prepare 

you for research because all of the 

information you need is at your 

fingertips. Some papers have helped 

because they included analytical and 

explanatory sections. They also help 

acclimate you to documenting your 

progress, which I consider important 

to any research process.

Madison: Research allows creative 

license to learn more about and 

explore a desired field, but the 

knowledge gained from extensive 

exam preparation and papers become 

necessary for a successful foundation 

in the pursuance of any science. 

Jordan: I do not believe that taking 

exams truly helped me in preparing 

me for research because exams are so 

much pressure and stress. I do think 

that some of my scientific essays 

assisted me in my preparation for 

research.

Megan: Taking exams and writing 

essays are both necessary for 

forming a foundational knowledge 

of the principles underlying research. 

Additionally, they help develop 

necessary problem solving and 

communication skills. They provide a 

good foundation to start research put 

they don’t completely prepare you.

Bailey: Research requires you to read 

and understand new material quickly. 

I think preparing for exams and 

writing essays builds a foundation of 

knowledge while teaching you how to 

learn. 

CMB is computational. What’s 

your view of the usefulness of 

computational science?

Kate: The usefulness of computational 

science correlates directly to its use in 

computational science and, strangely, 

donuts in the breakroom.

Brian: It’s completely necessary for 

scientific analysis and research. Unless 

you’d like to do all of that math by 

hand.

Madison: Computational science 

is an unparalleled method of 

research because the complexity of 

mathematical algorithms running 

simulations are not limited by the less 

efficient analytical approaches of man.

Jordan: I cannot believe that a 

computer is capable of such scientific 

analysis. Computational science makes 

research more enjoyable and easier to 

understand because most of the leg 

work is done for you. 

Megan: Computational science is 

incredibly useful for processing 

and understanding large amounts 

of data. It can overcome some of 

the limitations of in vitro or in vivo 

methods and can cut the costs 

associated with some areas of research 

like the drug design process. 

Bailey: Computational science allows 

us to conduct experiments that are 

impossible to do in a conventional 

lab. It opens up an entire new area for 

research while supporting others.

What are your plans for the 

future? Will they include 

research?

Kate: Plan A: I’ll probably pursue my 

master’s degree and then specialize 

in medicine or pharmaceuticals. 

Ideally, I’ll try going the route of MDPhD, though those programs are very 

competitive. Plan B: Vacationing 

in Switzerland indefinitely (I’m still 

working out the logistics...)

Brian: I’m applying to medical schools 

soon so I hope to attend one next year. 

I’m sure at some point in my future 

I’ll do research whether it’s medical in 

nature or otherwise.

Madison: After watching the TV show 

Scrubs, I was 100% reaching toward 

medical school. However, research 

has given me a new perspective on 

different paths I could take, so I am 

currently at 87%.

Jordan: My plans for the future are 

to apply to medical school. I’m sure 

one day I will be able to apply some 

of the knowledge I have gained 

during undergraduate research to my 

future medical research or any other 

scientific career path I decide to take. 

Megan: I hope to attend medical 

school after I complete my 

undergraduate degree. I do plan on 

continuing research in some form as a 

doctor whether it be clinical research 

or pursuing a dual MD/PhD program.

Bailey: I plan to attend graduate school 

once I finish my undergraduate degree. 

I am unsure what I will pursue after 

that, but I am confident it will include 

research.. 

68 69
Five Germans in Tennessee

When Jeremy Smith left Heidelberg to come to Tennessee in 2006 he managed 

to persuade five German students to follow him and register for a Ph.D. at UT. All 

five spent several years in East Tennessee and feel they even have a slight Southern 

accent to their English. We asked them to compare life in Knoxville to that in the 

rarified academia of Germany’s oldest university.

How’s life in Tennessee? 

Barmak: Pretty laid-back. I have to 

say my time here has helped me to 

become much more relaxed about 

many things in life. 

Benjamin: I like it. Being able to go 

shopping at 3 a.m. really fits the life 

of a scientist. Cars are essential – 

something you have to get used to 

– especially if you come from Europe, 

where public transit is very popular.

Dennis: It’s different in many small 

aspects, for example, the status of 

university sports, which are fun to 

discover.

Coming from far far away (left to right):

Barmak Mostofian, Benjamin Lindner, Dennis Glass, Roland Schulz, Xiaohu Hu

Roland: I like it. The nature is beautiful 

and the people are very nice. The main 

thing I don’t like is the urban sprawl.

Xiaohu: Nice warm weather – life is 

less hectic in general, nice people... 

and great BBQ! Oh my God is that 

good!

How would you compare the 

atmosphere at UT compared to 

the University of Heidelberg?

Dennis: I think undergraduate 

students in Heidelberg need to be 

more autonomous in planning 

their degree and workload, as the 

German system is quite flexible and 

formally wants students to get a broad 

education. Here, students benefit from 

well-designed degree paths and thus 

can give science a larger focus. 

Benjamin: The differences are very 

subtle. Both are gigantic institutes 

where the quality of the classes can 

vary significantly. A main difference is 

the timing of the semesters. UT allows 

you to enjoy Christmas because the fall 

semester ends before Christmas takes 

place.

Barmak: There are cultural differences 

as well. I think the University of 

Heidelberg has one of the largest 

medical centers in Germany while 

Knoxville has one of the largest football 

stadiums in the U.S. They name their 

department buildings after famous 

scientists in Heidelberg, while in 

Tennessee they are named after 

famous football players.

Xiaohu: In Heidelberg, we didn’t care 

much about University sports events, 

but here everybody is crazy about UT’s 

football team. It was quite an amazing 

phenomenon for me at the beginning.

Roland: In Heidelberg the learning 

process is less structured, e.g. 

attendance is often not required and 

for a PhD few classes are mandatory. 

UT offers more help with non-subject 

skill development such as grant 

writing.

Are the students the same?

Benjamin: At UT you see more 

international students. The students 

themselves are not that different. 

Roland: The German school system 

causes Heidelberg students to be on 

average a bit older but better prepared 

in Math.

Xiaohu: I’d say yes. People are working 

hard to get their degrees similar to 

Heidelberg.

Has Jeremy become a little 

less snobbish since leaving 

the German “Herr Professor” 

establishment?

Benjamin: I never had the feeling that 

Jeremy was snobbish. Otherwise I 

wouldn’t be here. 

Dennis: I think Jeremy was always 

“Jeremy” and never “Herr Professor”, 

and he still is “Jeremy” in Tennessee. 

70 71
Roland: I think that the majority 

of professors in Germany are not 

snobbish. This included Jeremy when 

he was still there.

Xiaohu: “Herr Professor”? I don’t 

think these words have ever been in 

Jeremy’s vocabulary. Besides, these 

words would make our young and 

dynamic Jeremy sounds he was 75+ 

and driving a motorized wheelchair 

to work rather than a BMW M3 

convertible (Editorial note: it was 

actually a 3-series convertible, not an 

M3), which is envied by all of us poor 

graduate students, but at the same 

time, also motivates us to work hard to 

become like Jeremy in the future. No, 

we have never called him anything 

else but Jeremy... Wait, what is his last 

name again? Jeremy Schmidt? :)

What’s the verdict on outdoor 

activities in Tennessee?

Barmak: Hiking, biking, climbing, 

rafting, canoeing, boating, camping 

– you name it! If it wasn’t for the 

Smokies, I’ve heard, we all would have 

been much more productive at work.

Benjamin: Not good for skiing though 

– also the climate can be tough 

sometimes. 

Dennis: You can go hiking and 

actually see wildlife, unlike in 

Germany where the only “wildlife” that 

will cross your way is mostly other 

hikers. Be aware if they just left a 

“Hütte“ that served alcoholic beverages, 

they might literally cross your way.

Roland: The large number of trails, 

both for hiking and mountain-biking, 

is very nice. In Heidelberg mountainbikers have to share trails with hikers 

and are hated by most of them. The 

Smokies are very beautiful. Particularly 

amazing are the synchronized fireflies.

Xiaohu: Well, there are some 

similarities between Heidelberg and 

Knoxville in this aspect: near Knoxville, 

there is the Great Smoky Mountains 

and near Heidelberg, there is the 

Odenwald (Oden-forest). Both are 

good for hiking, but I think there is 

clearly more wildlife in the Smokies. I 

don’t think anyone has seen wild bears 

in the Odenwald before.

What’s the difference between 

science in Germany and the 

USA?

Benjamin: The leadership feeling. 

Great science takes place in both 

locations, but it’s like comparing 

the NBA with the German basketball 

league. Incidentally, a current NBA 

superstar is German: Dirk Nowitzki. 

Dennis: I can just speak for the 

graduate student part of science – 

Here I prefer the German system 

where you start graduate school on 

a level similar to the Master’s degree, 

have no obligatory coursework, and 

can focus right on your research 

(instead of course-work).

Roland: The three difference I 

find most striking are: 1) Scientific 

Computing receives more attention 

and funding than in Germany. E.g. 

51% of the TOP500 and 50% of the 

TOP10 of supercomputers are in the 

US versus 6% of TOP500 and 0% of 

TOP10 for Germany. The current plans 

in the US for Exascale computing are 

far ahead of plans in Germany. 2) The 

funding of research groups is much 

more reliant on external funding by 

research grants than it is in Germany; 

thus the importance of grant writing is 

much higher in the US. 3) For science 

education a striking difference is the 

acceptance of some scientific theories. 

Having volunteered for the UTK 

Darwin Day I have experienced some 

of the challenges evolution education 

faces in the US. Also climate science 

is much more political than it is in 

Germany whereas the opposite is true 

for food biotechnology and nuclear 

research.

Xiaohu: In principle, not really 

different, working hard and producing 

good publications.

Will Juergen Klinsmann 

succeed as coach of Soccer 

Team USA?

Barmak: Haha, I guess he will be just 

fine. In fact, I would say if there is 

one team that would not lose against 

Germany in the 2014 World Cup, it will 

be Team USA . (Ed. note: Actually, they 

did play and Germany won with 1:0)

Benjamin - You were a topclass wrestler back in Germany, 

and even competed for the 

national team. Does the US 

pull its weight in international 

wrestling?

Benjamin: As a matter of fact I don’t 

know. Even though I was a successful 

wrestler I never really cared about the 

who’s who in wrestling. I wrestled 

because that is what I loved to do. Now 

it’s computational science.

Which is the liveliest: the 

Cumberland Strip or the 

Heidelberg Hauptstraße?

Dennis: Heidelberg what? I’ve been 

here for a while and hardly remember 

a previous life... 

Jeremy infinitely prefers 

bluegrass music to German 

“Volksmusik”. Do you agree?

Xiaohu: Oh yes, definitely! Compared 

to other music, Volksmusik is not 

nearly as enjoyable as is a Volkswagen 

compared to other automobiles.

72 73

Former CMB post-doc Liang Hong in a dis-

cussion with the then visiting Ph.D. student 

Mai Zahran from Jeremy’s lab in the Univer-

sity Heidelberg, 2011. Liang is now a profes-

sor at Shanghai Jiaotong University and Mai a 

professor at New York City College of Tech-

nology.
Question: What sparked your interest 

to pursue a career in science?

Jeremy Smith: In England in the 1970s 

one had to specialize early, very early 

-- at 16. For me it could have gone either 

way, arts or sciences. To be honest I 

wasn’t very interested in science at that 

time. I was never a geeky, gadget-type 

kid, although scientific concepts did 

interest me. My high school teachers 

advised science as having safer career 

prospects than arts subjects, so from 17 

on that’s all I did. Later in high school I 

became interested in protein structures 

and how atoms interact. I sometimes 

wonder what would have happened if 

I’d chosen the other way at 16...

Q: As the Director of the Center for 

Molecular Biophysics, your work spans 

across a multitude of fields. Can you 

tell us a little about your research background -- what led you to this unique 

position?

JS: My first degree was in biophysics at 

Leeds, England. After that, I did a Ph.D. 

in neutron scattering in France, a post 

doc in chemistry at Harvard, and then 

ran my first group at the French National Lab in Saclay. Before coming to 

Tennessee I held the Chair of Computational Molecular Biophysics at the University of Heidelberg in Germany. Yes, 

our work involves theoretical physics, 

quantum chemistry, statistical mechanics, computer science, supercomputing, 

catalytic chemistry, polymer science, 

biochemistry, molecular biology -- you 

name it!

I find it difficult to not get enthusiastic 

about a crisp new idea in molecular science and how we might help develop it.

Q: What projects are you working on 

right now? What do you hope they will 

lead to?

JS: We’re working on many different 

projects. Some of these include: cellulosic biofuels, which we hope will lead to 

cheap alternative energy, drug discovery towards curing prostate cancer and 

mercury biogeochemistry to understand 

the fate of mercury in the environment. 

We’re also working on describing the 

structure and dynamics of biological 

materials through neutron scattering. 

With regards to supercomputing, those 

The article below was posted on the energy blog of the Department of Energy.

10 Questions for a Biophysicist: 

Jeremy Smith

Source: energy.gov/articles/10-questions-biophysicist-jeremy-smith

In 2006, Dr. Smith came to Oak Ridge National Laboratory (ORNL). Since then, he 

has led a wide-ranging spectrum of projects focusing on everything from biofuels to 

drug discovery. He recently gave us the download on his many projects.

Dr Jeremy Smith | Photo Courtesy of ORNL

cheeky hardware guys keep building 

more and more powerful machines and 

challenging us to perform cutting-edge 

simulations that efficiently use their full 

capability.

Q: You are also a professor at University 

of Tennessee -- do you have any advice 

for students interested in science?

JS: Yes. Learn to write well -- too many 

youngsters can do science but not precisely express their thoughts and findings. Furthermore, don’t forget to lead 

a balanced, active, fun life -- it will help 

the scientific part.

Q: What classes do you teach? What 

have your students taught you?

JS: I teach an introduction to molecular 

biophysics, a journal club and our group 

meetings. My co-workers and students 

come up with all the crazy ideas and 

then do all the work -- they’re sickeningly bright and inexhaustibly hardworking.

Q: What can you never start a day at the 

lab without?

JS: I like to start the day finding a new 

research manuscript on my desk that 

a co-worker has left for me, preferably 

with a cookie on top.

Q: Do you have a favorite fictional scientist?

JS: Yes, Gromit. He remembered to take 

the handbrake off his rocket.

Q: We heard that you are an avid soccer fan and player -- having lived in 

England, France, Germany and now the 

United States, do you have a favorite for 

the next World Cup?

JS: Concerning playing soccer -- my 

79-year-old father still plays ninety 

minute games so I can’t possibly give up 

playing until he does, can I?

I’m the equivalent of a Cubs fan. I support Norwich City, a team in England 

apparently consigned to perennial failure, except of course, maybe, this year 

(hope springs eternal)! As for the World 

Cup, supporting England is too painful so I’ll just say anyone but Germany, 

please.

Q: What is it like to work in ORNL’s 

Spallation Neutron Source (SNS) facility?

JS: Well, the SNS brings together scientists from many different fields. It’s 

sometimes difficult for me to understand what someone from, for example, 

magnetism is working on but the diversity of backgrounds leads to fertile 

discussion. It will take a while before 

SNS achieves full science productivity 

and then a couple more years until the 

results obtained have their full effects 

on the scientific community but we’re 

getting there.

Q: Last question -- why is neutron scattering research important?

JS: Neutrons give direct, simultaneous 

information on molecular structure and 

dynamics and no other probe of matter does this. This should help us design 

new materials in the energy sciences, 

and understand important topics in bioenergy and biology. For example, we recently demonstrated with neutrons how 

a cancer drug, methotrexate, softens the 

target it binds to -- that’s fundamental 

understanding of how drugs work.

74 75
CMB Publications

2006

E Balog, JC Smith and D Perahia. Conformational heterogeneity and low frequency vibrational 

modes of proteins. Physical Chemistry Chemical Physics, 8:5543–5548, 2006.

J Baudry. Van der waals interactions and decrease of the rotational barrier of methyl-size rotators: 

A theoretical study. Journal of the American Chemical Society, 128(34):11088–11093, 2006.

N Bondar, JC Smith and S Fischer. Structural and energetic determinants of proton transfer in 

bacteriorhodopsin. Photochemical and Photobiological Sciences, 5:547–552, 2006.

T Efferth, SM Schwarzl, JC Smith and R Osieka. Role of glucose-6-phosphate dehydrogenase for 

oxidative stress and apoptosis. Cell Death and Differentiation, 13:527–528, 2006.

H Guo, A Wlodawer, T Nakayama, Q Xu and H Guo. Catalytic role of proton transfers in the formation of a tetrahedral adduct in a serine carboxyl peptidase. Biochemistry, 45(30):9129–9137, 2006.

P Imhof, F Noé, S Fischer and JC Smith. Am1/d parameters for magnesium in metalloenzymes. 

Journal of Chemical Theory and Computation, 2(4):1050–1056, 2006.

S Koppole, JC Smith and S Fischer. Simulations of the myosin ii motor reveal a nucleotide-state 

sensing element that controls the recovery stroke. Journal of Molecular Biology, 361(3):604–616, 

2006.

V Kurkal-Siebert, R Daniel, JL Finney, M Tehei, RV Dunn and JC Smith. Enzyme hydration, activity 

and flexibility: A neutron scattering approach. Journal of Noncrystalline Solids, 352(42-49):4387–

4393, 2006.

V Kurkal-Siebert and JC Smith. Low-temperature protein physics: A simulation analysis of interprotein vibrations and the boson peak at 150k. Journal of the American Chemical Society, 

128(7):2356–2364, 2006.

S Mesentean, S Fischer and JC Smith. Analyzing large-scale structural change in proteins: Comparison of principal component projection and Sammon mapping. Proteins: Structure, Function 

and Bioinformatics, 64(1):210–218, 2006.

K Moritsugu and JC Smith. Temperature-dependent protein dynamics: A simulation-based 

probabilistic diffusion-vibration Langevin description. The Journal of Physical Chemistry B, 

110(11):5807–5816, 2006.

F Noé, D Krachtus, JC Smith and S Fischer. Transition networks for the comprehensive characterization of complex conformational change in proteins. Journal of Chemical Theory and Computation, 2(3):840–857, 2006.

F Noé, M Oswald, G Reinelt, S Fischer and JC Smith. Computing best transition pathways in highdimensional dynamical systems: Application to the αl ↔ β ↔ αr transitions in octaalanine. Journal 

of Multiscale Modelling and Simulation, 5(2):393–419, 2006.

SM Schwarzl, JC Smith and S Fischer. Insights into the chemomechanical coupling of the myosin 

motor from simulation of its ATP hydrolysis mechanism. Biochemistry, 45(18):5830–5847, 2006.

JC Smith. Tight in titin. Structure, 14(3):389–390, 2006.

JC Smith, T Becker, S Fischer, F Noé, AL Tournier, GM Ullmann and V Kurkal. Physical and Functional Aspects of Protein Dynamics, chapter 12, pages 225–241. Taylor & Francis, 2006.

M Tehei, JC Smith, C Monk, J Ollivier, M Ottl, V Kurkal, JL Finney and R. M Daniel. Dynamics of 

immobilized and native Escherichia Coli dihydrofolate reductase by quasieleastic neutron scattering. Biophysical Journal, 90(3):1090– 1097, 2006.

L Walewski, D Krachtus, S Fischer, JC Smith, P Bala and B Lesyng. SCC-DFTB energy barriers for 

single and double proton transfer processes in the model molecular systems malonaldehyde and 

porphycene. International Journal of Quantum Chemistry, 106(3):636–640, 2006.

Xu, H-B Guo, A Wlodawer and H Guo. The importance of dynamics in substrate-assisted catalysis 

and specificity. Journal of the American Chemical Society, 128(18):5994–5995, 2006.

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2007

A-N Bondar, S Suhai, S Fischer, JC Smith and M Elstner. Suppression of the back proton-transfer 

from asp85 to the retinal Schiff base in bacteriorhodopsin: A theoretical analysis of structural elements. Journal of Structural Biology, 157(3):454–469, 2007.

A-N Bondar, S Suhai, JC Smith and PT Frangopol. Computer simulations of local anesthetic mechanisms: Quantum chemical investigation of procaine. Romanian Reports in Physics, 59(2):289–

299, 2007.

Z Cournia, MG Ullmann and JC Smith. Differential effects of cholesterol, ergosterol and lanosterol 

on a dipalmitoyl phosphatidylcholine membrane: A molecular dynamics simulation study. Journal of Physical Chemistry B, 111(7):1786–1801, 2007.

I Daidone, MB Ulmschneider, A Di Nola, A Amadei and JC Smith. Dehydration-driven solvent exposure of hydrophobic surfaces as a driving force in peptide folding. Proceedings of the National 

Academy of Sciences USA, 104(39):15230–15235, 2007.

H-B Guo and H Guo. Mechanism of histone methylation catalyzed by protein lysine methyltransferase set7/9 and origin of product specificity. Proceedings of the National Academy of Sciences 

USA, 104(21):8797–8802, 2007.

X de Hatten, Z Cournia, I Huc, JC Smith and N Metzler-Nolte. Force-field development and molecular dynamics simulations of ferrocene-peptide conjugates as a scaffold for hydrogenase mimics. 

Chemistry - A European Journal, 13(29):8139– 8152, 2007.

S Koppole, JC Smith and S Fischer. The structural coupling between atpase activation and recovery stroke in the myosin ii motor. Structure, 15(7):825–837, 2007.

L Meinhold, F Merzel and JC Smith. Lattice dynamics of a protein crystal. Physical Review Letters, 

99(13):138101, 2007.

L Meinhold and JC Smith. Protein dynamics from x-ray crystallography: Anisotropic, global motion in diffuse scattering patterns. Proteins, 66(4):941–953, 2007.

L Meinhold, JC Smith, A Kitao and AH Zewail. Picosecond fluctuating protein energy landscape 

mapped by pressure temperature molecular dynamics simulation. Proceedings of the National 

Academy of Sciences USA, 104(44):17261–17265, 2007.

S Mesentean, S Koppole, JC Smith and S Fischer. The principal motions involved in the coupling 

mechanism of the recovery stroke of the myosin motor. Journal of Molecular Biology, 367(2):591–

602, 2007.

K Moritsugu and JC Smith. Coarse-grained biomolecular simulation with reach: Realistic extension algorithm via covariance hessian. Biophysical Journal, 93(10):3460–3469, 2007.

F Noé, I Horenko, C Schütte and JC Smith. Hierarchical analysis of conformational dynamics in biomolecules: Transition networks of metastable states. Journal of Chemical Physics, 

126(15):155102, 2007.

F Noé and JC Smith. Transition networks: A unifying theme for molecular simulation and computer science. In H. Byrne G. de Vries A. Deutsch, L. Brusch and H.-P. Herzel, editors, Mathematical 

Modeling of Biological Systems, Volume I, chapter 11, pages 121–137. Birkhäuser Boston, 2007.

DR Nutt and JC Smith. Choosing an appropriate water model for use in biomolecular simulations. 

In W. F. Kuhs, editor, Physics and Chemistry of Ice, pages 451–458. RSC Publishing, 2007.

DR Nutt and JC Smith. Molecular dynamics simulations of proteins: Can the explicit water model 

be varied? Journal of Chemical Theory and Computation, 3(4):1550–1560, 2007.

CM Topham and JC Smith. Orientation preferences of backbone secondary amide functional 

groups in peptide nucleic acid complexes: Quantum chemical calculations reveal an intrinsic 

preference of cationic d-amino acid-based chiral PNA analogues for the p-form. Biophysical Journal, 92(3):769–786, 2007.

Q Xu, H-B Guo, A Gorin and H Guo. Stabilization of a transition-state analogue at the active site 

of yeast cytosine deaminase: Importance of proton transfers. Journal of Physical Chemistry B, 

111(23):6501–6506, 2007.

Q Xu, H-B Guo, A Wlodawer, T Nakayama and H Guo. The QM/MM molecular dynamics and free 

energy simulations of the acylation reaction catalyzed by the serine-carboxyl peptidase kumamolisin-as. Biochemistry, 46(12):3784–3792, 2007.

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2008

V Becker, D Sengupta, R Ketteler, M. G Ullmann, JC Smith and U Klingmüller. Packing Density of 

the Erythropoietin Receptor Transmembrane Domain Correlates with Amplification of Biological 

Responses. Biochemistry, 47(45):11771– 11782, 2008.

A-N Bondar, J Baudry, S Suhai, S Fischer and JC Smith. Key Role of Active Site Water Molecules 

in Bacteriorhodopsin Proton-Transfer Reactions. Journal of Physical Chemistry B, 112(47):14729–

14741, 2008.

M Krishnan, V Kurkal-Siebert and JC Smith. Methyl Group Dynamics and the Onset of Anharmonicity in Myoglobin. Journal of Physical Chemistry B, 112(17):5522–5533, 2008.

V Kurkal-Siebert, R Agarwal and JC Smith. Hydration-Dependent Dynamical Transition in Protein: 

Protein Interactions at Approximately 240 K. Physical Review Letters, 100(13):138102, 2008.

SE McLain, A. K Soper, I Daidone, JC Smith and A Watts. Charge-Based Interactions between Peptides Observed as the Dominant Force for Association in Aqueous Solution. Angewandte Chemie 

International Edition, 120(47):9199–9202, 2008.

L Meinhold, D Clement, M Tehei, R Daniel, JL Finney and JC Smith. Protein Dynamics and Stability: The Distribution of Atomic Fluctuations in Thermophilic and Mesophilic Dihydrofolate Reductase Derived Using Elastic Incoherent Neutron Scattering. Biophysical Journal, 94(12):4812–4818, 

2008.

K Moritsugu and JC Smith. REACH Coarse-Grained Biomolecular Simulation: Transferability between Different Protein Structural Classes. Biophysical Journal, 95(4):1639–1648, 2008.

T Neusius, I Daidone, I. M Sokolov and JC Smith. Subdiffusion in Peptides Originates from the 

Fractal-Like Structure of Configuration Space. Physical Review Letters, 100(18):188103, 2008.

F Noé, I Daidone, JC Smith, A di Nola and A Amadei. Solvent Electrostriction Driven Peptide Folding Revealed by Quasi-Gaussian Entropy Theory and Molecular Dynamics Simulation. Journal of 

Physical Chemistry B, 112(35):11155–11163, 2008.

DR Nutt and JC Smith. Dual Function of the Hydration Layer around an Antifreeze Protein Revealed by Atomistic Molecular Dynamics Simulations. Journal of the American Chemical Society, 

130(39):13066–13073, 2008.

L Petridis and JC Smith. Cellulosic Ethanol: Progress towards a Simulation Model of Lignocellulosic Biomass. Journal of Physics: Conference Series, 125(1):012055, 2008.

D Sengupta, JC Smith and G. M Ullmann. Partitioning of Amino-Acid Analogues in a Five-Slab 

Membrane Model. Biochimica et Biophysica Acta, 1778(10):2234–2243, 2008.

K Voltz, J Trylska, V Tozzini, V Kurkal-Siebert, J Langowski and JC Smith. Coarse-Grained Force 

Field for the Nucleosome from Self-Consistent Multiscaling. Journal of Computational Chemistry, 

29(9):1429–1439, 2008.

2009

X Cheng, I Ivanov, HL Wang, SM Sine and JA McCammon. Moleculardynamics simulations 

of elic - a prokaryotic homologue of the nicotinic acetylcholine receptor. Biophysical Journal, 

96(11):4502–4513, 2009.

H-B Guo, A Gorin and H Guo. A peptide-linkage deletion procedure for estimate of energetic contributions of individual peptide groups in a complex environment: Application to parallel β-sheets. 

Interdisciplinary Sciences: Computational Life Sciences, 1(1):12–20, 2009.

K Moritsugu and JC Smith. Reach: A program for coarse-grained biomolecular simulation. Computer Physics Communications, 180(7):1188–1195, 2009.

R Schulz, M Krishnan, I Daidone and JC Smith. Instantaneous normal modes and the protein 

glass transition. Biophysical Journal, 96(2):476–484, 2009.

J Xu, M. F Crowley and JC Smith. Building a foundation for structure-based cellulosome design 

for cellulosic ethanol: Insight into cohesin-dockerin complexation from computer simulation. 

Protein Science, 18(5):949–959, 2009.

JM Parks, H Guo, C Momany, L Liang, SM Miller, AO Summers and JC Smith. Mechanism of 

Hg-C protonolysis in the organomercurial lyase MerB. Journal of the American Chemical Society, 

131(37):13278–13285, 2009.

L Petridis and JC Smith. A molecular mechanics force field for lignin. Journal of Computational 

Chemistry, 30(3):457–467, 2009.

T Splettstoesser, F Noé, T Oda and JC Smith. Nucleotide-dependence of G-actin conformation 

from multiple molecular dynamics simulations and observation of a putatively polymerization39

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competent superclosed state. Proteins: Structure, Function and Bioinformatics, 76(2):353–364, 

2009.

A-N Bondar and JC Smith. Water molecules in short- and long-distance proton transfer steps of 

bacteriorhodopsin proton pumping. Israel Journal of Chemistry, 49(2):155–161, 2009.

R Schulz, B Lindner, L Petridis and JC Smith. Scaling of multimillion-atom biological molecular 

dynamics simulation on a petascale supercomputer. Journal of Chemical Theory and Computation, 5(10):2798–2808, 2009.

M Krishnan and JC Smith. Response of small-scale, methyl rotors to protein-ligand association: 

A simulation analysis of calmodulin-peptide binding. Journal of the American Chemical Society, 

131(29):10083–10091, 2009.

A Spies, JG Reese, D Dodd, K. L Pankow, SR Blanke and J Baudry. Determinants of catalytic 

power and ligand binding in glutamate racemase. Journal of the American Chemical Society, 

131(14):5274–5284, 2009.

P Phatak, JS Fraemcke, M Wanko, M Hoffmann, P Strodel, JC Smith, S Suhai, A-N Bondar and M 

Elstner. Long-distance proton transfer with a break in the bacteriorhodopsin active site. Journal of 

the American Chemical Society, 131(20):7064–7078, 2009.

M Krishnan, R Schulz and JC Smith. Protein dynamical transition: Role of methyl dynamics and 

local diffusion. Theory and Applications of Computational Chemistry 2008. AIP Conference Proceedings, 1102:122–131, 2009.

P Imhof, S Fischer and JC Smith. Catalytic mechanism of DNA backbone cleavage by the restriction enzyme EcoRV: A quantum mechanical/molecular mechanical analysis. Biochemistry, 

48(38):9061–9075, 2009.

K Moritsugu, V Kurkal-Siebert and JC Smith. Reach coarse-grained normal mode analysis of protein dimer interaction dynamics. Biophyical Journal, 97(4):1158–1167, 2009.

T Neusius, I. M Sokolov and JC Smith. Subdiffusion in time-averaged, confined random walks. 

Physical Review E, 80:011109, 2009.

J. P Ulmschneider, JC Smith, A Killian and MB Ulmschneider. Peptide partitioning and folding 

into lipid bilayers. Journal of Chemical Theory and Computation, 5(9):2202–2205, 2009.

L Thukral, JC Smith and I Daidone. Common folding mechanism of a beta-hairpin peptide via 

non-native turn formation revealed by unbiased molecular dynamics simulations. Journal of the 

American Chemical Society, 131(50):18147–18152, 2009.

JC Smith. Supercomputers tackle biofuel production problems. SciDAC Review, (15):34–45, 2009.

Q Xu, Y Chu, H-B Guo, JC Smith and H Guo. Energy triplets for writing epigenetic marks: Insights 

from QM/MM free energy simulations of protein lysine methyltransferases. Chemistry: A European Journal, 15(46):12596–12599, 2009.

T Shen, P Langan, A French, GP Johnson and S Gnanakaran. Conformational flexibility of soluble 

cellulose oligomers: Chain length and temperature dependence. Journal of the American Chemical Society, 131(41):14786–14794, 2009.

Q Xu, X Ye, L-Y Li, Z-M Cheng and H Guo. Structural basis for the action of xyloglucan endotransglycosylases/hydrolases: Insights from homology modeling. Interdisciplinary Sciences: Computational Life Sciences, 2(2):133–139, 2009.

2010

B Lu, X Cheng, J Huang and J. A McCammon. AFMPB: An improved Poisson-Boltzmann solver using an adaptive fast multipole method. Computer Physics Communications, 181(6):1150–1160, 2010.

I Daidone, H Neuweiler, S Doose, M Sauer and JC Smith. Hydrogen-bond driven loop-closure kinetics in unfolded polypeptide chains. PLoS Computational Biology, 6(1):e1000645, 2010.

K Moritsugu, BM Njunda and JC Smith. Theory and normal mode analysis of change in protein 

vibrational dynamics on ligand binding. Journal of Physical Chemistry B, 114(3):1479–1485, 2010.

MB Ulmschneider, JPF Doux, JA Killian, JC Smith and JP Ulmschneider. Mechanism and kinetics of peptide partitioning into membranes from all-atom simulations of thermostable peptides. 

Journal of the American Chemical Society, 132(10):3452–3460, 2010.

A-N Bondar, JC Smith and M Elstner. Mechanism of a proton pump analyzed with computer simulations. Theoretical Chemistry Accounts, 125(3-6):353–363, 2010.

MB Ulmschneider, JC Smith and JP Ulmschneider. Peptide partitioning properties from direct 

insertion studies. Biophysical Journal, 98(12):L60–L62, 2010.

M Zahran, I Daidone, JC Smith and P Imhof. Mechanism of DNA recognition by the restriction 

enzyme EcoRV. Journal of Molecular Biology, 401(3):415–432, 2010.

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Y Guan, DRE Ranoa, S Jiang, SK Mutha, X Li, J Baudry and R. I Tapping. Human TLRs 10 and 1 

share common mechanisms of innate immune sensing but not signaling. The Journal of Immunology, 184(9):5094–5103, 2010.

H-B Guo, A Johs, JM Parks, L Oliff, SM Miller, A. O Summers, L Liang and JC Smith. Structure and 

conformational dynamics of the metalloregulator MerR upon binding of Hg(II). Journal of Molecular Biology, 398(4):555–568, 2010.

DC Glass, M Krishnan, D Nutt and JC Smith. Temperature dependence of protein dynamics simulated with three different water models. Journal of Chemical Theory and Computation, 6(4):1390–

1400, 2010.

S Yang, ML Land, DM Klingeman, DA Pelletier, T-Y. S Lu, SL Martin, H-B Guo, JC Smith and SD 

Brown. A paradigm for strain improvement identifies sodium acetate tolerance loci in Zymomonas Mobilis and Saccharomyces Cerevisiae. Proceedings of the National Academy of Sciences USA, 

107(23):10395–10400, 2010.

J Xu and JC Smith. Probing the mechanism of cellulosome attachment to the clostridium thermocellum cell surface: Computer simulation of the type II cohesindockerin complex and its variants. 

Protein Engineering, Design and Selection, 23(10):759–768, 2010.

M Lopez, V Kurkal-Siebert, R. V Dunn, M Tehei, J. L Finney, JC Smith and R. M Daniel. Activity 

and dynamics of an enzyme, pig liver esterase, in nearanhydrous conditions. Biophysical Journal, 

99(8):L62–L64, 2010.

T Berezniak, M Zahran, P Imhof, A Jaeschke and JC Smith. Magnesium-dependent active-site 

conformational selection in the Diels-Alderase ribozyme. Journal of the American Chemical Society, 132(36):12587–12596, 2010.

M Saharay, H Guo and JC Smith. Mechanism of cellulose degradation by a cellobiohydrolase, cels. 

PLos ONE, 5(10):e12947, 2010.

G Bellesia, A Asztalos, T Shen, P Langan, A Redondo and S Gnanakaran. In silico studies of crystalline cellulose and its degradation by enzymes. ACTA Crystallographica. Section D: Biological 

Crystallography, 66(11):1184–1188, 2010.

Q Johnson, U Doshi, T Shen and D Hamelberg. Water’s contribution to the energetic roughness 

from peptide dynamics. Journal of Chemical Theory and Computation, 6(9):2591–2597, 2010.

R. M Lynch, R Rong, B Li, T Shen, W Honnen, J Mulenga, S Allen, A Pinter, S Gnanakaran and C. A 

Derdeyn. Subtype-specific conservation of isoleucine 309 in the envelope V3 domain is linked to 

immune evasion in subtype C HIV-1 infection. Virology, 404(1):59–70, 2010.

Q Xu, L Li and H Guo. Understanding the mechanism of deacylation reaction catalyzed by the 

serine carboxyl peptidase kumamolisin-As: Insights from QM/MM free energy simulations. The 

Journal of Physical Chemistry B, 114(32):10594– 10600, 2010.

Y Chu, Q Xu and H Guo. Understanding energetic origins of product specificity of SET8 from QM/

MM free energy simulations: What causes the stop of methyl addition during histone lysine methylation? Journal of Chemical Theory and Computation, 6(4):1380–1389, 2010.

JM Parks, P Imhof and JC Smith. Understanding Enzyme Catalysis Using Computer Simulation, 

Encyclopedia of Catalysis, Wiley, 2nd edition, 2010. DOI: 10.1002/0471227617.eoc217

2011

A-N Bondar, S Fischer and JC Smith. Water pathways in the bacteriorhodopsin proton pump. 

Journal of Membrane Biology, 239(1-2):73–84, 2011.

T Neusius, I Daidone, I Sokolov and JC Smith. Configurational subdiffusion of peptides: A network 

study. Physical Review E, 83:021902, 2011.

Z Xu, W Cai and X Cheng. Image charge method for reaction fields in a hybrid ion-channel model. 

Communication in Computational Physics, 9(4):1056–1070, 2011.

Z Xu, X Cheng and H Yang. Treecode-based generalized born method. Journal of Chemical Physics, 134(6):064107, 2011.

S Fritsch, I Ivanov, H Wang and X Cheng. Ion selectivity mechanism in a bacterial pentameric 

ligand-gated ion channel. Biophysical Journal, 100(2):390–398, 2011.

M Saharay, H-B Guo, JC Smith and H Guo. QM/MM Analysis of Cellulase Active Sites and Actions 

of the Enzymes on Substrates, ACS Symposium Series, volume 1052, chapter 7, pages 135–154. ACS 

Publications, 2011.

L Petridis, J Xu, M Crowley, JC Smith and X Cheng. Atomistic Simulation of Lignocellulosic Biomass and Associated Cellulosomal Protein Complexes, ACS Symposium Series, volume 1052, 

chapter 3, pages 55–73. ACS Publications, 2011.

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V Philip, J Harris, R Adams, D Nguyen, J Spiers, J Baudry, EE Howell and RJ Hinde. A survey of 

aspartate-phenylalanine and glutamate-phenylalanine interactions in the protein data bank: 

Searching for anion-π pairs. Biochemistry, 50(14):2939–2950, 2011.

L Petridis, SV Pingali, V Urban, W Heller, H O’ Neil, M Foston, A Ragauskas and JC Smith. Self-similar multiscale structure of lignin revealed by neutron scattering and molecular dynamics simulation. Physical Review E, 83:061911, 2011.

B Collignon, R Schulz, JC Smith and J Baudry. Task-parallel MPI implementation of Autodock4 for 

docking of very large databases of compounds using high performance super-computers. Journal 

of Computational Chemistry, 32(6):1202– 1209, 2011.

WI Hembree and J Baudry. Three-dimensional mapping of micro-environmental control of 

methyl rotational barriers. Journal of Physical Chemistry B, 115(26):8575–8580, 2011.

T Li, W-G Choi, IS Wallace, J Baudry and DM Roberts. Arabidopsis thaliana NIP7;1: An anther-specific boric acid transporter of the aquaporin superfamily regulated by an unusual tyrosine in helix 

2 of the transport pore. Biochemistry, 50(31):6633–6641, 2011.

J Yao, R Nellas, M.M Glover and T Shen. Stability and sugar recognition ability of ricin-like carbohydrate binding domains. Biochemistry, 50(19):4097–4104, 2011.

SS Wang, T Shen and PG Wolynes. The interplay of nonlinearity and architecture in equilibrium 

cytoskeletal mechanics. Journal of Chemical Physics, 134(1):014510, 2011.

F Noé, S Doose, I Daidone, M Lollmann, M Sauer, JD Chodera and JC Smith. Dynamical fingerprints: Probing individual relaxation processes in biomolecular dynamics with simulations and 

kinetic experiments. Proceedings of the National Academy of Sciences (U.S.A), 108(12):4822–4827, 

2011.

J-H Prinz, J Chodera, V Pande, W Swope, JC Smith and F Noé. Optimal use of data in parallel 

tempering simulations for the construction of discrete-state Markov models of biomolecular 

dynamics. Journal of Chemical Physics, 134:244108, 2011.

T Splettstoesser, K. C Holmes, F Noe and JC Smith. Structural modeling and molecular dynamics 

simulation of the actin filament. PROTEINS: Structure, Function and Bioinformatics, 79(7):2033–

2043, 2011.

I Daidone, A Di Nola and JC Smith. Molecular origin of Gerstmann-Sträusslerscheinker syndrome: 

Insight from computer simulation of an amyloidogenic prion peptide. Biophysical Journal, 

100(12):3000–3007, 2011.

E Balog, D Perahia, JC Smith and F Merzel. Vibrational softening of a protein on ligand binding. 

The Journal of Physical Chemistry B, 115(21):6811–6817, 2011.

Q Xu, J Yao, A Wlodawer and H Guo. Clarification of the mechanism of acylation reaction and 

origin of substrate specificity of the serine-carboxyl peptidase sedolisin through QM/MM free 

energy simulations. The Journal of Physical Chemistry B, 115(10):2470–2476, 2011.

J Yao, Q Xu, F Chen and H Guo. QM/MM free energy simulations of salicylic acid methyltransferase: Effects of stabilization of TS-like structures on substrate specificity. The Journal of Physical 

Chemistry B, 115(2):389–396, 2011.

L Li, X Jiang, H Guan, P Wang and H Guo. Three alginate lyases from marine bacterium pseudomonas fluorescens HZJ216: Purification and characterization. Applied Biochemistry and Biotechnology, 164(3):305–317, 2011.

SD Brown, AM Guss, TV Karpinets, JM Parks, N Smolin, S Yang, ML Land, DM Klingeman, A 

Bhandiwand, M Rodriguez, B Raman, X Shao, JR Mielenz, JC Smith, M Keller and LR Lynd. Mutant 

alcohol dehydrogenase leads to improved ethanol tolerance in clostridium thermocellum. Proceedings of the National Academy of Sciences (U.S.A.), 108(33):13752–13757, 2011.

M Zahran, T Berezniak, P Imhof and JC Smith. Role of magnesium ions in DNA recognition by the 

ecorv restriction endonuclease. FEBS Letters, 585(17):2739– 2743, 2011.

SV Pingali, HM O’Neill, J McGaughey, VS Urban, CS Rempe, L Petridis, JC Smith, BR Evans and WT 

Heller. Small-angle neutron scattering reveals pH-dependent conformational changes in trichoderma reesei cellobiohydrolase I: Implications for enzymatic activity. Journal of Biological Chemistry, 286:32801– 32809, 2011.

L Hong, B Lindner, N Smolin, A Sokolov and JC Smith. Three classes of motion in the dynamic 

neutron-scattering susceptibility of a globular protein. Physical Review Letters, 107(14):148102, 

2011.

X Hu, N Elghobashi-Meinhardt, D Gembris and JC Smith. Response of water to electric fields at 

temperatures below the glass transition: A molecular dynamics analysis. Journal of Chemical 

Physics, 135:134507, 2011.

X Hu, A Norris, J Baudry and E Serpersu. Coenzyme a binding to the aminoglycoside acetyltransferase (3)-IIIb increases conformational sampling of antibiotic binding site. Biochemistry, 

50(48):10559–10565, 2011.

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A Sangha, L Petridis, JC Smith, A Ziebell and JM Parks. Molecular Simulation as a Tool for Studying Lignin. Environmental Progress & Sustainable Energy, 31(1):47–54, 2012.

M Biswas, T Wocjan, J Langowski and JC Smith. DNA Bending Potentials for Loop-Mediated Nucleosome Repositioning. Europhysics Letters, 97(3):38004, 2012.

K Voltz, J Trylska, N Calimet, JC Smith and J Langowski. Unwrapping of Nucleosomal DNA Ends: 

A Multiscale Molecular Dynamics Study. Biophysical Journal, 102(4):849–858, 2012.

B Lindner and JC Smith. Sassena - X-ray and Neutron Scattering Calculations from Molecular 

Dynamics Trajectories using Massively Parallel Computers. Computer Physics Communications, 

183(7):1491–1501, 2012.

X Cheng, JM Parks, L Petridis, B Lindner, R Schulz, H-B Guo, G Srinivas and JC Smith. Molecular 

Simulation in the Energy Biosciences, Innovations in Biomolecular Modeling and Simulations : 

Volume 1, chapter 5, pages 87–114. RSC Publishing, 2012.

X Cheng and I Ivanov. Molecular Dynamics. Methods in Molecular Biology, 929:243–285, 2012.

M Krishnan and JC Smith. Reconstruction of Protein Side-Chain Conformational Free Energy 

Surfaces From NMR-Derived Methyl Axis Order Parameters. The Journal of Physical Chemistry B, 

116(14):4124–4133, 2012.

L Hong, DC Glass and JC Smith. Surface Hydration Amplifies Single-Well Protein Atom Diffusion 

Propagating into the Macromolecular Core. Physical Review Letters, 108(23):238102, 2012.

T Berezniak, A Jaeschke, JC Smith and P Imhof. Stereoselection in the Diels-Alderase Ribozyme: A 

Molecular Dynamics Study. Journal of Computational Chemistry, 33(19):1603–1614, 2012.

R Pool, J Heringa, M Hoefling, R Schulz and JC Smith. Enabling Grand-Canonical Monte Carlo: 

Extending the Flexibility of GROMACS through the GromPy Python Interface Module to the GROMACS Simulation C-library. Journal of Computational Chemistry, 33(12):1207–1214, 2012.

JP Ulmschneider, JC Smith, MB Ulmschneider, AS Ulrich and E Strandberg. Reorientation and Dimerization of the Membrane-Bound Antimicrobial PeptidePGLa from Microsecond All-Atom MD 

Simulations. Biophysical Journal, 103(3):472–482, 2012.

DC Glass, K Moritsugu, X Cheng and JC Smith. REACH Coarse-Grained Simulation of a Cellulose 

Fiber. Biomacromolecules, 13(9):2634–2644, 2012.

F Merzel, MR Johnson and JC Smith. Normal Modes and Neutrons: Defining Collective, Functional Biomolecular Motions. Neutron News, 3(23):26–30, 2012.

Z Yi, Y Miao, J Baudry, N Jain and JC Smith. Derivation of Mean-Square Displacements for Protein Dynamics from Elastic Incoherent Neutron Scattering. The Journal of Physical Chemistry B, 

116(16):5028–5036, 2012.

AK Sangha, JM Parks, RF Standaert, A Ziebell, M Davis and JC Smith. Radical Coupling Reactions 

in Lignin Synthesis: A Density Functtional Theory Study. The Journal of Physical Chemistry B, 

116(16):4760–4768, 2012.

I Daidone, C Iacobucci, SE McLain and JC Smith. Alteration of Water Structure by Peptide Clusters Revealed by Neutron Scattering in the Small-Angle Region (below 1 ̊A−1). Biophysical Journal, 

103(7):1518–1524, 2012.JD Nickels, H O’Neill, L Hong, M Tyagi, G Ehlers, KL Weiss, Q Zhang, Z Yi, 

E Mamontov, JC Smith and AP Sokolov. Dynamics of a Protein and Its Hydration Water: Neutron 

Scattering Studies on Fully Deuterated GFP. Biophysical Journal, 104(7):1566–1575, 2012.

P Langan, BR Evans, M Foston, WT Heller, H O’Neill, L Petridis, SV Pingali, AJ Ragauskas, JC Smith, 

VS Urban and BH Davison. Neutron Technologies for Bioenergy Research. Industrial Biotechnology, 8(4):209–216, 2012.

RB Nellas, MM Glover, D Hamelberg and T Shen. High-Pressure Effect on the Dynamics of Solvated Peptides. The Journal of Chemical Physics, 136:145103, 2012.

A Asztalos, M Daniels, A Sethi, T Shen, P Langan, A Redondo and S Gnanakaran. A Coarse-Grained 

Model for Synergistic Action of Multiple Enzymes on Cellulose. Biotechnology for Biofuels, 5(1):55, 

2012.

QR Johnson, RB Nellas and T Shen. Solvent-Dependent Gating Motions of an Extremophilic Lipase from Pseudomonas aeruginosa. Biochemistry, 51(31):6238– 6245, 2012.

J Yao, Y Chu, R An and H Guo. Understanding Product Specificity of Protein Lysine Methyltransferases from QM/MM Molecular Dynamics and Free Energy Simulations: The Effects of Mutation 

on SET7/9 beyond the Tyr/Phe Switch. Journal of Chemical Information and Modeling, 52(2):449–

456, 2012.

X Ye, S Yuan, H Guo, F Chen, GA Tuskan and Z-M Cheng. Evolution and Divergence in the Coding 

and Promoter Regions of the Populus Gene Family Encoding Xyloglucan Endotransglycosylase/

Hydrolases. Tree Genetics & Genomes, 8(1):177–194, 2012.

Y Chu, J Yao and H Guo. QM/MM MD and Free Energy Simulations of G9a-Like Protein (GLP) 

and Its Mutants: Understanding the Factors that Determine the Product Specificity. PLOS ONE, 

7(5):e37674, 2012.

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Y Miao and J Baudry. Active site hydration and water diffusion in cytochrome P450cam: A highly 

dynamic process. Biophysical Journal, 101(6):1493–1503, 2011.

L Petridis, R Schulz and JC Smith. Simulation analysis of the temperature dependence of lignin 

structure and dynamics. Journal of the American Chemical Society, 133(50):20277–20287, 2011.

B Mostofian, JC Smith and X Cheng. The solvation structures of cellulose microfibrils in ionic 

liquids. Interdisciplinary Sciences - Computational Life Sciences, 3(4):308–320, 2011.

JC Smith, M Krishnan, N Smolin and L Petridis. Integration of Neutron Scattering with Computer 

Simulation to Study the Structure and Dynamics of Biological Systems, Dynamics of Biological 

Macromolecules by Neutron Scattering, chapter IV, pages 99–108. Bentham Science Publishers, 

2011.

H Klein, X Cheng, JC Smith and T Shen. Transfer matrix approach to the hydrogen-bonding in 

cellulose Ia fibrils describes the recalcitrance to thermal deconstruction. Journal of Physical 

Chemistry, 135(8):085106, 2011.

L Thukral, I Daidone and JC Smith. Structured pathway across the transition state for peptide 

folding revealed by molecular dynamics simulations. PLOS Computational Biology, 7(9):e1002137, 

2011.

J. P Ulmschneider, JC Smith, S White and MB Ulmschneider. In silico partitioning and transmembrane insertion of hydrophobic peptides under equilibrium conditions. Journal of the American 

Chemical Society, 133(39):15487–15495, 2011.

A Johs, I. M Harwood, JM Parks, RE Nauss, JC Smith, L Liang and SM Miller. Structural characterization of intramolecular Hg2+ transfer between flexibly linked domains of mercuric ion reductase. Journal of Molecular Biology, 413(3):639–656, 2011.

A-N Bondar, M Knapp-Mohammady, S Suhai, S Fischer and JC Smith. Groundstate properties of 

the retinal molecule: from quantum mechanical to classical mechanical computations of retinal 

proteins. Theoretical Chemistry Accounts: Theory, Computation and Modeling (Theoretica Chimica Acta), 130(4-6):1169–1183, 2011.

A Godec, JC Smith and F Merzel. Increase of both order and disorder in the first hydration shell 

with increasing solute polarity. Physical Review Letters, 107(26):267801, 2011.

M Biswas, K Voltz, JC Smith and J Langowski. Role of histone tails in structural stability of the 

nucleosome. PLOS Computational Biology, 7(12):e1002279, 2011.

J-H Prinz, M Held, JC Smith and F Noe. Efficient computation, sensitivity and error analysis of 

committor probabilities for complex dynamical processes. Multiscale Modeling and Simulation, 

9(2):545–567, 2011.

G Srinivas, X Cheng and JC Smith. A solvent-free coarse-grain model for crystalline and amorphous cellulose fibrils. Journal of Chemical Theory and Computation, 7(8):2539–2548, 2011.

H-B Guo, JM Parks, A Johs and JC Smith. Mercury Detoxification by Bacteria: Simulations of 

Transcription Activation and Mercury-Carbon Bond Cleavage, Modeling of Molecular Properties, 

chapter 20, pages 311–324. Wiley, 2011.

S Gnanakaran, T Bhattacharya, M Daniels, B. F Keele, P. T Hraber, A. S Lapedes, T Shen, B Gaschen, 

M Krishnamoorthy, H Li, J. M Decker, J. F Salazar-Gonzalez, S Wang, C Jiang, F Gao, R Swanstrom, 

J. A Anderson, L-H Ping, M. S Cohen, M Markowitz, P. A Goepfert, M. S Saag, J. J Eron, C. B Hicks, 

W. A Blattner, G. D Tomaras, M Asmal, N. L Letvin, P. B Gilbert, A. C DeCamp, C. A Magaret, W. R 

Schief, Y-E Andrew Ban, M Zhang, K. A Soderberg, J. G Sodroski, B. F Haynes, G. M Shaw, B. H 

Hahn and B Korber. Recurrent signature patterns in HIV-1 B clade envelope glycoproteins associated with either early or chronic infections. PLOS Pathogens, 7(9):e1002209, 2011.

SR Ellingson and J Baudry. High-Throughput Virtual Molecular Docking: Hadoop Implementation 

of AutoDock4 on a Private Cloud, Proceedings of the second international workshop on Emerging 

computational methods for the life sciences, chapter 5, pages 33–38. ACM, 2011.

2012

M. C Brothers, AE Nesbitt, MJ Hallock, S. G Rupasinghe, M Tang, J Harris, J Baudry, M. A Schuler 

and CM Rienstra. VITAL NMR: Using Chemical Shift Derived Secondary Structure Information for 

a Limited Set of Amino Acids to Assess Homology Model Accuracy. Journal of Biomolecular NMR, 

52(1):41–56, 2012.

N Smolin, R Biehl, GR Kneller, D Richter and JC Smith. Functional Domain Motions in Proteins on 

the ~1–100 ns Timescale: Comparison of Neutron Spin-Echo Spectroscopy of Phosphoglycerate 

Kinase with Molecular-Dynamics Simulation. Biophysical Journal, 102(5):1108–1117, 2012.
J Yao, Q Xu and H Guo. QM/MM and Free Energy Simulations of Deacylation Reaction Catalyzed 

by Sedolisin, a Serine Carboxyl Peptidase. Molecular Simulation, 39(3):1–8, 2012.

H Guo. Narrowing the Gap in Understanding Protein Structure and Function through Computer 

Simulations. AIP Conference Proceedings, 1456:180–186, 2012.

Hao-Bo Guo, Feng He, Baohua Gu, Liyuan Liang and JC Smith. Time-Dependent Density Functional Theory Assessment of UV Absorption of Benzoic Acid Derivatives. The Journal of Physical 

Chemistry A, 116(48):11870–11879, 2012.

J Pan, X Cheng, F. A Heberle, B Mostofian, N Kucerka, P Drazba and J Katsaras. Interactions between Ether Phospholipids and Cholesterol As Determined by Scattering and Molecular Dynamics 

Simulations. The Journal of Physical Chemistry B, 116(51):14829–14838, 2012.

Y Miao, Z Yi, C Cantrell, DC Glass, J Baudry and JC Smith. Coupled Flexibility Change in Cytochrome P450cam Substrate Binding Determined by Neutron Scattering, NMR and Molecular Dynamics Simulation. Biophysical Journal, 103(10):2167–2176, 2012.

TJ Tschaplinski, RF Standaert, NL Engle, MZ Martin, AK Sangha, JM Parks, JC Smith, R Samuel, Y 

Pu, AJ Ragauskas, CY Hamilton, C Fu, ZY Wang, BH Davison, RA Dixon and JR Mielenz. DownRegulation of the Caffeic Acid O-Methyltransferase Gene in Switchgrass Reveals a Novel Monolignol Analog. Biotechnology for Biofuels, 5:71, 2012.

Y Miao, Z Yi, DC Glass, L Hong, M Tyagi, J Baudry, N Jain and JC Smith. Temperature-Dependent 

Dynamical Transitions of Different Classes of Amino Acid Residue in a Globular Protein. Journal 

of the American Chemical Society, 134(48):19576–19579, 2012.

B. J Mintz and JM Parks. Benchmark Interaction Energies for Biologically Relevant Noncovalent 

Complexes Containing Divalent Sulfur. The Journal of Physical Chemistry A, 116(3):1086–1092, 

2012.

H-L Wang, X Cheng and SM Sine. Intramembrane Proton Binding Site Linked to Activation of Bacterial Pentameric Ion Channel. The Journal of Biological Chemistry, 287(9):6482–6489, 2012.

H Sharma, X Cheng and JK Buolamwini. Homology Model-Guided 3D-QSAR Studies of HIV-1 Integrase Inhibitors. Journal of Chemical Information and Modeling, 52(2):515–544, 2012.

2013

B Zhang, B Lu, X Cheng, J Huang, N Pitsianis, X Sun and AJ McCammon. Mathematical and 

numerical aspects of the adaptive fast multipole Poisson-Boltzmann solver. Communications in 

Computational Physics, 13(1):107–128, 2013.

D Riccardi, H-B Guo, JM Parks, B Gu, L Liang and JC Smith. Clustercontinuum calculations of 

hydration free energies of anions and group 12 divalent cations. Journal of Chemical Theory and 

Computation, 9(1):555–569, 2013.

M Mayes, S Jagadamma, H Ambaye, L Petridis and V Lauter. Neutron reflectometry reveals the internal structure of natural organic matter deposited onto an aluminum oxide. Geoderma, 192:182–

188, 2013.

L Hong, DC Glass, JD Nickels, S Perticaroli, Z Yi, M Tyagi, H O’Neill, Q Zhang, AP Sokolov and 

JC Smith. Elastic and conformational softness of a globular protein. Physical Review Letters, 

110(2):028104, 2013.

R Chaudret, JM Parks and W Yang. Pseudobond parameters for QM/MM studies involving nucleosides, nucleotides and their analogs. The Journal of Chemical Physics, 138:045102, 2013.

JM Parks, A Johs, M Podar, R Bridou, R. A Hurt, SD Smith, SJ Tomanicek, Y Qian, SD Brown, CC 

Brandt, AV Palumbo, JC Smith, JD Wall, DA Elias and L Liang. The genetic basis for bacterial mercury methylation. Science, 339(6125):1332–1335, 2013.

DC Glass, M Krishnan, JC Smith and J Baudry. Three entropic classes of side chains in a globular 

protein. The Journal of Physical Chemistry B, 117(11):3127– 3134, 2013.

DD Jenkins, JB Harris, EE Howell, RJ Hinde and J Baudry. Staar: Statistical analysis of aromatic 

rings. Journal of Computational Chemistry, 34(6):518–522, 2013.

Y Miao, L Hong, Z Yi and JC Smith. Zaccai neutron resilience and site-specific hydration dynamics 

in a globular protein. The European Physical Journal E, 36:72, 2013.

D Sawada, Y Nishiyama, L Petridis, R Parthasarathi, S Gnanakaran, VT Forsyth, M Wada and P 

Langan. Structure and dynamics of a complex of cellulose with EDA: Insights into the action of 

amines on cellulose. Cellulose, 20(4):1563–1571, 2013.

B Lindner, L Petridis, R Schulz and JC Smith. Solvent-driven preferential association of lignin 

with regions of crystalline cellulose in molecular dynamics simulation. Biomacromolecules, 

14(10):3390–3398, 2013.

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Z Yi, B Lindner, J-H Prinz, F Noe and JC Smith. Dynamic neutron scattering from conformational 

dynamics. II. Application using molecular dynamics simulation and Markov modeling. The Journal of Chemical Physics, 139(17):175102, 2013.

DR Ortega, C Yang, P Ames, J Baudry, JS Parkinson and IB Zhulin. A phenylalanine rotameric 

switch for signal-state control in bacterial chemoreceptors. Nature Communications, 4:2881, 2013.

RB Nellas, QR Johnson and T Shen. Solvent-induced α- to 3 10-helix transition of an amphiphilic 

peptide. Biochemistry, 52(40):7137–7144, 2013.

T Shen, X Qi and RB Nellas. Nucleation dynamics of active particles. The Journal of Physical 

Chemistry B, 117(42):12844–12849, 2013.

X Qi, RB Nellas, MW Byrn, MH Russell, AN Bible, G Alexandre and T Shen. Swimming motility 

plays a key role in the stochastic dynamics of cell clumping. Physical Biology, 10(2):026005, 2013.

SR Ellingson, JC Smith and J Baudry. VinaMPI: Facilitating multiple receptor high-throughput virtual docking on high performance computers. Journal of Computational Chemistry, 34(25):2212–

2221, 2013.

DJ Cashman, DR Ortega, IB Zhulin and J Baudry. Homology modeling of the chew coupling protein of the chemotaxis signaling complex. PLOS ONE, 8(8):e70705, 2013.

DR Ortega, G Mo, K Lee, H Zhou, J Baudry, FW Dahlquist and IB Zhulin. Conformational coupling 

between receptor and kinase binding sites through a conserved salt bridge in a signaling complex 

scaffold protein. PLOS Computational Biology, 9(11):e1003337, 2013.

S Pronk, S Pall, R Schulz, P Larsson, P Bjelkmar, R Apostolov, MR Shirts, JC Smith, P. M Kasson, D 

van der Spoel, B Hess and E Lindahl. Gromacs 4.5: A high-throughput and highly parallel open 

source molecular simulation toolkit. Bioinformatics, 29(7):845–854, 2013.

C McClintock, JM Parks, M Bern, PK GhattyVenkataKrishna and RL Hettich. Comparative informatics analysis to evaluate site-specific protein oxidation in multidimensional LC–MS/MS data. 

Journal of Proteome Research, 12(7):3307– 3316, 2013.

D Riccardi, H-B Guo, JM Parks, B Gu, A. O Summers, SM Miller, L Liang and JC Smith. Why mercury prefers soft ligands. The Journal of Physical Chemistry Letters, 4(14):2317–2322, 2013.

BH Davison, JM Parks, MF Davis and BS Donohoe. Plant Cell Walls: Basics of Structure, Chemistry, 

Accessibility and the Influence on Conversion, Aqueous Pretreatment of Plant Biomass for Biological and Chemical Conversion to Fuels and Chemicals, chapter 3, pages 23–35. John Wiley & Sons, 

Ltd., 2013.

D Vural, L Hong, JC Smith and H Glyde. Long-time mean square displacements in proteins. Physical Review E, 88:052706, 2013.

JC Smith and B Roux. Eppur si muove! The 2013 Nobel prize in chemistry. Structure, 21(12):2102–

2105, 2013.

Q Chen, JK Buolamwini, JC Smith, A Li, Q Xu, X Cheng and D-Q Wei. Impact of resistance mutations on inhibitor binding to HIV-1 integrase. Journal of Chemical Information and Modeling, 

53(12):3297–3307, 2013.

B Lindner, Z Yi, J-H Prinz, JC Smith and F Noe. Dynamic neutron scattering from conformational 

dynamics I: Theory and Markov models. The Journal of Chemical Physics, 139(17):175101, 2013.

A Godec, JC Smith and F Merzel. Soft collective fluctuations governing hydrophobic association. 

Physical Review Letters, 111(12):127801, 2013.

A Devarajan, S Markutsya, MH Lamm, X Cheng, JC Smith, JY Baluyut, Y Kholod, MS Gordon and 

TL Windus. Ab initio study of molecular interactions in cellulose Iα. The Journal of Physical Chemistry B, 117(36):10430–10443, 2013.

X Wang, T Yang, X Cheng and Q Shen. Enantioselective electrophilic trifluoromethylthiolation 

of β-ketoesters: A case of reactivity and selectivity bias for organocatalysis. Angewandte Chemie 

International Edition, 52(49):12860–12864, 2013.

B Lu, X Cheng, J Huang and JA McCammon. AFMPB: An adaptive fast multipole Poisson–

Boltzmann solver for calculating electrostatics in biomolecular systems. Computer Physics Communications, 184(11):2618–2619, 2013.

H Sharma, TW Sanchez, N Neamati, M Detorio, RF Schinazi, X Cheng and JK Buolamwini. Synthesis, docking and biological studies of phenanthrene β-Diketo acids as novel HIV-1 integrase 

inhibitors. Bioorganic & Medicinal Chemistry Letters, 23(22):6146–6151, 2013.

PK GhattyVenkataKrishna, EC Uberbacher and X Cheng. Effect of the amyloid β-hairpin’s structure on the handedness of helices formed by its aggregates. FEBS Letters, 587(16):2649–2655, 2013.

PK GhattyVenkataKrishna, EM Alekozai, GT Beckham, R Schulz, MF Crowley, EC Uberbacher and 

X Cheng. Initial recognition of a cellodextrin chain in the cellulose-binding tunnel may affect cellobiohydrolase directional specificity. Biophysical Journal, 104(4):904–912, 2013.

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2014

SR Ellingson, S Dakshanamurthy, M Brown, JC Smith and J Baudry. Accelerating virtual highthroughput ligand docking: Screening one million compounds using a petascale supercomputer. 

Concurrency and Computation: Practice and Experience, 2014. 

P Langan, L Petridis, HM O’Neill, SV Pingali, M Foston, Y Nishiyama, R Schulz, B Lindner, B. L 

Hanson, S Harton, WT Heller, V Urban, BR Evans, S Gnanakaran, AJ Ragauskas, JC Smith and BH 

Davison. Common processes drive the thermochemical pretreatment of lignocellulosic biomass. 

Green Chemistry, 16:63–68, 2014.

AK Sangha, BH Davison, RF Standaert, MF Davis, JC Smith and JM Parks. Chemical factors that 

control lignin polymerization. The Journal of Physical Chemistry B, 118(1):164–170, 2014.

J Zhou, D Riccardi, A Beste, JC Smith and JM Parks. Mercury methylation by HgcA: Theory supports carbanion transfer to Hg(II). Inorganic Chemistry, 53(2):772–777, 2014.

G Srinivas, X Cheng and JC Smith. Coarse-grain model for natural cellulose fibrils in explicit water. 

The Journal of Physical Chemistry B, 118(11):3026–3034, 2014.

E Alekozai, PK GhattyVenkataKrishna, EC Uberbacher, M. F Crowley, JC Smith and X Cheng. Simulation analysis of the cellulase Cel7A carbohydrate binding module on the surface of the cellulose 

Iβ. Cellulose, 21(2):951–971, 2014.

P Lian, H-B Guo, JC Smith, D-Q Wei and H Guo. Catalytic mechanism and origin of high activity 

of cellulase TmCel12A at high temperature: A quantum mechanical/molecular mechanical study. 

Cellulose, 21(2):937–949, 2014.

B Mostofian, JC Smith and X Cheng. Simulation of a cellulose fiber in ionic liquid suggests a synergistic approach to dissolution. Cellulose, 21(2):983–997, 2014.

DJ Cashman, T Zhu, RF Simmerman, C Scott, BD Bruce and J Baudry. Molecular interactions 

between photosystem I and ferredoxin: An integrated energy frustration and experimental model. 

Journal of Molecular Recognition, 27(20):597–608, 2014.

S Ellingson, JC Smith and J Baudry. Polypharmacology and supercomputerbased docking: Opportunities and challenges. Molecular Simulation, 40(1011):848–854, 2014.

JB Harris, ML Eldridge, G Sayler, FM Menn, A. C Layton and J Baudry. A computational approach 

predicting CYP450 metabolism and estrogenic activity of an endocrine disrupting compound 

(pcb-30). Environmental Toxicology and Chemistry, 33(7):1615–1623, 2014.

QR Johnson, RJ Lindsay, SR Raval, JS Dobbs, RB Nellas and T Shen. Effects of branched o-glycosylation on a semiflexible peptide linker. The Journal of Physical Chemistry B, 118(8):2050–2057, 

2014.

H Lin, RA Hurt Jr., A Johs, JM Parks, JL Morrell-Falvey, L Liang, DA Elias and B Gu. Unexpected effects of gene deletion on interactions of mercury with the methylation-deficient mutant ΔhgcAB. 

Environemntal Science &Technology Letters, 1(5):271–276, 2014.

T Wymore, M. J Field, P Langan, JC Smith and JM Parks. Hydrolysis of DFP and the nerve agent 

(S)-Sarin by DFPase proceeds along two different reaction pathways: Implications for engineering 

bioscavengers. The Journal of Physical Chemistry B, 118(17):4479–4489, 2014.

L Hong, M. A Sharp, S Poblete, R Biehl, M Zamponi, N Szekely, M-S Appavou, RG Winkler, RE Nauss, 

A Johs, JM Parks, Z Yi, X Cheng, L Liang, M Ohl, SM Miller, D Richter, G Gompper and JC Smith. 

Structure and dynamics of a compact state of a multidomain protein, the mercuric ion reductase. 

Biophysical Journal, 107(2):393–400, 2014.

P Langan, AK Sangha, T Wymore, JM Parks, ZK Yang, BL Hanson, Z Fisher, SA Mason, MP Blakeley, 

VT Forsyth, JP Glusker, HL Carrell, JC Smith, DA Keen, DE Graham and A Kovalevsky. L-arabinose 

binding, isomerization and epimerization by d-xylose isomerase: X-ray/neutron crystallographic 

and molecular simulation study. Structure, 22:1–14, 2014.

SV Pingali, HM O’Neill, Y Nishiyama, L He, YB Melnichenko, V Urban, L Petridis, B Davison and P 

Langan. Morphological changes in the cellulose and lignin components of biomass occur at different stages during steam pretreatment. Cellulose, 21(2):873–878, 2014.

L Hong, N Smolin and JC Smith. de Gennes narrowing describes the relative motion of protein 

domains. Physical Review Letters, 112:158102, 2014.

RT Rajeshwar, JC Smith and M Krishnan. Hidden regularity and universal classification of fast 

side chain motions in proteins. Journal of the American Chemical Society, 136(24):8590–8605, 

2014.

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K Moritsugu, A Kidera and JC Smith. Solvent friction effects propagate over the entire protein molecule through low-frequency collective modes. The Journal of Physical Chemistry B, 118(29):8559–

8565, 2014.

B Mostofian, X Cheng and JC Smith. Replica-exchange molecular dynamics simulations of cellulose solvated in water and in the ionic liquid 1-butyl-3methylimidazolium chloride. The Journal of 

Physical Chemistry B, 118(38):11037– 11049, 2014.

L Hong, L Petridis and JC Smith. Biomolecular structure and dynamics with neutrons: The view 

from simulation. Israel Journal of Chemistry, 54(8-9):1264– 1273, 2014.

B Lindner, L Petridis, P Langan and JC Smith. Determination of cellulose crystallinity from powder diffraction patterns. Biopolymers, 103(2):67–73, 2014.

P Lian, H-B Guo, D Riccardi, A Dong, JM Parks, Xu Q, EF Pai, SM Miller, D-Q Wei, JC Smith and H 

Guo. X-ray structure of a Hg2+ complex of mercuric reductase (MerA) and quantum mechanical/

molecular mechanical study of Hg2+ transfer between the c-terminal and buried catalytic site 

cysteine pairs. Biochemistry, 53(46):7211–7222, 2014.

L Petridis, H O’Neill, M Johnsen, B Fan, R Schulz, E Mamontov, J Maranas, P Langan and JC Smith. 

Hydration control of the mechanical and dynamical properties of cellulose. Biomacromolecules, 

15(11):4152–4159, 2014.

J Erler, R Zhang, L Petridis, X Cheng, JC Smith and J Langowski. The role of histone tails in the 

nucleosome: A computational study. Biophysical Journal, 107(12):2911–2922, 2014.

BJ Harris, X Cheng and P Frymier. All-atom molecular dynamics simulation of a photosystem I/

detergent complex. The Journal of Physical Chemistry B, 118(40):11633–11645, 2014.

RK Le, BJ Harris, IJ Iwuchukwu, BD Bruce, X Cheng, S Qian, WT Heller, H O’Neill and PD Frymier. Analysis of the solution structure of Thermosynechococcus elongatus photosystem I in 

n-dodecyl-β-d-maltoside using small-angle neutron scattering and molecular dynamics simulation. Archives of Biochemistry and Biophysics, 550:50–57, 2014.

J Pan, X Cheng, L Monticelli, FA Heberle, N Kucerka, DP Tieleman and J Katsaras. The molecular 

structure of a phosphatidylserine bilayer determined by scattering and molecular dynamics simulations. Soft Matter, 10(21):3716–3725, 2014.

2015

T Wolter, M Elstner, S Fischer, JC Smith and A-N Bondar. Mechanism by which untwisting of retinal leads to productive bacteriorhodopsin photocycle states. The Journal of Physical Chemistry B, 

119(6):2229–2240, 2015.

SR Ellingson, Y Miao, J Baudry and JC Smith. Multi-conformer ensemble docking to difficult protein targets. The Journal of Physical Chemistry B, 119(3):1026–1034, 2015.

C Carmona, P Langan, JC Smith and L Petridis. Why genetic modification of lignin leads to lowrecalcitrance biomass. Physical Chemistry Chemical Physics, 17:358–364, 2015.

C Topham and JC Smith. Tri-peptide reference structures for the calculation of relative solvent 

accessible surface area in protein amino acid residues. Computational Biology and Chemistry, 

54:33–43, 2015.

D Riccardi, JM Parks, A Johs and JC Smith. Hackamol: An object-oriented modern perl library for 

molecular hacking on multiple scales. Journal of Chemical Information and Modeling, 55(4):721–

726, 2015.

I Daidone, L Thukral, JC Smith and A Amadei. Monitoring the folding kinetics of a β-hairpin by 

time-resolved IR spectroscopy in silico. The Journal of Physical Chemistry B, 119(14):4849–4856, 

2015.

SD Smith, R Bridou, A Johs, JM Parks, DA Elias, RA Hurt Jr, SD Brown, M Podar and JD Wall. Sitedirected mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury 

methylation. Applied and Environmental Microbiology, 81(9):3205–3217, 2015.

QR Johnson, RJ Lindsay, L Petridis and T Shen. Investigation of carbohydrate recognition via 

computer simulation. Molecules, 20(5):7700–7718, 2015.

QR Johnson, RJ Lindsay, RB Nellas, E. J Fernandez and T Shen. Mapping allostery through computational glycine scanning and correlation analysis of residue–residue contacts. Biochemistry, 

54(7):1534–1541, 2015.

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T Shen and C. F Wong. Brownian Dynamics Simulation of Peptides with the University of Houston 

Brownian Dynamics (UHBD) Program, volume 1268 of Methods in Molecular Biology, chapter 5, 

pages 75–87. Springer, New York, 2015.

D Vural, L Hong, JC Smith and H Glyde. Motional displacements in proteins: The origin of wavevector-dependent values. Physical Review E, 91:052705, 2015.

Z Cournia, T Allen, I Andricioaei, B Antonny, D Baum, G Brannigan, N-V Buchete, T Deckman, L 

Delemotte, C Del Val, R Freidman, P Gkeka, H-C Hege, J Henin, M Kasimova, A Kolocouris, ML 

Klein, S Khalid, J Lemieu, N Lindow, M Roy, J Selent, M Tarek, F Tofoleanu, S Vanni, S Urban, DJ 

Wales, JC Smith and A-N Bondar. Membrane proteins: Environmental effects on structure, function and dynamics. Journal of Membrane Biology, 248(4):611–640, 2015.

NK Khadka, X Cheng, CS Ho, J Katsaras and J Pan. Interactions of the anticancer drug tamoxifen 

with lipid membranes. Biophysical Journal, 108(10):2492– 2501, 2015.

JS Wall, EB Martin, T Richey, AC Stuckey, S Macy, C Wooliver, A Williams, JS Foster, P McWilliamsKoeppen, E Ubebacher, X Cheng and SJ Kennel. Preclinical validation of the heparin-reactive peptide p5+14 as a molecular imaging agent for visceral amyloidosis, Molecules, 20(5):7657–7682, 2015.

Q Chen, Z-J Luan, X Cheng and J-H Xu. Molecular dynamics investigation of the substrate binding mechanism in carboxylesterase. Biochemistry, 54(9):1841– 1848, 2015.

Q Chen, X Cheng, D Wei and Q Xu. Molecular dynamics simulation studies of the wild type and 

E92Q/N155H mutant of elvitegravir-resistance HIV-1 integrase. Interdisciplinary Sciences: Computational Life Sciences, 7(1):36–42, 2015.

J Pan, X Cheng, M Sharp, C-S Ho, N Khadka and J Katsaras. Structural and mechanical properties 

of cardiolipin lipid bilayers determined using neutron spin echo, small angle neutron and x-ray 

scattering and molecular dynamics simulations. Soft Matter, 11(1):130–138, 2015.

R Ming, R VanBuren, C. M Wai, H Tang, M. C Schatz, J. E Bowers, E Lyons, Ming-Li Wang, J Chen, 

E Biggers, J Zhang, L Huang,L Zhang, W Miao,J Zhang, Z Ye, C Miao, Z Lin, H Wang, H Zhou, W C 

Yim, H. D Priest, C Zheng, M Woodhouse, P. P Edger, R Guyot, H-B Guo, H Guo, G Zheng, R Singh, 

A Sharma, X Min, Y Zheng, H Lee, J Gurtowski, F. J Sedlazeck, A Harkess,M. R McKain, Z Liao, J 

Fang, Juan Liu, X Zhang, Q Zhang, W Hu, Y Qin, K Wang, L-Y Chen, N Shirley, Y-R Lin, L-Y Liu, A. G 

Hernandez, C. L Wright, V Bulone, G. A Tuskan, K Heath, F Zee, P. H Moore,R Sunkar, J. H LeebensMack, T Mockler, J. L Bennetzen, M Freeling, D Sankoff, A. H Paterson, X Zhu,X Yang, J Andrew C 

Smith, J. C Cushman, R. E Paull and Q Yu The pineapple genome and the evolution of CAM photosynthesis. Nature Genetics, 47, 1435–1442, 2015.

Y Yue, CY Chu and H Guo. Computational Study of Symmetric Methylation on Histone Arginine 

Catalyzed by Protein Arginine Methyltransferase PRMT5 through QM/MM MD and Free Energy 

Simulations, Molecules, 20(6), 10032–10046, 2015.

CY Chu and H Guo. QM/MM MD and free energy simulation study of methyl transfer processes 

catalyzed by PKMTs and PRMTs. Interdisciplinary Sciences: Computational Life Sciences, 7, 1–10, 

2015.

A Das, O Gerlits, JM Parks, P Langan, SS Taylor, A Kovalevsky and WT Heller. Protein kinase A 

catalytic subunit primed for action: Time-lapse crystallography of Michaelis complex formation. 

Structure, 23, 2331–2340, 2015.

Q Wan, JM Parks, BL Hanson, Z Fisher, A Ostermann, TE Schrader, DE Graham, L Coates, P Langan 

and A Kovalevsky. Direct determination of protonation states and visualization of hydrogen bonding in a glycoside hydrolase with neutron crystallography. Proceedings of the National Academy 

of Sciences (U.S.A), 112, 12384–12389, 2015.

SD Smith, R Bridou, A Johs, JM Parks, DA Elias, RA Hurt Jr., SD Brown, M. Podar and J. D. Wall. 

Site-directed mutagenesis of HgcA and HgcB reveals amino acid residues important for mercury 

methylation, Applied and Environmental Microbiology, 81, 3205–3217, 2015.

MJ Abraham, T Murtola, R. Schulz, S. Pall JC Smith, B Hess and E Lindahl. GROMACS: high performing molecular simulation through multi-level parallelism from laptops to supercomputers. 

SoftwareX, 1, 19-25, 2015.

JD Nickels, JC Smith and X. Cheng. Lateral organization, bilayer asymmetry and inter-leaflet coupling of biological membranes. Chemistry and Physics of Lipids, 192, 87-99, 2015.

J Vermaas, L Petridis, X Qi, R. Schulz, B Lindner and JC Smith. Mechanism of lignin inhibition of 

enzymatic biomass deconstruction. Biotechnology for Biofuels, 8(1), 1, 2015.

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2016

MD Smith, B Mostofian, X Cheng, L Petridis, CM Cai, CE Wyman and JC Smith. Cosolvent pretreatment in cellulosic biofuel production: effect of tetrahydrofuran-water on lignin structure and 

dynamics. Green Chemistry, 18 (5), 1268-1277, 2016.

X Hu, L Hong, MD Smith, T Neusius, X Cheng and JC Smith. The dynamics of single protein 

molecules is non-equilibrium and self-similar over thirteen decades in time. Nature Physics, 12, 

171-174, 2016.

VG Vandavasi, DK Putnam, Q Zhang, L Petridis, WT Heller, BT Nixon, CH Haigler, U Kalluri, L 

Coates, P Langan, JC Smith, J Meiler and H O’Neill. A Structural Study of CESA1 Catalytic Domain 

of Arabidopsis Cellulose Synthesis Complex: Evidence for CESA Trimers. Plant Physiology, 170 (1), 

123-135, 2016.

MD Smith, L Petridis, X Cheng, B Mostofian and JC Smith. Enhanced sampling simulation analysis of the structure of lignin in the THF-water miscibility gap. Physical Chemistry Chemical Physics, 18 (9), 6394-6398, 2016.

L Petridis and JC Smith. Conformations of Low-Molecular-Weight Lignin Polymers in Water. 

ChemSusChem, 9(3), 289-295, 2016.

MD Smith, B Mostofian, L Petridis, X Cheng and JC Smith. Molecular Driving Forces behind the 

Tetrahydrofuran–Water Miscibility Gap. The Journal of Physical Chemistry., B 120 (4), 740-747, 

2016.

K Zheng, X Wang, DA Weighill, H-B Guo, M Xie, Y Yang, J Yang, S Wang, DA Jacobson, H Guo, W 

Muchero, GA Tuskan and J-G Chen. Characterization of DWARF14 Genes in Populus. Scientific 

Reports, 6, 21593, 2016.

N Zhao, H Lin, S Lan, Q Jia, X Chen, H Guo and F Chen. VvMJE1 of the grapevine (Vitis vinifera) 

VvMES methylesterase family encodes for methyl jasmonate esterase and has a role in stress response. Plant Physiology and Biochemistry, 102, 125-132, 2016.

O Gerlits, T Wymore, A Das, C-H Shen, JM Parks, JC Smith, K Weiss, DA Keen, MP Blakeley, JM 

Louis, P Langan, I Weber and A Kovalevsky. Long-Range Electrostatics-Induced Two-Proton 

Transfer Captured by Neutron Crystallography in an Enzyme Catalytic Site. Angewandte Chemie 

International Edition, 128(16), 5008-5011, 2016.

M Pi, K Kapoor, R Ye, SK Nishimoto, JC Smith, J. Baudry and LD Quarles. Evidence for Osteocalcin 

Binding and Activation of GPRC6A in β-Cells Endocrinology. Endocrinology, 157(5), 1866-1880, 

2016.

K Kapoor, N McGill, C Peterson, H Meyers, M Blackburn and J Baudry. Discovery of Novel NonActive Site Inhibitors of the Prothrombinase Enzyme Complex. Journal of Chemical Information 

and Modeling., 56, 535-547, 2016.

QR Johnson, RJ Lindsay, RB Nellas and T Shen. Pressure-induced conformational switch of an 

interfacial protein. Proteins: Structure, Function, and Bioinformatics, 84, 820-827, 2016.

Y Miao, J Baudry, JC Smith and JA McCammon. General Trends of Dihedral Conformational Transitions in a Globular Protein. Proteins: Structure, Function, and Bioinformatics, 84(4), 501-14, 2016..

AK Clark, JH Wilder, AW Grayson, QR Johnson, RJ Lindsay, RB Nellas, EJ Fernandez and T Shen. 

The Promiscuity of Allosteric Regulation of Nuclear Receptors by Retinoid X Receptor, The Journal 

of Physical Chemistry B, (in press).

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Postdoctoral Fellows

Mayank Aggarwal

Postdoctoral Fellow since 2014

John Eblen

Postdoctoral Fellow since 2011

Mikolai Fajer

Postdoctoral Fellow since 2015

Xiaohu Hu

Postdoctoral Fellow since 2016

Quentin Johnson

Postdoctoral Fellow since 2015

Ryan C. Johnston

Postdoctoral Fellow since 2015

Peng Lian

Postdoctoral Fellow since 2016

Micholas Dean Smith

Postdoctoral Fellow since 2013

Barmak Mostofian

Postdoctoral Fellow since 2014

Hector Velazquez

Postdoctoral Fellow since 2014

Visiting Researchers

Toyokatsu Ishida

National Institute of Advanced Industrial Science and Technology, Japan 

Administration

Lora Davis

Administrative Assistant

since 2015

System Administration

Michael D. Galloway

System Administrator since 8/2006

Nathan T. Grodowitz

System Administrator since 2014

90 91

Undergraduate Students

Kate Armstrong

Undergraduate Research Assistant 

since 2015

Alexander Clark

Undergraduate Research Assistant 

since 2015

Jordan Finley

Undergraduate Research Assistant 

since 2016 

Bailey Humphreys

Undergraduate Research Assistant

since 2016

Megan Landon

Undergraduate Research Assistant 

since 2015

Rexel Laureano

Undergraduate Research Assistant

since 2016

Madison Leonard

Undergraduate Research Assistant 

since 2016

Brian Simon

Summer Intern 2016

Gus White

Undergraduate Research Assistant 

since 2015

Graduate Students

Rupesh Agarwal

Graduate Student since 2014

Baker Martin

Graduate Student since 2015

Sarah Cooper

Graduate Student since 2015

Willy Evangelista Falcon

Graduate Student since 2011

Adam Green

Graduate Student since 2014

Yue (Moon) Ma

Graduate Student since 2016

Chris Mendoza

Graduate Student since 2016

John Ossyra

Graduate Student since 2013

Jing Zhou

Graduate Student since 2011

Yaojin Sun

Graduate Student since 2016

2016 Staff
Pavan Ghatty

Postdoctoral Fellow

2007 – 2013

Mary Glover

Undergraduate Research Assistant

2011 – 2012

Dennis C. Glass

Graduate Student

2009 – 2012

Michael Grace

Undergraduate Intern

2013

Aaron Grayson

Undergraduate Research Assistant

2015 – 2016

Hao-Bo Guo

Postdoctoral Fellow

2007 – 2014

Jason Harris

Graduate Student

2009 – 2014

William Hembree

Undergraduate Research Assistant

2008 – 2010

Liang Hong

Postdoctoral Fellow

2010 – 2014

Petra Imhof

Visiting Researcher

2008

Ryan Johnson

Undergraduate Research Assistant

2010-2013

Karan Kapoor

Graduate Student

2010 – 2015

Elizabeth Kolmus

Undergraduate Intern

2015

Ian Kotthoff

Undergraduate Intern

2015

Dieter P. Krachtus

Visiting Graduate Student

2008

Marimuthu Krishnan

Postdoctoral Fellow

2006 – 2010

Benjamin Lindner

Graduate Student

2007 – 2012

Postdoctoral Fellow

2012 – 2014

Richard Justin Lindsay

Graduate student

2011 – 2015

Michael Love

Undergraduate Student

2009

Sirui Ma

Undergraduate Intern

2015

Krishnan Marimuthu

Postdoctoral Fellow

2007 – 2010

92 93

Emal M. Alekozai

Visiting Graduate Student

2008, 2009

Samah Alnarthi

Undergraduate Research Assistant

2016

Anita Alton

Administrative Assistant 

2013 – 2015

Hector D. Arbelo-Lopez

Visiting Graduate Student

2014

Tyler Bennett

Graduate Student

2012 – 2014 

Tomasz Berezniak

Visiting Graduate Student

2008, 2009

Mithun Biswas

Visiting Graduate Student

2008

Cody Bogema

Undergraduate Research Assistant

2008 – 2009

Morgan Carson

Undergraduate Research Assistant

2008

Derek Cashman

Postdoctoral Fellow 

2011 – 2013

Casey Ceri

Undergraduate Research Assistant

2008

Qi Chen

Visiting Researcher

2011 – 2012

Janamejaya Chowdhary

Postdoctoral Fellow 

2013 – 2016

Barbara Collignon

Postdoctoral Fellow 

2008 – 2011

Julia Cooper

Administrative Assistant 

2006 – 2013

Ruggero Cortini

Visiting Graduate Student

2013

Isabella Daidone

Visiting Researcher

2007

Ian Deledicq

Visiting Undergraduate Student

2009

Tyler Earnest

Visiting Graduate Student

2014 – 2015

Sally Ellingson

Graduate Student 

2009 – 2014

Jochen Erler

Visiting Postdoctoral Fellow

2012

Sebastian Fritsch

Visiting Undergraduate Student

2008 – 2009

Alumni
Bogdan Tarus

Visiting Researcher

2008

Vonel Teragene

Undergraduate Research Assistant

2011

Lipi Thukral

Visiting Graduate Student

2007, 2010

Jianhui Tian

Postdoctoral Fellow 

2013 – 2016

Christopher Topham

Visiting Researcher in 2006

Derya Vural

Postdoctoral Fellow

2014 – 2016

Corinne Wacker

Visiting Undergraduate Student

2009 – 2010

Rebecca Weir

Undergraduate Research Assistant

2015

James Wilder

Undergraduate Research Assistant

2015

Troy Wymore

Research Assistant Professor

2013 – 2016

Jiancong Xu

Postdoctoral Fellow

2007 – 2009

Quin Xu

Postdoctoral Fellow

2010 – 2012

Zheng Yi

Postdoctoral Fellow

2009 – 2012

Yufei Yue

Graduate Student

2011 – 2016

Mai Zahran

Visiting Graduate Student

2007, 2010

He Zhang

Visiting undergraduate student

2011 – 2012

94 95

Sylvia E. McLain

SNS Clifford G. Shull Research Fellow and Group Leader

2006 – 2008

Yinglong Miao

Postdoctoral Fellow

2009 – 2012

Dan Mihailescu

Visiting Researcher in 2006

Kei Moritsugu

Postdoctoral Fellow

2007 – 2008

Steve Moulton

System Administrator

2011 – 2015

Saharay Moumita

Postdoctoral Fellow

2007 – 2010

Ricky Nellas

Postdoctoral Fellow

2010 – 2013

Jan-Hendrik Prinz

Visiting Graduate Student

2008

Daniel Putnam

Visiting Graduate student

2015

Xianghong Qi

Postdoctoral Fellow

2011 – 2014

Jonathan Reyles

Graduate Student

2010 – 2011

Demian Riccardi

Postdoctoral Fellow

2011 – 2014

Amandeep Sangha

Postdoctoral Fellow

2010 – 2014

Roland Schultz

Graduate Student

2007 – 2015

Pashupati Shah

Undergraduate Research Assistant

2013 – 2014

Sydney Smith

Undergraduate Intern

2015

Andrew B. Sneed

Undergraduate Research Assistant

2008 – 2009

Nikolai Smolin

Postdoctoral Fellow

2008 – 2011

Jeremy Spiers

Undergraduate Research Assistant

2008

Thomas Splettstoesser

Visiting Graduate Student

2007, 2009

Goundla Srinivas

Postdoctoral Fellow

2009 – 2011

Taylor Stevens

Undergraduate Research Assistant

2015 – 2016
Past and Current Grants

Starting 2006

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Computational Methods for Molecular Biophysics

Funding Period: 2006 – 2009

Jeremy C. Smith: PI

Starting 2007

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: Bioenergy Science Center

Funding Period: 2007 – 2012

Jeremy C. Smith: Task Leader

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: LAB 07-12 Dynamic Visualization of 

Lignocellulose Degradation by Integration 

of Neutron Scattering Imaging and 

Computer Simulation

Funding Period: 2007 – 2010

Jeremy C. Smith: Co-PI

Starting 2008

U.S. Department of Energy (DOE) – 

EpsCOR Implementation Award (+ 50% 

Univ. Tennessee matching)

Title: DE-FG02-08ER46528 Neutron Scattering Research Network for EPSCoR 

States

Funding Period: 2008 – 2011

Jeremy C. Smith: Co-PI

National Science Foundation (NSF)

Title: NIMBioS – Center for Synthesis of 

Mathematics and Biology

Funding Period: 2008 – 2012

Jeremy C. Smith: Co-PI

National Science Foundation (NSF) – 

Integrative Graduate Education and 

Research Traineeship Program (IGERT)

Title: SCALE-IT (Scalable Computing and 

Leading Edge Innovative Technologies) 

for Biology

Funding Period: 2008 – 2013

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: ORNL Scientific Focus Area Program: Biogeochemical and Molecular 

Mechanisms Controlling Contaminant 

Transformation in the Environment

Funding Period: 2008 – 2012

Jeremy C. Smith: Co-PI

Starting 2009

Deutsche Forschungegemeinschaft 

(DFG) – Excellence Initiative Dritte Säule: 

Internationale Zusammenarbeit

Title: International cooperation

Funding Period: 2009 – 2013

Jeremy C. Smith: PI

National Institute of Health (NIH)– PEER

Title: Program for Excellence and Equity 

in Research

Funding Period: 01/2009 – 01/2014

Jeremy C. Smith: Senior Personnel

National Science Foundation (NSF)

Title: Integration of Computer Simulation 

and Neutron Scattering in the Characterization of Protein Dynamics

Funding Period: 08/2009 – 07/2012

Jeremy C. Smith: PI

Hong Guo: Co-PI

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: Computer Purchase Grant

Funding Period: 09/2009

Jeremy C. Smith: PI

U.S. Department of Energy (DOE) – 

Advanced Scientific Computing Research 

(ASCR)

Title: LAB 08-19 Software Development 

Tools for Improved Ease-of-Use of Petascale Systems

Funding Period: 2009 – 2012

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – OBER/

ASCR

Title: Multiscale Mathematics for the 

Simulation of Complex Biological Systems

Funding Period: 2009 – 2013

Jeremy C. Smith: PI

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: A Systems Biology Approach 

to Study Metabolic and Energetic 

Interdependencies in the IgnicoccusNanoarchaeum System

Funding Period: 2009 – 2011

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Catalytic Conversion of Lignin 

Feedstocks for Bioenergy Applications

Funding Period: 2009 – 2011

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Neutron scattering methodologies 

for the study of protein dynamics

Funding Period: 2009 – 2012

Jeremy C. Smith: PI

U.S. Department of Energy (DOE) – 

Scientific Discovery through Advanced 

Computing (SciDAC)/Biological and 

Environmental Research (BER) Award

Title: Multiscale Mathematics for the Simulation of Complex Biological Systems and 

Application to Lignocellulosic Biomass

Funding Period: 2009 – 2012

Jeremy C. Smith: PI

Xiaolin Cheng: Co-PI

Starting 2010

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: ORNL Science Focus Area “Biofuels”

Funding Period: 2010 – 2013

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER) Science Focus Area (Renewal)

Title: Biogeochemical and Molecular 

Mechanisms Controlling Contaminant 

Transformation in the Environment

Funding Period: 2010 – 2015

Jeremy C. Smith: Co-PI and Task leader

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Photocatalytic Approach to the Degradation of pf Renewable Lignin-Cellulose 

Feedstock for Hydrogen Production

Funding Period: 2010 – 2011

Jeremy C. Smith: Co-PI

96 97
National Institute of Health (NIH)

Title: Georgetown-Howard Universities 

Center for Clinical and Translational 

Science

Funding Period: 2010 – 2015

Jeremy C. Smith: Senior Personnel

National Institute of Health (NIH) – U54

Title: Novel Translational Methodologies 

(NTM)

Funding Period: 2010 – 2012

Jerome Baudry: Task Leader

Jeremy C. Smith: Task Leader

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: Molecular Mechanisms of Bacterial 

Mercury Transformation

Funding Period: 2010 – 2013

Jeremy C. Smith: PI

Hong Guo: Co-PI

U.S. Department of Energy (DOE) – 

Director’s Research and Development 

(R&D) Fund

Title: Incorporating molecular-scale 

mechanisms stabilizing soil organic carbon into terrestrial carbon cycle models

Funding Period: 2011 – 2013

Loukas Petridis: Co-PI

Starting 2012

U.S. Department of Energy (DOE) – 

EpsCOR Implementation Award + 50% 

Univ. Tennessee matching

Title: DE-FG02-08ER46528 Neutron Scattering Research Network for EPSCoR 

States (Renewal)

Funding Period: 2012 – 2015

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – Office 

of Science

U.S. Department of Energy (DOE) – 

Genomics Biological and Environmental 

Research

Title: Dynamic Visualization of Lignocellulose Degradation by Integration of Neutron Scattering Imaging and Computer 

Simulation

Funding Period: 03/2012 – 03/2015

Loukas Petridis: Senior Personnel

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Joining Neutron Scattering and 

Simulations towards Improved Lipid 

Modelling

Funding Period: 2012 – 2013

Xiaolin Cheng: PI

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: High-Performace Computer 

Simulation Study of the Mechanism of 

Nerve Agent Degradation by an Enzymatic 

Bioscavenger

Funding Period: 2012 – 2013

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: Combining neutrons with highperformance computing to produce 

value-added products from lignocellulosic 

biomass

Funding Period: 2012 – 2013

Jerry Parks: Co-PI

ORNL – Laboratory Directed Research & 

Development (LDRD)

Title: High-performance computer 

simulation of nerve agent degradation by 

a catalytic bioscavenger

Funding Period: 2012 – 2014

Jerry Parks: PI

American Chemical Society Petroleum 

Research Fund (ACS-PRF)

Title: Organic Solvent-Specific Gating 

Motions of an Extremophilic Lipase.

Funding Period: 2012 – 2014

Tongye Shen: PI

Starting 2013

National Science Foundation (NSF)

Title: NIMBioS – Center for Sythesis of 

Mathematics and Biology (Renewal)

Funding Period: 10/2013 – 10/2018

Jeremy C. Smith: Senior Personnel

National Science Foundation (NSF)

Title: SI2-SSI: A Productive and Accessible 

Development Workbench for HPC 

Applications Using the Elcipse Parallel 

Tools Platform

Funding Period: 2013 – 2014

Jeremy C. Smith: Subcontractee from 

Univ. Illinois

INTEL Corporation

Title: Porting and Optimization of the 

General-Purpose Molecular Dynamics 

Code GROMACS on Next-Generation 

Intel-Based Computers

Funding Period: 2013 – 2014

Jeremy C. Smith: PI

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: ORNL Science Focus Area “Biofuels” 

(Renewal)

Funding Period: 2013 – 2017

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: Bioenergy Science Center (Renewal)

Funding Period: 2013 – 2017

Jeremy C. Smith: Task Leader

U.S. Department of Energy (DOE) National 

Nuclear Security Administration (NNSA)

Title: Toward rational design of nerve 

agent bioscavengers using QM/MM 

simulations

Funding Period: 2013 – 2014

Jerry Parks: PI

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: ORNL Scientific Focus Area 

Program: Biogeochemical and Molecular 

Mechanisms Controlling Contaminant 

Transformation in the Environment

Funding Period: 2013 – 2015

Jerry Parks: Co-PI 

U.S. Department of Energy (DOE) – 

Director’s Research and Development 

(R&D) Fund

Title: Probing the Structure-Function 

Relationship of Protein Kinase A

Funding Period: 2013 – 2015

Loukas Petridis: Co-PI

U.S. Department of Energy (DOE) – 

Director’s Research and Development 

(R&D) Fund

Title: Structural Biology of Metabolic and 

Signaling Pathways in Plants

Funding Period: 2013 – 2015

Loukas Petridis: Co-PI

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: Bioenergy Science Center

Funding Period: 2013 – 2015

Jerry Parks: Co-PI

ORNL-UT Joint Directed Research and 

Development (JDRD)

Title: Coarse-Grained Modeling of the 

Conformational Dynamics of Signaling 

Protein Complex. 

Funding Period: 2013 – 2014

Tongye Shen: PI

98 99
Starting 2014

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Understanding the principles of 

small peptide binding and inhibition 

of amyloid fibril growth in Alzheimer’s, 

amyloid kidney disease and other 

amyloidosis using high-performance 

computational docking and molecular 

dynamics

Funding Period: 01/2014 – 12/2014

Xiaolin Cheng: Co-PI

National Institute of Health (NIH) – 

National Institute of Arthritis and 

Musculoskeletal and Skin Diseases 

(NIAMS)

Title: Regulation and Function of FGF23

Funding Period: 2014 – 2015

Jeremy C. Smith: Co-PI

National Institute of Health (NIH) 

– National Institute of Allergy and 

Infectious Diseases (NIAID)

Title: Design of Accelerated Acetylcholin-

esterase Reactivators through Mechanistic 

Neutron Diffraction Studies

Funding Period: 07/2014 – 06/2019

Xiaolin Cheng: Co-Investigator

U.S. Department of Energy (DOE) 

– Laboratory Directed Research & 

Development (LDRD)

Title: Functional domains in model 

membranes and protocells probed with 

high performance simulation and neutron 

scattering

Funding Period: 2014 – 2017

Xiaolin Cheng: PI

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – 

Biological and Environmental Research 

(BER)

Title: Adaptive Biosystems Imaging

Funding Period: 2014 – 2016

Jeremy C. Smith: Co-PI

Xiaolin Cheng: Co-Investigator

U.S. Department of Energy (DOE) – Basic 

Energy Sciences (BES)

Title: Center for Lignocellulose Structure 

and Formation

Funding Period: 2014 – 2018

Loukas Petridis: Senior Personnel

National Institute of Health (NIH) 

– National Institute of Allergy and 

Infectious Diseases (NIAID)

Title: Transport across two membranes by 

AcrAB-TolC

Funding Period: 2014 – 2019

Jerry Parks: Co-PI

Jerome Baudry: Co-PI

Jeremy C. Smith: Co-PI

INTEL Corporation

Title: Porting and Optimization of the 

General-Purpose Molecular Dynamics 

Code GROMACS on Next-Generation 

Intel-Based Computers (Renewal)

Funding Period: 2014 – 2015

Jeremy C. Smith: PI

Starting 2015

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: ORNL Science Focus Area “Biofuels” 

(Renewal)

Funding Period: 2015 – 2017

Jeremy C. Smith: Co-PI

U.S. Department of Energy (DOE) – Office 

of Biological and Environmental Science 

(OBER)

Title: Biogeochemical and Molecular 

Mechanisms Controlling Contaminant 

Transformation in the Environment 

(Renewal)

Funding Period: 2015 – 2018

Jerry Parks: Task Leader

Jeremy C. Smith: Co-PI and Task Leader

U.S. Department of Agriculture (USDA)

Title: Zinkicide A Nanotherapeutic for 

HLB

Funding Period: 2015 – present

Loukas Petridis: Co-PI

U.S. Department of Energy – Seed Fund

Title: Overcoming Antibiotic Resistance: 

Neutron Crystallographic and Quantum 

Chemical Studies of a Beta-Lactamase 

Enzyme

Funding Period: 2015 – 2017

Jerry Parks: Co-PI

Starting 2016

INTEL Corporation

Title: Porting and Optimization of the 

General-Purpose Molecular Dynamics 

Code GROMACS on Next-Generation 

Intel-Based Computers (Renewal)

Funding Period: 2016 – 2017

Jeremy C. Smith: PI

U.S. Department of Energy (DOE) – 

Subsurface Biogeochemical Research 

(SBR)

Title: ORNL Scientific Focus Area Program: 

Biogeochemical Transformations at 

Critical Interfaces

Funding Period: 2016 – 2018

Jeremy C. Smith: PI

100 101
Supercomputer Allocations

2011 NCCS Director Discretionary Application: High Performance Computing 

for Rational Drug Discovery and Design, Supercomputing molecular 

discovery of prostate cancer molecular effectors

Organization: National Center for Computational Sciences (NCCS)

Allocation: 5.8 million CPU-hours

Platform: Jaguar supercomputer

PI: Jerome Baudry

2011 INCITE Award: Cellulosic Ethanol: Simulation of Multicomponent 

Biomass System

Organization: ORNL Leadership Computing Facility (OLCF)

Allocation: 30 million core-hours

Platform: Jaguar supercomputer

PI: Jeremy C. Smith

2012 NCCS Director Discretionary Application: Dynamics of the Chemotaxis 

Receptor

Organization: National Center for Computational Sciences (NCCS)

Allocation: 5 million core-hours

Platform: Titan supercomputer

PI: Jerome Baudry

2012 NRBSC/PSC award for ANTON supercomputer: Multi-microseconds 

molecular dynamics simulations of the signaling domain of the bacterial 

chemotaxis receptor

Organization: National Resource for Biomedical Supercomputing 

(NRBSC)/Pittsburgh Supercomputing Center (PSC)

Allocation: 50 thousand node-hours

Platform: ANTON

PI: Jerome Baudry

102

2012 Amazon Cloud Computing Award: Virtual High-Throughput Docking 

using Cloud Infrastructure

Organization: Amazon.com, Inc.

Allocation: 7500 CPU-hours

Platform: EC2 cloud computers

PI: Jerome Baudry

2012 INCITE Award: Cellulosic Ethanol: Simulation of Multicomponent 

Biomass Systems 

Organization: ORNL Leadership Computing Facility (OLCF)

Allocation: 23 million core-hours

Platform: Jaguar supercomputer

PI: Jeremy C. Smith

2012 XSEDE award: Atomistic Simulations on Nuclear Receptor Complex and 

on the Organic Solvent-Dependent Motions of an Extremophilic Lipase

Organization: National Science Foundation (NSF) XSEDE Program

Allocation: 2 million core-hours

Platform: Kraken supercomputer

PI: Tongye Shen

2013 NICS award: High-throughput Docking in Undergraduate Curriculum

Organization: National Institute for Supercomputing Sciences (NICS)

Allocation: 70 thousand CPU-hours

Platform: Kraken supercomputer

Task Leader: Jerome Baudry

103
2013 NCCS Director Discretionary Application: Massive screening for drug 

discovery and toxicity prediction

Organization: National Center for Computational Sciences (NCCS)

Allocation: 5 million core-hours

Platform: Titan supercomputer

PI: Jerome Baudry

2013 INCITE Award: Cellulosic Ethanol: Simulation of Multicomponent 

Biomass Systems 

Organization: ORNL Leadership Computing Facility (OLCF)

Allocation: 78 million core-hours

Platform: Titan supercomputer

PI: Jeremy C. Smith

2013 ASCR Leadership Computing Challenge (ALCC) award: Simulating the 

Structure and Dynamics of Protein Kinase A

Organization: Advanced Scientific Computing Research (ASCR), DOE

Allocation: 4 million processor-hours

Platform: NERSC supercomputer

Task Leader: Loukas Petrides

2014 ASCR Leadership Computing Challenge (ALCC) award: Molecular 

Simulation in Bioenergy

Organization: Advanced Scientific Computing Research (ASCR), DOE

Allocation: 59 million core-hours

Platform: Titan supercomputer

Task Leader: Jeremy C. Smith

2014 ANTON Computing Award: Investigation of long time protein dynamics 

under physiological conditions

Organization: Pittsburgh Supercomputing Center and D. E. Shaw Research

Allocation: 100 thousand node-hours

Platform: ANTON supercomputer 

PI: Xiaolin Cheng

Co-PI: Jeremy C. Smith

104

2014 NCCS Director Discretionary Application: Drugging the undrugable

Organization: National Center for Computational Sciences (NCCS)

Allocation: 10 million core-hours

Platform: Titan supercomputer

PI: Jerome Baudry

2014 XSEDE award: Atomistic Simulations of Proteins on a Lipid Droplet 

Surface

Organization: National Science Foundation (NSF) XSEDE Program

Allocation: 322,215 core-hours

Platform: Texas Advanced Computing Center’s STAMPEDE

PI: Tongye Shen

2016 INCITE award: A generic plant cell wall and its deconstruction for 

bioenergy

Organization: ORNL Leadership Computing Facility (OLCF)

Allocation: 100 million core-hours

Platform: Titan supercomputer

PI: Jeremy C. Smith

2016 Computing Award: Molecular Dynamics Simulations of Protein 

Dynamics and Lignocellulosic Biomass

Organization: National Energy Research Scientific Computing Center (NERSC)

Allocation: 12 million core-hours

Platform: Edison/Cori supercomputers at NERSC

PI: Jeremy C. Smith

2016 XSEDE award: A High-Throughput Computational Method to Detect 

Allostery in Biomolecular Complexes

Organization: National Science Foundation (NSF) XSEDE Program

Allocation: 243,484 core-hours 

Platform: Texas Advanced Computing Center’s STAMPEDE

PI: Tongye Shen

105
Awards

2009

Julia Cooper

Award title: ORNL Biosciences Divisional Administrative Award

Description: Julia received the ORNL 

Biosciences Divisional Administrative Award in for her role in the establishment and continued operation of 

CMB.

2011

Jerome Baudry

Award title: Outstanding Teaching 

Award for Junior Faculty

Description: Jerome Baudry received 

the Outstanding Teaching Award for 

Junior Faculty 2011 at the University 

of Tennessee Knoxville. Through this 

award, the BCMB faculty acknowledges faculty members who demonstrate 

a commitment and excellence in 

teaching at the undergraduate level.

Sally Ellingson

Award title: NSF funded Broader Engagement Grant

Award title: NSF funded Scholarship 

/ ACM student research competition 

Grace Hopper Celebration

Description: Sally Ellingson received 

an NSF funded Broader Engagement 

grant to attend Supercomputing 11 

in Seattle, WA, and a NSF funded 

scholarship to attend the Grace Hopper Celebration 11 in Portland, OR to 

present her work in the Baudry lab on 

developing Cloud strategies for virtual 

docking.

2012

Chelsea Knotts

Award title: The Torchbearer Award 

by the University of Tennessee 

Description: Chelsea, a former BCMB 

major and long-term undergrad research student in the Baudry lab, who 

was also a Haslam Scholar and Lady 

Vols student athlete, was awarded 

with the highest student honor conferred by the University of Tennessee: 

The Torchbearer Award. This award 

was presented to Chelsea particularly 

for her outstanding social work in the 

Knoxville community. She led hundreds of UT students in an effort to 

end chronic homelessness, particularly in the Fort Sanders neighborhood. She began a running group for 

homeless men and women, organized 

a 5K benefit run, and regularly befriends people who are without shelter 

and social stability.

2013

Jerry Parks and Jeremy C. Smith

Award title: UT/Battelle Award for Scientific Achievement

Award title: ORNL Director’s Award 

for Outstanding Team Achievement

Description: Jerry Parks and Jeremy 

Smith of CMB are part of the Mercury 

SFA Team that won both the 2013 UT/

Battelle Award for Scientific Achievement and the 2013 ORNL Director’s Award for Outstanding Team 

Achievement.

Sally Ellingson

Award title: The Chemical Computing Group (CCG) Excellence Award for 

Graduate Students by the American 

Chemical Society (ACS)

Description: Sally has been awarded 

for her work in the Baudry lab with 

the American Chemical Society CCG 

award by the Computers in Chemistry 

division of the ACS. The CCG award 

is given to no more than 10 graduate students nationwide every year to 

recognize the quality and significance 

of their research in the general field of 

computational (bio) chemistry.

Sally Ellingson

Award title: The University of Tennessee Science Alliance Award for Graduate Students

Description: Sally has been awarded 

for her work in CMB with the Science 

Alliance Award. The Science Alliance 

has a mission to expand cooperative 

ventures in research with the Oak 

Ridge National Laboratory and recognizes achievements of UTK graduate 

students.

2014

Xiaohu Hu and Jason Harris

Award title: The University of Tennessee Science Alliance Award for Graduate Students

Description: Xiaohu and Jason were 

awarded for their research works in 

CMB with the Science Alliance Award. 

The Science Alliance has a mission 

to expand cooperative ventures in 

research with the Oak Ridge National 

Laboratory and recognizes achievements of UTK graduate students.

Xiaolin Cheng

Award title: Superb Performance 

Award by the ORNL Computer Science and Mathematics Division

Description: Xiaolin was presented 

with this award for his outstanding 

research work carried out at CMB and 

ORNL.

106 107

Jerry and Jeremy in the 

group photo with ORNL 

director Tom Mason 

during the award

ceremony for ORNL 

Director’s Award

for Outstanding Team 

Achievement
Designed and edited by: 

Thomas Splettstößer

www.scistyle.com

Composed and edited by: 

Xiaohu Hu, Jeremy C. Smith

Contact

Center for Molecular Biophysics, 

University of Tennessee / 

Oak Ridge National Laboratory 

1 Bethel Valley Road, Bldg 2040

Oak Ridge , TN 37830-6309

Tel.: +1 865 574 6308 

Fax: +1 865 576 7651

Email: davislb@ornl.gov (Lora Davis)