Impact on Nucleosome Dynamics via Histone Variants
and Post-Translational Modifications
Hidetoshi Kono
Group Leader, Molecular Modeling and Simulation Group
Quantum Beam Science Center, Japan Atomic Energy Agency
JAPANDynamics of the nucleosome and accessibility of nucleosomal DNA play critical roles in many nuclear
processes. The nucleosome is the fundamental structural unit of chromatin, comprising a histone octamer
formed by two copies each of the four core histones H3, H4, H2A, and H2B and approximately 150bpof DNA that is wrapped around the octamer. There are variants of these histone proteins. In humans, thevariants include H3.1 (canonical), H3.2, H3.3, H3T, H3.5, H3.X, H3.Y, and CENP-A, each of which islikely to have a specific role. However, their crystal structures look similar to each other and thedynamics of them has not been fully understood. In addition, post-translational modifications of histones
have been known to affect gene regulation, but it is still unknown how they affect changes of the structureand dynamics of chromatin. Using supercomputers including K computer, we are currently investigating
the following topics to understand the molecular mechanism: 1) how nucleosomal DNA is unwrappedfrom the histone core, 2) if there are differences in DNA unwrapping process among nucleosome types
composed of histone variants and 3) how acetylation on histone tails impacts nucleosome structure.
We have found that different nucleosomes showed different free energy profiles for DNA unwrapping.
The canonical nucleosome which has H3 histones showed that a state where nucleosomal DNA was fully
wrapped around the histone core was the most stable while in the nucleosome having a H3 variant,
CENP-A, was in a state where DNA at entry and exit regions were partially unwrapped. We also observed
the asymmetric DNA unwrapping: only either of ends of DNA was unwrapped from the histone core
after about 35bp of DNA on both sides were dissociated from the core. In addition, our simulation
indicated that an acetylation of H3 tail induced α-helix formation and enhanced DNA flexibility, which
may be the first step of the disruption of nucleosome.

Figure1.
Nucleosomal DNA unwrapping process simulated with K computer. For clarity, only DNA
and the histone core (blue) are shown.

Millisecond Dynamics of RNA Polymerase II
Translocation at Atomic Resolution
Xuhui Huang
Associate Professor, Department of Chemistry
Division of Biomedical Engineering
The Hong Kong University of Science and Technology
HONG KONG

Transcription, the synthesis of RNA from a complementary DNA template, plays a crucial role in cellularregulation, including differentiation, development, and other fundamental processes. We performed
molecular dynamics (MD) simulations to understand the thermodynamics of the major states oftranscription elongation and the kinetics of transitioning between them for RNA polymerase II (pol II).
In this talk, I will discuss our recent results on simulating pol II Translocation and other functional
conformational changes of this enzyme. The main challenge for MD simulations is to reach biologically
relevant timescales, which are orders of magnitude longer than most atomistic simulations. In order toovercome this timescale gap, we have developed a novel algorithm, Hierarchical Nystrom Extension
Graph method, to construct Markov State Models to extract long timescale dynamics from shortsimulations. We reveal that RNA polymerase II translocation is driven purely by thermal energy anddoes not require the input of any additional chemical energy. Our simulations show an important role forthe bridge helix: Large thermal oscillations of this structural element facilitate the translocation by
specific interactions that lower the free-energy barriers between four metastable states. Among thesestates, we identify two previously unidentified intermediates that have not been previously captured bycrystallography. The dynamic view of translocation presented in our study represents a substantial
advance over the current understanding based on the static snapshots provided by X-ray structures oftranscribing complexes.

Molecular Simulation for Antibody Drug Development

Keiko Shinoda
Project Assistant Professor, LSBM, RCASTThe Univ. of TokyoJAPANThe use of antibodies that bind specifically to antigens has achieved considerable success in cancer
therapy in recent years. Epiregulin (EPR) is one such promising therapeutic target protein for advanced
cancer. EPR is a member of the epidermal growth factor family, and is overexpressed on various cancer
cells such as in colon, ovarian, breast and prostate cancer as well as in pancreatic cancer, which is difficult
to detect in its early stages. Here we developed an anti-EPR murine antibody, 9E5. In advanced cancer,
since the cancer spreads inside the cancer tissue, it is important that the molecular weight of antibody,while including the recognition site, is small to penetrate the cancer tissue. However, a small size will
result in low affinity between the antibody and antigen. Humanization is also important to reduce murine
antibody immunogenicity. In order to humanize an antibody and to increase affinity, it is necessary tomodify the antibody. To this end, we first analyzed the structures of apo 9E5 (EPR-free 9E5) and its
complex with EPR (this complex is hereafter referred to asthe “trans-Complex”) by X-ray crystallography. It was
found that a proline at residue 103 in the third
complementarity-determining region (CDR) of the heavy
chain changed its conformation from cis to trans by bindingto EPR. The cis-trans isomerization is known to occur
slowly, with the duration ranging from several seconds tohours. We then aimed to determine whether EPR forms astable complex with apo 9E5 in which Pro103 is in the cis
conformation before the isomerization occurs. In order toidentify an EPR-binding structure with apo 9E5, weconducted extensive binding MD simulations using K
computer. From the simulations, an EPR-binding structure
similar to the “trans-Complex” was found. We thenconducted additional long MD simulations to determine
Figure 1. Epiregulin (orange) and thethe stability of this structure. This structure changed torecognition site of antibody 9E5 consistinga more similar structure to the “trans-Complex” (thisof heavy chains (yellow) and light chainscomplex is referred to as the “cis-Complex”) and(light blue). CDRs are indicated in red.
remained stable during total 16 μs simulation. However,there were several major differences between the “cisComplex” and the “trans-Complex” in the short-interaction energy, fluctuation of the CDR H3 loop, anddynamics of water at the EPR-9E5 interface.

The Path to Exascale Molecular Simulation:
Parallelization, Acceleration, and Ensemble Simulation
Techniques to Understand Biological Macromolecules
Erik R. Lindahl
Professor of Theoretical Chemistry, Department of Biochemistry &Biophysics, Stockholm University, SWEDEN
Professor of Theoretical Biophysics, Science for Life Laboratory,
KTH Royal Institute of Technology, SWEDEN

Supercomputers have been a revolution to modern simulation research, and yet they also lead to gigantic
challenges. As computers have grown in size, power, and parallelism, researchers have resorted tosimulating larger and larger systems simply to achieve good scaling to justify access to the resources.
This works well in many engineering disciplines, but in biomolecular life sciences we are typicallyconstrainted by the size of the target molecule: A membrane protein system might need 200,000 atoms,
but very few (if any) systems need atomic detail with millions or billions of particles. This makes
classical molecular simulation an extremely difficult problem for supercomputers, since improved
performance depends on decreasing the iteration time for an entire step to hundreds of microseconds.
Here, I will describe some of the efforts we have done to achieve this in GROMACS over the last decade,and how we are increasingly relying on large-scale fully automated ensembles of several simulations,even for complex systems such as membrane proteins. I will present our new COPERNICUS frameworkthat interacts with GROMACS to enable usage of a number of important adaptive sampling algorithms
such as free energy calculations, markov state models, and the string method using swarms. This
combines the strongest points of traditional supercomputers with throughput-focused techniques such as
distributed computing, and will make it possible to use tens to hundreds of thousands processors
efficiently for biomolecular simulation. Finally, I will discuss how we apply these methods to studies ofion channels, and how the simulations make it possible to probe both structure and dynamics that are
simply out of reach to current experimental methods.

Figure 1. The ion-conducting pore in a ligand-gated ion channel. Ensemble molecular simulations
make it possible to predict how the response will be left- or right-shifted (as a function of the voltage),depending on the pH, mutations, and binding of allosteric modulators.

Can an Integrated Model of the Basal Ganglia-ThalamoCortico-Spinal Network and the Musculoskeletal System
Reproduce Healthy and Pathological Motor Behaviors?
Kenji DoyaProfessor, Neural Computation Unit
Okinawa Institute of Science and Technology Graduate University
JAPANUnder the Strategic Program in Supercomputational Life Science, we perform hierarchical integrated
simulation of the brain, the spinal cord, muscles and the skeletal system for understanding themechanisms of movement disorders and for identifying therapeutic targets. Our team at OIST focuses on
constructing realistic spiking neural network models of the basal ganglia, the thalamus, and the motor
cortex for elucidating the mechanism of Parkinsonian tremors. The specific aim is to clarify the dynamic
mechanism for oscillations in different frequencies observed in the brain and the musculoskeletal system.
The present network model includes five types of neurons in the basal ganglia, four types in the thalamus,
and eight types in five layers of the motor cortex. They form a topographic basal ganglia-thalamo-corticalloop circuit and the size of the network is matched to the entire basal ganglia and 1.6x1.6 mm2 corticalsurface of the rat brain. Each sub-network is implemented by NEST (www.nest-initiative.org) and theentire network, consisting of more than 3 million neurons, is connected by MUSIC(software.incf.org/software/music) to run in parallel on K Supercomputer. The model reproduces 8 to 15
Hz oscillation in the basal ganglia and sub-harmonic responses in the thalamus and the motor cortex.

Figure 1. The basal ganglia-thalamo-cortical network model combined with the spinal andmusculoskeletal models. Insets: power spectra of activities in a: globus pallidus internal segment (GPi),b: thalamo-cortical (TC) neurons, and c: cortical pyramidal tract (PT) neurons.

Sensing Positive Versus Negative Reward Related Signals
in the Direct and Indirect Basal Ganglia Pathways - A
Computational Investigation of Read-Out Mechanisms
Jeanette Hellgren Kotaleski
Professor, Science for Life Laboratory
School of Computer Science and Communication
KTH Royal Institute of Technology
SWEDEN

To understand the inner workings of the brain, it is necessary to integrate data from the molecular level up
to the systems level. Here computational modeling and simulations provide important tools. Some recent
insights resulting from kinetic modeling on the subcellular level are reviewed, and it will be demonstrated
how modeling may help when extrapolating to in vivo like conditions in the search for the cellularmechanisms involved in basal ganglia dependent learning and decision making. Transient positive andnegative changes in the striatal dopamine (DA) concentration are considered to encode a reward prediction
error (RPE) in reinforcement learning tasks. This phasic DA change occurs concomitantly with a transient
dip in striatal acetylcholine (ACh) while other neuromodulators like adenosine (Adn) changes more slowly.
There are abundant G protein coupled receptors (GPCR) linked to adenylyl cyclase for theseneuromodulators in striatal medium spiny neurons (MSNs), but little is known about the way in which thesefast transients might affect the cAMP/PKA signaling axis which is activated mainly by G olf and inhibitedby G i/o in MSNs. The goal of our computational study was to investigate how transient changes in theseneuromodulators are detected and read out by MSNs. We have found that in direct pathway MSNs
expressing D1 receptors the ACh dip, operating via M4R/G i/o , could function as a gate for the activation ofPKA triggered by the D1R/G olf . On the other hand, in indirect pathway D2R expressing MSNs the DA dip,
associated with reward omission and operating via D2R/G i/o , produced significant PKA activation due tothe presence of a high Adn tone acting through A2aR/G olf . Our study highlights the common pattern of G i/oand G olf interactions in both the MSN types, where a dip in G i/o acts as a gate for the G olf activation. These
results together with earlier experimental and computational studies also corroborate the functional
dichotomy between D1R- and D2R expressing MSNs in terms of the RPE sign (positive or negative) which
could elicit significant PKA activation, and which thus would facilitate long-term potentiation in thecortico-striatal synapses. Our simulation experiments furthermore help to identify during whatcircumstances the DA transients are expected to promote synaptic plasticity during reward dependent
learning paradigms.

Breast Cancer Phenotype Characterization by NetworkBased Omics Data Analysis
Sun Kim
Professor, Computer Science and Engineering;
Director, Bioinformatics InstituteSeoul National University
KOREA

Sequencing technology is routinely used to produce a genome-wide measurement of biological events,
known as omics data. Omics data provides a complete picture of genetic and epigenetic events in thewhole cell, so it is invaluable for research in medical and biological sciences to characterize biologicalmechanisms underlying phenotypes, e.g., cancer subtypes. Unfortunately, integrating omics data is achallenging data mining task and we need to employ novel approaches. In particular, association ofdifferent genetic and epigenetic events increases the complexity of the computational problem
dramatically and we need powerful computational techniques. Network based approaches are mostpromising since associations are naturally modeled as edges in the network. In this talk, we show how
network mining can be useful to characterize phenotypes and biological mechanisms underlying breast
cancer phenotypes. Three phenotypes in breast cancer are characterized by using protein-protein
interaction (PPI) networks, TF-target gene networks, and miRNA-target gene networks, respectively.
The first study is to show that decomposition of biological pathways using PPI networks can effectively
enhance patient survival prediction in breast cancer. Recent studies show that pathway based cancer
analysis is effective. However, some pathways consist of several hundred genes, thus these pathways
involve multiple biological functions. We used PPI information to decompose pathways into functionallycoherent components and used them for breast cancer patient survival prediction. The second study is toshow that sub-network mining of TF-target gene networks can effectively classify breast cancer grades.
This is to investigate on biological functions underlying breast cancer tumor grades in terms oftranscriptional mechanisms and distinct biological functions of TF target genes. The third study is toshow that use of miRNA-target gene networks can be useful for characterizing breast cancer subtypes
and roles of miRNAs in breast cancer subtypes. In this study, to leverage characteristics of sequencing
data that can measure transcription quantities, we used a novel algorithm that considers miRNA-target
gene split ratios to distribute transcription quantities of miRNAs to their target genes in an optimal way.
The analysis results not only suggest miRNAs that are related to tumor subtypes but also regulatory
mechanisms by miRNAs in different breast cancer subtypes.

Supercomputers for Cancer Research and Cancer Big
DataSatoru Miyano
Professor, Human Genome Center, The Institute of Medical Science,
The University of Tokyo
JAPANIn our cancer research, we have been using K computer of RIKEN and the supercomputer system
Shirokane 1&2 (225 TFLOPS, 3PB Lustre file system, 2009-2015) and Shirokane 3 (440 TFLOPS fromApril 2015) with 12PB Lustre file system together with a nearline storage (1PB) connected to the IBM
tape archive system extensible to 100PB of our Human Genome Center. In this talk, we will first present
highly parallel software applications developed in the project “HPCI Strategic Programs for Innovative
Research Field 1 “Supercomputational Life Science” (2011- 2015) and the Grand Challenge Project forLife Science “Next-Generation Integrated Simulation of Living Matters (2006-2012). The first series ofapplications include various gene network estimation software applications on both supercomputers. The
second is a series of software applications and pipelines for cancer genome analysis on thesesupercomputers. The power of K computer led us to a challenge and the Shirokane seires efficiently
accelerated our cancer genome sequence data analysis. We present some innovative contributions incancer systems biology which could not be achieved without K computer and important discoveries oncancer genomes which were made by Professor Seishi Ogawa with the Shirokane series. The talk also
includes our story of struggles behind.