Chapter 18

Computational Structural Biology
Research Unit
18.1

Members

Florence Tama (Unit Leader)
Atsushi Tokuhisa (Research Scientist)
Miki Nakano (Post-doctoral researcher)
Sandhya Tiwari (Post-doctoral researcher)
Sachiko Kikumoto (Assistant)
Yumeno Kusahara (Assistant)

18.2

Research Activities

Biological molecular complexes such as proteins and RNAs are of great interest in the area of molecular biology as they are involved in cell replication, gene transcription, protein synthesis, regulationof cellular transport and other core biological functions. Those systems undergo large conformationaltransitions to achieve functional processes. Therefore characterization of structures of these macromolecular complexes is crucial to understand their functional mechanisms, and play an importantrole in the development of new drugs to treat human disease.
Experimentally, X-ray crystallography has been the primary tool to study protein conformations,providing high-resolution structures. Cryo electron microscopy (EM) has provided, although at
lower-resolution, critical information on structure and dynamics of large biological molecules. Morerecently, efforts like in Riken/SPring 8 have focused on developing intense X-ray free-electron laser(XFEL) light sources, which offer a new possibility to image single biological macromolecules. Since
crystallization is not necessary for such a protein structure analysis, it would be possible to investigate
the structure of macromolecular complexes and proteins under various physiological conditions or toobserve elementary steps of a biochemical function. However, at the current experimental condition,
it cannot achieve atomic level resolution such as obtained by X-ray crystallography.
Computationally, methods have been developed to predict structures from low-resolution datasuch as cryo-EM either using rigid body fitting or flexible deformations of known atomic structures.
In addition, even when structures of the molecules are unknown, atomic models can be predictedusing homology modeling and ab initio predictions. While ab initio prediction still remains difficult
for large proteins, success in predicting small proteins have been observed. Finally, algorithms toanalyze protein/proteins interactions also have shown success in predicting proteins complexes.
Our research focuses on the development of computational tools to study biological systems,
more specifically to help in their 3D structural determination using various experimental techniques
and to analyze their potential interactions with small molecules in order to design new drugs. The
ultimate line of our interdisciplinary research is too bring experimental data as obtained from Xray, cryo-EM and XFEL with development and applications of computational tools through the K153

Chapter 18. Computational Structural Biology Research Unit

Figure 18.1: Refinement of 2D raw data
computer to acquire knowledge on the structure of a physiologically important protein complexes
that are unattainable with existing experimental techniques, and to contribute to development ofdrug design and medical treatment in collaboration with pharmaceutical companies.

18.3

Research Results and Achievements

18.3.1

Refinement 2D raw data

Following discussions with experimental groups, in this fiscal year, we have been working on refinement of real 2D data. Diffraction patterns from X-ray Free Electron Laser is approximately theFourier transform of 2D projection image of the target, however the detectors can observe only theintensity and phase cannot be determined. Reconstruction of 2D projection images requires thephase, and a common approach is ”phase reconstruction”, in which numerical algorithm is used toestimate the phase. To further improve agreement (R-factor) with experimental data, we developed
a method that utilizes Limited memory Broyden Fletcher Goldfarb Shanno B Nonlinear Optimizer
and Basinhopping algorithm. Such refinement will allow for a more accurate interpretation of thereal 2D data derived from XFEL experiments (see Figure 18.1)

18.3.2

3D reconstruction from XFEL data

In order to restore the 3D real structure of the molecule from the diffraction patterns obtained byXFEL experiments, computational algorithms are necessary as one needs to estimate the laser beamincidence angles to the molecule and retrieve the phase information. We are developing a program
package for XFEL analysis based on XMIPP, which is commonly used for image processing of singleparticle 3D cryo electron microscopy. Since XMIPP is designed to work with 2D data in real space,some of the routine were modified to deal with 2D diffraction data in Fourier space. The overall
protocol used for 3D reconstruction is shown in Figure 18.2.
The protocol was tested using simulated diffraction images from the crystal structure of a translation termination complex formed by the Thermus thermophilus 70S ribosome bound with releasefactor RF2 (PDB ID = 4V67). The algorithm is able to estimate relative orientations of the diffraction images. The resulting reconstructed 3D structure is shown in Figure 18.3.

18.3.3

Fast image filtering procedure

For current XFEL experiments, the number of diffraction data is limited. Therefore, we have beendeveloping new approaches to obtain structural and dynamical information by combining such lowresolution and limited information from XFEL data with other data, such as X-ray structure and154

Chapter 18. Computational Structural Biology Research Unit

Figure 18.2: 3D reconstruction from XFEL

Figure 18.3: 3D XFEL reconstruction for the ribosome. (a) Electron density map of the ribosome
created from the X-ray structure. (b) Electron density map obtained using the protocol describedin Fig 18.2. (c) and (d) are cross section views of the structure factor (structure in Fourier space)for (a) and (b) respectively155

Chapter 18. Computational Structural Biology Research Unit

Figure 18.4: Fast image filtering procedurecomputational tools. We published algorithms to identify a conformation, among several candidates,that agrees with XFEL data.
To perform ab initio modeling, a large number of conformations need to be explored to find a goodmodel that agrees with the data. Thus it is important that the agreement between the models andthe target diffraction can be evaluated quickly. In our previous study, diffraction patterns were firstsimulated from the candidate models, and then the simulated diffraction patterns were comparedwith the target pattern. While this approach is accurate, it is also time consuming; the simulationof X-ray diffractions is mathematically complex and 2D image comparisons need to be performedfor different alignment angles.
To speed up this process, we have been developing an algorithm to filter out unpromising models
using information reduction technique. In this approach, from the original 2D target image, 1D
radial profile (average over circles) is calculated. Then for each model, corresponding 1D profile iscalculated and compared against the target 1D profile. Such 1D profile can be quickly calculated
directly from mass distribution, and 1D profile comparison is also fast. Since 1D approach is anapproximation, this is used as a preliminary filtering process to reject the models with low 1D score.
Then, we evaluate more precise similar score for the remaining models using 2D images to select thebest models. Recently, we developed a new equation that can increase the sensitivity of the filtering:Z ∞˜ =I(q)p(s)J0 (2πqs)ds(18.1)
0

where I(q) is the 2D profile used for comparison, J is a Bessel function, and p(s) is 2D distance
distribution function. This new formula replaces conventional Debye equation used for SAXS analysis
and more accurate for 2D diffraction image analysis such as used here. We have tested this approach
for a biological molecule to confirm the validity of this formalism (see Figure 18.4)

18.3.4

DNA solvation properties

Under physiological conditions, where various biomolecules and other components are present, DNAstrands may adopt many different structures in addition to the canonical B-form duplex. Such effects
of molecular crowding on thermal stabilities of DNA structures maybe associated with the propertiesof the water molecules around the DNAs.
Using MD simulations, we studied the thermodynamic properties of water molecules around a
hairpin duplex and a G-quadruplex to understand how cosolutes affect the thermal stability of DNAstructures. Our findings suggest that differences in the hydration shell structure around DNAs are
one factor that affects the thermal stabilities of DNA structures under the crowding conditions. In
addition, thermodynamic properties of water molecules around single- and double-stranded DNAs
were investigated. Upon the formation of double-stranded DNA, thermodynamics features of thewater molecules is primarily changed in the minor groove. Free energies of water molecules around156

Chapter 18. Computational Structural Biology Research UnitFigure 18.5: Different forms of DNA
double-stranded DNA are lower than those around single-stranded DNA even in the second andthird hydration shells

18.4

Schedule and Future Plan

We are planning to continue to develop tools to analyze XFEL data to obtain structural informationof biological molecules. In particular, we intend to improve the 3D reconstruction protocol proposedfor XFEL data by providing more informed initial reference volume. To achieve such a goal, we plan
to maintain a database of shapes and associated diffraction patterns found in biological molecules.
In addition, efficiency of the protocols in terms of speed will be addressed. We will also apply ourtools to experimental data. In parallel, we will continue the development of algorithms to extract
information from a limited number of low-resolution data, since only limited experiments might befeasible for some systems even with the development of experimental techniques. Ab-initio modelingof sample shapes based on the use of simplified representations of the biological molecules as well as
optimization procedure will be further developed taking advantage of the newly fast filtering scheme.
As experimental data from cryo-EM and for XFEL will continue to grow in numbers, analysis
of such big dataset will increase the necessity of high performance computing. We aim to utilize
K and post-K to break the limitation of current processing power to obtain new level of structural
information of biological complexes from EM and XFEL data. For this goal, we plan to develop
softwares to analyze data to obtain structural models that can utilize computers in different sizes
such as cluster and supercomputer. By sharing the software and results from structural modelingwith other research institutes, we aim to contribute to the structural biology community.
As our research focuses on developing computational tools to analyze low-resolution experimental
data, we intend to establish collaborations with experimental groups in Japan and abroad in orderto study structure, function and dynamics of biological molecules.

18.5

Publications

Journal Articles[1]Tokuhisa A et al. “Hybrid approach for structural modeling of biological systems from X-ray
free electron laser diffraction patterns”. In: J. Struct. Biol (2016).
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Chapter 18. Computational Structural Biology Research Unit
[2]

Nakano M et al. “Thermodynamic properties of water molecules in the presence of cosolutedepend on DNA structure: a study using grid inhomogeneous solvation theory.” In: Nucleic
Acids Res (2015).

Conference Papers[3]

Jin Q. et al. “Macromolecular Dynamics by Hybrid Electron Microscopy Normal Mode Analysis Macromolecular Dynamics by Hybrid Electron Microscopy Normal Mode Analysis”. In:
Microscopy and Microanalysis 20:1218-1219. 2014.

Invited Talks[4]

F. Tama. Hybrid approaches to characterize structure and dynamics of biomolecular systems
from single molecule experiments. Cutting Edge of Technical Innovations in Structural andSystems Biology, Kyushu University. 2015.

[5]

F. Tama. Hybrid approaches to characterize structure and dynamics of biomolecular systems
from single molecule experiments. Symposium: Integrative Structural Biology with HybridMethods, Osaka. 2015.

[6]

F. Tama. Hybrid approaches to characterize structure and dynamics of biomolecular systems
from single molecule experiments. The 3rd International Symposium on Transformative BioMolecules, Nagoya University. 2015.

[7]

F. Tama. Hybrid approaches to characterize structure and dynamics of biomolecular systems
from single molecule experiments. Large-scale biological simulations using supercomputers,
Kobe. 2015.

[8]

F. Tama. Hybrid approaches to characterize structure and dynamics of biomolecular systems
from single molecule experiments. American Chemical Society Meeting. 2016.

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