Chapter 9

Computational Biophysics ResearchTeam

9.1 Members

Yuji Sugita (Team Leader (Concurrent))**

Osamu Miyashita (Senior Research Scientist)

Jaewoon Jung (Research Scientist (Concurrent))**

Chigusa Kobayashi (Research Scientist)

Yasuhiro Matsunaga (Research Scientist)

Motoshi Kamiya (Postdoctoral Researcher)

Koichi Tamura (Postdoctoral Researcher)

Hiromi Kano (Assistant)*

Hiraku Oshima (Postdoctoral Researcher)*

Suyong Re (Research Scientist)*

Takaharu Mori (Research Scientist (Concurrent))**

Isseki Yu (Research Scientist (Concurrent))**

Kiyoshi Yagi (Research Scientist (Concurrent))**

Ai Nittsu (Special Postdoctorial researcher (Concurrent))**

Takao Yoda (Visiting Scientist) ***

Mitsunori Ikeguchi (Visiting Scientist)****

Naoyuki Miyashita (Visiting Scientist)*****

Michael Feig (Visiting Scientist)******

* The main affiliation of these people is Laboratory for Biomolecular Function Simulation, ComputationalBiology Research Core, RIKEN Quantitative Biology Center.

** The main affiliation is RIKEN Theoretical Molecular Science Laboratory.*** The main affiliation is Nagahama Bio Institute.**** The main affiliation is Yokohama City University.***** The main affiliation is Kinki University****** The main affiliation Michigan State University

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9.2 Research Activities

We mainly have developed GENESIS (Generalized Ensemble Simulation system) for molecular dynamics sim-ulations. GENESIS is highly parallelized on K and other massively parallel supercomputers, and has a lotof enhanced conformational sampling methods. In March, 2014, we provided the first pre-released version ofGENESIS as free software under the license of GPLv2 on the team website. In this fiscal year, we updatedGENESIS with better performance and with advanced algorithms. We also added new functions to enlarge theapplication range of GENESIS. These updates are released as GENESIS 1.1.

9.3 Research Results and Achievements

9.3.1 Release of GENESIS 1.1

GENESIS is aimed at enlarging system size and simulation time by adopting highly parallelized schemes and en-hanced conformational sampling algorithms. We have developed efficient algorithms and implemented functionsafter release of GENESIS 1.0. The new version, GENESIS 1.1, contains several significant updates of functions,models, and computations. The all-atom and physical-based/structure-based coarse-grained potential energyfunctions used in AMBER and GROMACS packages now become available in addition to CHARMM energyfunctions. The performance of GENESIS 1.1 has been improved compared to the previous version by (1) in-troducing multiple time step integration based on reversible reference system propagator algorithm (RESPA),(2) enabling GPUs on hybrid (CPU+GPU) computers, (3) enabling mixed-precision floating points. GENESIS1.1 has better performance than GENESIS 1.0, showing good parallel scaling up to 16000 nodes for a 28.8 Msystem (Figure 9.1). The new version of GENESIS also has more enhanced sampling calculations, including thestring method and replica-exchange umbrella sampling with off-lattice collective variables. The new features ofthe updates increase the usefulness and power of GENESIS for modeling and simulation in biological research.

Figure 9.1: Benchmark of GENESIS on K computer (28.8 M atoms system)

9.3.2 Increasing of parallel efficiency of GENESIS by multiple program/multiple(MPMD) data scheme

To increase the parallel efficiency of MD, we have developed multiple program/multiple data (MPMD) schemewhich separates processors responsible for real space and reciprocal space calculations. The new method showsbetter performance for very large number of processors. It also increases the available number of processors.Based on this, we developed multiple time step integration where communication-intensive part is skippedregularly during MD simulations. The new method is tested on K computer, and 60 % speed up is investigatedfor a 28.8 M atoms system (Figure 9.2).

9.3. RESEARCH RESULTS AND ACHIEVEMENTS 99

Figure 9.2: Benchmark of MPMD on K computer (28.8 M atoms system)

9.3.3 Machine learing approach for combining single-molecule experiments andsimulations

Single-molecule experiment and molecular dynamics (MD) simulation are indispensable tools for investigatingprotein conformational dynamics. The former provides single-molecule time-series data, such as donor-acceptordistances, while the latter gives atomistic motions, though often biased by model parameters. We have developeda machine learning algorithm to link the two approaches and construct an atomically detailed and experimentallyconsistent model of protein dynamics. It is applied to the folding dynamics of a dye-labeled WW domain of theformin-binding protein1. MD simulations over 400 microseconds led to an initial Markov state model, whichwas then refined using single-molecule F旦 rster resonance energy transfer (FRET) time-series data throughhidden Markov modeling. The refined model reproduces experimental FRET efficiency and features hairpin 1 inthe transition-state ensemble, consistent with mutation experiments. Our machine learning process provides ageneral framework applicable to investigating conformational transitions in other proteins (Figure /reffig:fig3).

Figure 9.3: Schematic of our semi-supervised learning approach. (a) Current approach comprises two steps:As the first step, an initial Markov State Model data is constructed only from simulation data by simpleycounting transitions between conformational states. (b) In the second step, transition probabilities (depictedwith arrows) are updated through the unsupervised learning from experimental time-series data

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9.3.4 Development of generalized replica-exchange with solute tempering (gREST)

Conformational search is still one of the important problems in molecular dynamics (MD) simulations ofbiomolecules. We developed a new method for the conformational search, which we call gREST (generalizedreplica exchange with solute tempering). This method allows us flexible separation of solute and solvent energyterms, which significantly reduces the required number of replicas in comparison with conventional REST. Weapplied this method to a vast conformation search involving folding events of TrpCage mini-protein. Our newmethod successfully found the folded state in comparable timescale as conventional REST, while the compu-tation cost was half of the conventional one. The free energy map also converged rapidly in gREST due tothe decreased number of replicas and more efficient random walk performance in parameter space (Figure /ref-fig:fig4).

Figure 9.4: RMSD time series of TrpCage from conventional REST and gREST trajectories. The initial structure(top-left) was unfolded state. Ten and five replicas were employed for conventional REST (REST2) and gREST,respectively

9.3.5 Computational modeling of the ATP-bound outward-facing form of a hemeimporter

Bacteria infected on a human body take iron as a nutrient from the host in the form of heme iron. As heme ironcannot pass through intact cell membrane of bacteria, bacteria prepare unique machine in its cell membrane,and efficiently absorb heme iron. This molecular machine is called heme importer. Heme importer belongs to thelarge family of type-II ATP-binding cassette (ABC) transporter. Transport of heme across the cell membraneinvolves large and global conformational changes of the protein during which two ATPs are consumed. Type-IIABC transporter to which heme importer belongs is believed to follow the alternating access mechanism wherethe transporter can alternate between an inward-facing (IF) form and an outward-facing (OF) one. Recently,crystal structures of bacterial heme importer in the ATP-free inward-facing form have been solved. Based on thestructures and biochemical experiments, a molecular mechanism for heme transport cycle was proposed. In thisstudy, computational modeling approach was adopted to model the ATP-bound OF form of the heme importerand thereby complement the molecular model of the transport cycle. An iterative remodeling approach yieldeda structurally stable atomistic model and revealed a gating mechanism for the OF form (Figure /reffig:fig5).

9.4. SCHEDULE AND FUTURE PLAN 101

Figure 9.5: Gating mechanism of OF

9.4 Schedule and Future Plan

Until now, GENESIS has been optimized considering only a few computational platforms: K, Intel SandyBridge, and NVIDIA Tesla GPU. From this fiscal year, we will consider optimizations for recently developedCPU and GPU architectures, including Intel Haswell/Broadwell/KNL, postK, and NVIDIA Pascal GPU. Wewill also introduce multi kernels according to each platform. We will further improve the enhanced samplingmethod for more precise free energy evaluation by merging generalized replica-exchanged with solute-tempering(gREST) into free energy perturbation (FEP).

9.5 Publications

9.5.1 Journal Articles

[1] Jaewoon Jung, Akira Naruse, Chigusa Kobayashi, and Yuji Sugita., “Graphics Processing Unit Accelerationand Parallelization of GENESIS for Large-Scale Molecular Dynamics Simulations.”, J. Chem. Theory Comput.,12, 4947 - 4958 (2016).[2] Isseki Yu, Takaharu Mori, Tadashi Ando, Ryuhei Harada, Jaewoon Jung, Yuji Sugita, Michael Feig,“Biomolecular interactions modulate macromolecular structure and dynamics in atomistic model of a bacterialcytoplasm.”, eLife, 5, e19274 (2016).[3] Jaewoon Jung and Yuji Sugita, “Multiple program/multiple data molecular dynamics method with multipletime step integrator for large biological systems”, J. Comput. Chem. 38, 1410 – 1418 (2017)[4] Osamu Miyashita, Chigusa Kobayashi, Takaharu Mori, Yuji Sugita, and Florence Tama, “Flexible fitting tocyro-EM density map using ensemble molecular dynamics simulations”, J. Comput. Chem., 38, 1447 – 1461(2017)

9.5.2 Book

[5] Jaewoon Jung and Yuji Sugita, HPC Technology for Computational Science 1. Chapter 9 (Osaka Univeristypress) (written in Japanese)

9.5.3 Conference Papers

9.5.4 Invited Talks

[6] Jaewoon Jung and Yuji Sugita, “Efficient Parallelization of Molecular Dynamics on Hybrid CPU/GPUSupercomputers”, 2016 GTC Conference, San Jose Convention Center, April 4 – 7 (2016)[7] Yuji Sugita, “Two-step Proton Transfer Mechanism in the Outward-Facing Form of MATE Multidrug Trans-porter”, IAS Focused Program on Molecular Machines of Life: Simulation Meets Experiments, Hong KongUniversity of Science and Technology, May 26 (2016)

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[8] Yuji Sugita, “Membrane protein dynamics and folding by enhanced conformational sampling”, Symposiumon Free Energy Landscape of Protein Folding and Dynamics by Simulations based on Enhanced ConformationalSampling Algorithms, Nagoya University, August 6 (2016)[9] Yuji Sugita, “Enhanced conformational sampling methods for conformational dynamics of proteins, mem-branes, and N-glycans”, International Workshop on Frontiers in Molecular Biophysics, Shanghai, China, July23 – 25 (2016)[10] Yuji Sugita, “Enhanced conformational sampling methods for membrane protein folding and dynamics”,4th International Conference on Molecular Simulations 2016 (ICMS2016), Shanghai, October 23 – 26 (2016)[11] Yuji Sugita, “In-vivo Macromolecular Crowding Effects”, Telluride Summer Conference on Protein andPeptide Dynamics in Cellular Environments, June 27 – July 1 (2016)[12] Chigusa Kobayashi, Yasuhiro Matsunaga, Jaewoon Jung, and Yuji Sugita, “Multi-resolution simulationmethod for reaction mechanism analsysis of large scale structural change of P-type ATPase”, The 89th Annualmeeting of the Japanese Biochemical Society, Sendai, Japan, September 26 (2016) (in Japanese)[13] Yasuhiro Matsunaga, “Functional dynamics of AcrB multidrug transporter by string method”, The 10thKeihan KEIHANNA Seminar, Kyoto University, Japan, March 22 (2017)[14] Yasuhiro Matsunaga, “Drug extrusion mechanism of the multidrug transport AcrB studied by moleculardynamics simulation”, The 97th Annual meeting of Chemical Socieity of Japan, Keio Hiyoshi Campus, Japan,March 18 (2017)[15] Yasuhiro Matsunaga, “Markov state modeling of protein folding dynamics by combining single-moleculeexperiments and simulations”, Simulations Encounter with Data Science in Institute of Statistical Mathematics,Tokyo, March 9 – 11 (2017)[16] Yasuhiro Matsunaga, “A method for searching reaction degrees of freedom in protein structure change”,The 10th Molecular Simulation School-from basic to applied, Okazaki, Japan, October 19 (2016) (in Japanese)[17] Yasuhiro Matsunaga, “Data assimilation in biomolecules”, RIKEN Data assimilation workshop, Kobe,Japan, October 14 (2016) (in Japanese)[18] Yasuhiro Matsunaga, “Analysis of sinlge molecule FRET data and protein dynamics by molecular dynamicssimulation”, The 16th Annual meeting of the Japanese Protein Science Association: Symposium of YoungScientist Awards, Fukuoka, June 7 – 9 (2016) (in Japanese)[19] Yasuhiro Matsunaga, “Analysis of single molecule FRET data and protein dynamics by molecular dynamicssimulation”, The 16th Annual meeting of the Japanese Protein Science Association: Workshop “Sparse Modelingof Life Molecular Measurements”, Fukuoka, Japan, June 7 – 9 (2016) (in Japanese)

9.5.5 Posters and Presentations

[20] Chigusa Kobayashi, Yasuhiro Matsunaga, Jaewoon Jung, and Yuji Sugita, “Analysis of reaction path ofcalcium ion pump by computer simulation”, The 42th Annual meeting of Japanese Bioenergetics Group, Nagoya,Japan, Dec. 20 (in Japanese)[21] Chigusa Kobayashi, Yasuhiro Matsunaga, Jaewoon Jung, and Yuji Sugita, “Molecular dyanmics simulationsfor reaction with large conformational changes in biological system”, The 7th AICS International Symposium,Feb. 23 (2017)[22] Jaewoon Jung and Yuji Sugita, “Multiple program/multiple data molecular dynamics method with multipletime step integrator for large biological systems”, The 30th Annual meeting of Molecular Simulation Society,Osaka, Japan, Nov. 30 – Dec. 2 (2016)[23] Motoshi Kamiya and Yuji Sugita, “In silico folding simulation of Trp-cage using the REST method and itsvariants”, The 54th Annual meeting of the Japanese Biophysical Society, Tsukuba, Japan, Nov. 26 (2016)[24] Koichi Tamura and Shigehiko Hayashi, “Deciphering alternating access mechanism of a MitochondrialADP/ATP Membrane Transporter with Atomistic Simulations”, The 54th Annual meeting of the JapaneseBiophysical Society, Tsukuba, Japan, Nov. 25 (2016)

9.5.6 Patents and Deliverables

[25] Generalized-Ensemble SImulation System (GENESIS) is released. https://aics.riken.jp/labs/cbrt/