理化学研究所 計算科学研究センター

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第142回 第一部

第142回 第一部
日時: 2018年8月3日(金)、13:00 – 14:00
場所: R-CCS 6階講堂

・講演題目:Current Progress on Biomolecular Dynamics Simulations in Cellular Environments
・講演者:杉田 有治(粒子系生物物理研究チーム チームリーダー)
※発表・スライド共に英語

講演要旨: 詳細を見る

To understand structure-dynamics-function relationship of various biomolecules, such as proteins, nucleic acids, membranes, oligosaccharides, and many other metabolites, is essential in molecular biology as well as drug discovery. For this purpose, classical molecular dynamics (MD) simulation was first applied to a small protein, BPTI, in gas by M. Karplus et al in 1977. Since then, the simulation method together with molecular models have been continuously improved and updated by many computational biophysicists in the world.

The computational biophysics research team has developed high-performance MD software, GENESIS (GENeralized Ensemble SImulation System), for performing cutting-edge MD simulations of biological systems on supercomputer K or other computational platforms. One of the key features in GENESIS is the excellent weak scaling on massively parallel supercomputers. This allows us to perform all-atom MD simulations of the cytoplasm in small bacteria, Mycoplasma Genitarium.
This study showed the importance of non-specific protein-protein and protein-metabolite interactions on structure, dynamics, and functions of proteins in the cellular environment.

We are also developing various advanced simulation methods in GENESIS, which are the other key feature in the program. This is important to investigate slow conformational dynamics of biomolecules, such as protein folding, large-scale domain motion of membrane proteins, protein-ligand binding, and so on. In GENESIS, various enhanced conformational sampling methods, including gREST, RSE-MTD, and String method were implemented.

The accuracy of classical MD simulations highly relies on the quality of molecular force field, which consists of a number of parameters that describe bonded and nonbonded interactions. In some cases, the artifact based on the low-quality of molecular force field cannot be neglected. We are now developing a novel machine learning approach linking MD simulations with single-molecule experimental data for overcoming the problems due to the force-field biases. The machine learning approach is applicable to different experimental data for providing reliable information on three-dimensional conformational dynamics.