[1F4-GS-10] AI application: Chemistry / Physics

[1F4-GS-10-04] Representation Learning for Biomolecules by Time-Series Contrastive Learning

〇Tsuyoshi Ishizone¹, Yasuhiro Matsunaga², Sotaro Fuchigami³, Kazuyuki Nakamura¹ (1. Meiji University, 2. Saitama University, 3. University of Shizuoka)

Keywords:protein dynamics, representation learning, contrastive learning

With the development of machine learning technology and improvements in computational power, many new protein structures have been revealed. Among them, AlphaFold2 has brought a breakthrough in protein structure prediction; however, most of the structures that have been elucidated so far are only the most stable structures, and there are still issues to be solved for proteins with multiple stable states or no stable states. It is said that approximately 30 percent of proteins are naturally intrinsically disfolded proteins with unstable structures, and it is essential to elucidate the dynamic mechanisms of the proteins concerning their biological functions.Generally, protein dynamics is described by molecular dynamics simulation. Still, since it is a stochastic calculation, a huge amount of computational time is required to cover the manifold regarding transition. Enhanced sampling (ES) reduces computational time by accelerating the search by adding a bias to the potential. This study proposes a representation learning method for leading to ES potentials. The proposed method is a contrastive learning-based method, and we show that it can construct embeddings suitable for capturing conformational dynamics.