Mutually Beneficial Confluence of Structure-based Modeling of Protein Dynamics and Machine Learning Methods

Overview

Journal Curr Opin Struct Biol

Date 2023 Jan 1

PMID 36587424

Authors

Anupam Banerjee

Satyaki Saha

Nathan C Tvedt

Lee-Wei Yang

Ivet Bahar

Affiliations

Soon will be listed here.

Abstract

Proteins sample an ensemble of conformers under physiological conditions, having access to a spectrum of modes of motions, also called intrinsic dynamics. These motions ensure the adaptation to various interactions in the cell, and largely assist in, if not determine, viable mechanisms of biological function. In recent years, machine learning frameworks have proven uniquely useful in structural biology, and recent studies further provide evidence to the utility and/or necessity of considering intrinsic dynamics for increasing their predictive ability. Efficient quantification of dynamics-based attributes by recently developed physics-based theories and models such as elastic network models provides a unique opportunity to generate data on dynamics for training ML models towards inferring mechanisms of protein function, assessing pathogenicity, or estimating binding affinities.

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