Mutually beneficial confluence of structure-based modeling of protein dynamics and machine learning methods

Proteins sample an ensemble of conformers under physio-logical 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 pathoge-nicity, or estimating binding affinities.

Automated summary

Proteins can adopt a range of conformations, known as intrinsic dynamics, which are essential for their biological function. Machine learning frameworks have been proven useful in structural biology, and recent studies highlight the importance of considering intrinsic dynamics for improving predictive ability. Physics-based theories and models, such as elastic network models, offer an efficient way to quantify dynamic attributes and generate data for training machine learning models to infer protein function mechanisms, assess pathogenicity, or estimate binding affinities.