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

Curr Opin Struct Biol. 2023 Feb:78:102517. doi: 10.1016/ Epub 2022 Dec 30.


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.

Publication types

  • Review
  • Research Support, N.I.H., Extramural

MeSH terms

  • Machine Learning*
  • Proteins* / chemistry


  • Proteins