Inferring Dynamic Information from Protein Structures by Gaussian Integrals and Deep Learning (opens in new tab)

AbstractMotivationProtein dynamics are central to function, but experiments and molecular dynamics (MD) simulations remain costly, low-throughput, and difficult to compare across protocols. Scalable structure-based methods are needed to infer dynamics from static protein structures.ResultsWe present a deep learning framework that predicts protein dynamics from 30-dimensional Gaussian integral (GI) descriptors of Cα backbone topology. Using 1,374 ATLAS protein chains with MD-derived RMSF, GI s...