Discovering loop conformational flexibility in T4 lysozyme mutants through artificial intelligence aided molecular dynamics

Abstract

# These two authors contributed equally.Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and pathways for moving between them contain enormous information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these higher energy states but often at a prohibitively high computational cost.Here we apply our recent statistical mechanics and artificial intelligence based molecular dynamics framework for enhanced sampling of protein loops in three mutants of the protein T4 lysozyme. We are able to correctly rank these according to the stability of their excited state. By analyzing reaction coordinates, we also obtain crucial insight into why these specific perturbations in sequence space lead to tremendous variations in conformational flexibility.Our framework thus allows accurate comparison of loop conformation populations with minimal prior human bias, and should be directly applicable to a range of macromolecules in biology, chemistry and beyond.

Confronting pitfalls of AI-augmented molecular dynamics using statistical physics

“…The biased MD itself can be performed using one of the many available biased sampling schemes. 40,64,65 In order to learn the PIB, RAVE uses an encoder-decoder framework. The PIB or RC χ is expressed as a linear combination of order parameters χ = i c i s i where the order parameters 57 and d denotes the dimension of the order parameter space.…”