Discovering Protein Conformational Flexibility through Artificial-Intelligence-Aided Molecular Dynamics

摘要

Proteins sample a variety of conformations distinct from their crystal structure. These structures, their propensities, and the pathways for moving between them contain an enormous amount of information about protein function that is hidden from a purely structural perspective. Molecular dynamics simulations can uncover these alternative conformations 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. We exemplify the approach through the study of three mutants of the classical test-piece 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 an 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.