Multiple-Basin Go-Martini for Investigating Conformational Transitions and Environmental Interactions of Proteins

Abstract

Proteins are inherently dynamic molecules, and their conformational transitions among various states are essential for numerous biological processes, which are often modulated by their interactions with surrounding environments. Although molecular dynamics (MD) simulations are widely used to investigate these transitions, all-atom (AA) methods are often limited by short timescales and high computational costs, and coarse-grained (CG) implicitsolvent G[o]-like models are usually incapable of studying the interactions between proteins and their environments. Here, we present an approach called Multiple-basin G[o]-Martini, which combines the recent G[o]-Martini model with an exponential mixing scheme to facilitate the simulation of spontaneous protein conformational transitions in explicit environments. We demonstrate the versatility of our method through five diverse case studies: GlnBP, Arc, Hinge, SemiSWEET, and TRAAK, representing ligand-binding proteins, fold-switching proteins, de novo designed proteins, transporters, and mechanosensitive ion channels, respectively. The Multiple-basin G[o]-Martini offers a new computational tool for investigating protein conformational transitions, identifying key intermediate states, and elucidating essential interactions between proteins and their environments, particularly protein-membrane interactions. In addition, this approach can efficiently generate thermodynamically meaningful datasets of protein conformational space, which may enhance deep learning-based models for predicting protein conformation distributions.