Structural generation by inverse transformation using principal component analysis enhances conformational sampling of protein

Abstract

Abstract Molecular dynamics (MD) simulations are frequently used to elucidate the molecular mechanisms underlying protein behavior. Based on a conformational search with MD simulations, protein structures rich in high-dimensional data can be quantitatively evaluated in free-energy landscapes (FELs). Generally, FELs are defined in low-dimensional subspaces spanned by reaction coordinates to characterize biological functions. When calculating FELs of proteins, principal component analysis (PCA) is particularly useful for capturing large-amplitude motions via dimensionality reduction into low-dimensional subspaces. In this study, to efficiently calculate FELs, a simple and convenient method is proposed by accelerating conformational search in a PCA subspace, which is achieved by quick generation of protein configurations. Specifically, inverse transformation driven by PCA facilitates the quick generation of diverse protein configurations from arbitrary grids in a defined PCA subspace. In our conformational search, a set of newly generated configurations serves as initial structures for multiple MD simulations, enabling one to calculate FELs of proteins by building Markov state models from their multiple trajectories. In conclusion, the conformational search from protein configurations broadly distributed in a PCA subspace accelerates FEL calculations, which supports a comprehensive approach to understanding collective protein dynamics.