Protein folding pathway and intermediates on the dimensionality reduced landscape with UMAP and native contact likelihood.

Abstract

To understand protein folding mechanisms from molecular dynamics (MD) simulations, it is important to explore not only folded/unfolded states but also representative intermediate structures on the conformational landscape. Generally, such a conformational landscape is obtained by linear dimensionality reduction methods, principal component analysis (PCA) or time-lagged independent component analysis (tICA), for instance. However, it is often difficult to apply these linear methods to large-scale, nonlinear conformational changes such as protein folding. Here, we propose a novel approach to construct the landscape using the uniform manifold approximation and projection (UMAP) method, which nonlinearly reduces the dimensionality without losing data-point proximity. In the approach, native contact likelihood, a measure for native contact formation, is used as feature variables rather than the conventional Cartesian coordinates or dihedral angles of protein structures. We tested the performance of UMAP for coarse-grained MD simulation trajectories of B1 domain in protein G and observed on-pathway transient structures and other metastable states on the UMAP conformational landscape. In contrast, these structures were not clearly distinguished on the dimensionality reduced landscape using PCA or tICA. This approach is also useful to obtain dynamical information through Markov State Modeling and would be applicable to large-scale conformational changes in many other biomacromolecules.