Heterogeneous path ensembles for conformational transitions in semi-atomistic models of adenylate kinase

TL;DR

This study uses weighted ensemble path-sampling simulations to explore conformational transitions in adenylate kinase, revealing pathway heterogeneity and potential for model accuracy improvements without extensive computational costs.

Contribution

It demonstrates the application of semi-atomistic models with weighted ensemble sampling to analyze conformational pathways and tests the symmetry of heterogeneity in transition pathways.

Findings

Significant heterogeneity in conformational pathways.

Evidence of two principal transition pathways.

Path sampling is computationally efficient with semi-atomistic models.

Abstract

We performed "weighted ensemble" path-sampling simulations of adenylate kinase, using several semi-atomistic protein models. Our study investigated both the biophysics of conformational transitions as well as the possibility of increasing model accuracy without sacrificing good sampling. Biophysically, the path ensembles show significant heterogeneity and the explicit possibility of two principle pathways in the Open-Closed transition. We recently showed, under certain conditions, a "symmetry of hetereogeneity" is expected between the forward and the reverse transitions: the fraction of transitions taking a specific pathway/channel will be the same in both the directions. Our path ensembles are analyzed in the light of the symmetry relation and its conditions. In the realm of modeling, we employed an all-atom backbone with various levels of residue interactions. Because reasonable path sampling required only a few weeks of single-processor computing time with these models, the addition of further chemical detail should be feasible.