An Ab-initio tree-based exploration to enhance sampling of low-energy protein conformations

This paper proposes a robotics-inspired method to enhance sampling of native-like protein conformations when employing only amino-acid sequence. Computing such conformations, essential to associate structural and functional information with gene sequences, is challenging due to the high-dimensionality and the rugged energy surface of the protein conformational space. The contribution of this work is a novel two-layered method to enhance the sampling of geometrically-distinct lowenergy conformations at a coarse-grained level of detail. The method grows a tree in conformational space reconciling two goals: (i) guiding the tree towards lower energies and (ii) not oversampling geometrically-similar conformations. Discretizations of the energy surface and a low-dimensional projection space are employed to select more often for expansion low-energy conformations in under-explored regions of the conformational space. The tree is expanded with low-energy conformations through a Metropolis Monte Carlo framework that uses a move set of physical fragment configurations. Testing on sequences of seven small-to-medium structurally-diverse proteins shows that the method rapidly samples native-like conformations in a few hours on a single CPU. Analysis shows that computed conformations are good candidates for further detailed energetic refinements by larger studies in protein engineering and design.