We have proposed a novel robotics-inspired method to enhance the sampling of native-like protein conformations when employing online amino-acid sequence information for the protein at hand. The method is named FeLTr for Fragment Monte CarLo Tree Exploration. The novelty of FeLTr lies in a two-layered exploration of the conformational space that allows to rapidly obtain geometrically-distinct low energy conformations at a coarse-grained level of detail.
FeLTr 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 FeLTr rapidly samples native-like conformations in a few hours on a single CPU. The figures below showcase lowest-energy conformations obtained for one of the tested sequences and juxtapose energies of computed conformations versus their lRMSD from the known native structure of this sequence. Analysis tested sequences shows that computed conformations represent diverse low-energy regions of the energy landscape and are therefore good candidates for further detailed energetic refinements by larger studies in protein engineering and design.
 This work appears in: 1) Amarda Shehu and Brian Olson "Guiding the Search for Native-like Protein Conformations with an Ab-initio Tree-based Exploration" Intl J of Robot Res 2010, 29(8):1106-1127; and 2) Amarda Shehu "An Ab-initio Tree-based Exploration to Enhance Sampling of Low-energy Protein Conformations" Robotics Science and Systems (RSS), Seattle, USA, 2009, pg. 241-248.
On this Project:
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Brian Olson
Kevin Molloy
Amarda Shehu