Elucidating the ensemble of functionally-relevant transitions in protein systems with a robotics-inspired method

Abstract

Background: Many proteins tune their biological function by transitioning between different functional states, effectively acting as dynamic molecular machines. Detailed structural characterization of transition trajectories is central to understanding the relationship between protein dynamics and function. Computational approaches that build on the Molecular Dynamics framework are in principle able to model transition trajectories at great detail but also at considerable computational cost. Methods that delay consideration of dynamics and focus instead on elucidating energetically-credible conformational paths connecting two functionally-relevant structures provide a complementary approach. Effective sampling-based path planning methods originating in robotics have been recently proposed to produce conformational paths. These methods largely model short peptides or address large proteins by simplifying conformational space. Methods: We propose a robotics-inspired method that connects two given structures of a protein by sampling conformational paths. The method focuses on small-to medium-size proteins, efficiently modeling structural deformations through the use of the molecular fragment replacement technique. In particular, the method grows a tree in conformational space rooted at the start structure, steering the tree to a goal region defined around the goal structure. We investigate various bias schemes over a progress coordinate for balance between coverage of conformational space and progress towards the goal. A geometric projection layer promotes path diversity. A reactive temperature scheme allows sampling of rare paths that cross energy barriers. Results and conclusions: Experiments are conducted on small-to medium-size proteins of length up to 214 amino acids and with multiple known functionally-relevant states, some of which are more than 13Å apart of each-other. Analysis reveals that the method effectively obtains conformational paths connecting structural states that are significantly different. A detailed analysis on the depth and breadth of the tree suggests that a soft global bias over the progress coordinate enhances sampling and results in higher path diversity. The explicit geometric projection layer that biases the exploration away from over-sampled regions further increases coverage, often improving proximity to the goal by forcing the exploration to find new paths. The reactive temperature scheme is shown effective in increasing path diversity, particularly in difficult structural transitions with known high-energy barriers.

Frustration-guided motion planning reveals conformational transitions in proteins

“…These values are in close agreement with those reported by Beckstein et al The structures with high RMSD (1dvr_a, 1ak2_a and, to a lesser extent, 2bbw_a) lie off the main transition pathways, with an RMSD, for example, for 1ak2_a, exceeding 4 Å. Compared to peer methods, our transitions approach intermediates closer.…”

“…The transition of ADK has been extensively studied with different computational methods . Abundant experimentally determined intermediate structures make it a suitable test case for validation.…”

“…At the heart of these methods are motion planners like probabilistic roadmaps, rapidly exploring random trees (RRT), and their variants. Applied to biomolecules, these methods have offered new insights into local loop motions, protein folding, conformational heterogeneity and transitions . For a broad overview we refer the reader to this review …”

“…Applied to biomolecules, these methods have offered new insights into local loop motions, 27,28 protein folding, 29,30 conformational heterogeneity 31 and transitions. [32][33][34][35] For a broad overview we refer the reader to this review. 36 Motion planning has been combined with ENMs to study conformational transition pathways.…”

Motion planning with temporal-logic specifications: Progress and challenges

“…Sampling-based motion planners have now been used to solve high-dimensional problems ranging from navigation, manipulation, to computational biology, and medicine [2,76,60,9,47]. Another success story has been their use in the DARPA Urban Challenge, which required teams to build a fully autonomous car that can navigate in urban traffic.…”