Tracing conformational changes in proteins

Abstract

Background: Many proteins undergo extensive conformational changes as part of their functionality. Tracing these changes is important for understanding the way these proteins function. Traditional biophysics-based conformational search methods require a large number of calculations and are hard to apply to large-scale conformational motions. Results: In this work we investigate the application of a robotics-inspired method, using backbone and limited side chain representation and a coarse grained energy function to trace large-scale conformational motions. We tested the algorithm on four well known medium to large proteins and we show that even with relatively little information we are able to trace low-energy conformational pathways efficiently. The conformational pathways produced by our methods can be further filtered and refined to produce more useful information on the way proteins function under physiological conditions. Conclusions: The proposed method effectively captures large-scale conformational changes and produces pathways that are consistent with experimental data and other computational studies. The method represents an important first step towards a larger scale modeling of more complex biological systems.

A robotics-inspired method to sample conformational paths connecting known functionally-relevant structures in protein systems

“…AdK represents a more challenging case for method. Lowest IRMSDs obtained here are 3-sA , slightly higher than the 2.sA obtained with very coarse-grained models [39]. Table I I CaM (140 aa) 1efd ---+ 2f3y (9.9 A) 2f3y ---+ 1efd (9.9 A)…”

“…The working assumption is that credible conformational paths can be locally deformed with techniques that consider dynamics to obtain transition trajectories. While T-RRT has been shown to connect known low-energy states of the dialanine peptide (2 amino acids long) [38], the PDST method has been shown to produce credible information on the order of conformational changes connecting functional states of large proteins (200-500 amino acids long) [39]. Both methods control the dimensionality of the conformational space by either focusing on systems with very few amino acids [38] or by employing very coarse-grained representations to limit the number of modeled parameters in large proteins [39].…”

“…This is one of the reasons we pursue an EST-based method in this work, where the tree pushes out in conformational space by expanding selected conformations. As is common practice [38], [39], multiple executions can be used to obtain different paths. We emphasize that these paths are not transition trajectories.…”

“…In particular, the T-RRT [38] and PDST [39] methods have focused on the problem of computing conformational changes connecting two given structures. Due to removal of dynamics, the contribution of these methods is not in producing transition trajectories but rather a sequence of conformations (a conformational path) with a credible energy profile.…”

“…Methods building over the Probabilistic Roadmap (PRM) framework [27], [28] have exploited analogies between robot articulated chains and protein chains to fold small proteins [29], [30]. Methods based on treebased planners, such as Rapidly-exploring Random Tree (RRT) [31], Expansive Spaces Tree (EST) [32], and Pathdirected Subdivision Tree (PDST) [33] have been proposed to model conformational changes and flexibility, predict the native structure, and even compute conformational paths connecting given structural states in proteins [34]- [39].…”