Frustration-guided motion planning reveals conformational transitions in proteins

Budday D, Fonseca R, Leyendecker S, van den Bedem H (2017)


Publication Language: English

Publication Type: Journal article

Publication year: 2017

Journal

Book Volume: 85

Pages Range: 1795-1807

Journal Issue: 10

DOI: 10.1002/prot.25333

Open Access Link: http://onlinelibrary.wiley.com/doi/10.1002/prot.25333/epdf

Abstract

sProteins exist as conformational ensembles, exchanging between substates to perform their func- tion. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics-inspired motion planning procedure called dCC- RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non-native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Ca atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC-RRT to examine how collective, small- scale motions of four side-chains in the active site of cyclophilin A propagate through the protein. dCC-RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non-canonical capsid binding site 25 Å away, rationalizing NMR and multi-temperature crystallography experiments. In all, dCC- RRT can reveal detailed, all-atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/. gdfdfgdf
Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics-inspired motion planning procedure called dCC-RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non-native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 å root mean square deviation (RMSD), our pathways reduced the Ca atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC-RRT to examine how collective, small scale motions of four side-chains in the active site of cyclophilin A propagate through the protein. dCC-RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non-canonical capsid binding site 25 å away, rationalizing NMR and multi-temperature crystallography experiments. In all, dCC-RRT can reveal detailed, all-atom molecular mechanisms for small and large amplitude motions.
Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/.

Authors with CRIS profile

Related research project(s)

Involved external institutions

How to cite

APA:

Budday, D., Fonseca, R., Leyendecker, S., & van den Bedem, H. (2017). Frustration-guided motion planning reveals conformational transitions in proteins. Proteins-Structure Function and Bioinformatics, 85(10), 1795-1807. https://dx.doi.org/10.1002/prot.25333

MLA:

Budday, Dominik, et al. "Frustration-guided motion planning reveals conformational transitions in proteins." Proteins-Structure Function and Bioinformatics 85.10 (2017): 1795-1807.

BibTeX: Download