A multi-directional rapidly exploring random graph (mRRG) for protein folding
KAUST Grant NumberKUS-C1-016-04
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AbstractModeling large-scale protein motions, such as those involved in folding and binding interactions, is crucial to better understanding not only how proteins move and interact with other molecules but also how proteins misfold, thus causing many devastating diseases. Robotic motion planning algorithms, such as Rapidly Exploring Random Trees (RRTs), have been successful in simulating protein folding pathways. Here, we propose a new multi-directional Rapidly Exploring Random Graph (mRRG) specifically tailored for proteins. Unlike traditional RRGs which only expand a parent conformation in a single direction, our strategy expands the parent conformation in multiple directions to generate new samples. Resulting samples are connected to the parent conformation and its nearest neighbors. By leveraging multiple directions, mRRG can model the protein motion landscape with reduced computational time compared to several other robotics-based methods for small to moderate-sized proteins. Our results on several proteins agree with experimental hydrogen out-exchange, pulse-labeling, and F-value analysis. We also show that mRRG covers the conformation space better as compared to the other computation methods. Copyright © 2012 ACM.
CitationNath SK, Thomas S, Ekenna C, Amato NM (2012) A multi-directional rapidly exploring random graph (mRRG) for protein folding. Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB ’12. Available: http://dx.doi.org/10.1145/2382936.2382942.
SponsorsThis work is supported in part by NSF awards CRI-0551685, CCF-0833199, CCF-0830753, IIS-096053, IIS-0917266 by THECB NHARP award 000512-0097-2009, byChevron, IBM, Intel, Oracle/Sun and by Award KUS-C1-016-04, made by King Abdullah University of Science andTechnology (KAUST).
JournalProceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12