A multi-directional rapidly exploring random graph (mRRG) for protein folding

Shuvra Kanti Nath, Shawna Thomas, Chinwe Ekenna, Nancy M. Amato

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Modeling 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.
Original languageEnglish (US)
Title of host publicationProceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine - BCB '12
PublisherAssociation for Computing Machinery (ACM)
Pages44-51
Number of pages8
ISBN (Print)9781450316705
DOIs
StatePublished - 2012
Externally publishedYes

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