Maintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methods

Simple item page
dc.citation.issueNumber1en_US
dc.citation.journalTitleJournal of Computional Biologyen_US
dc.citation.volumeNumber25en_US
dc.contributor.authorAbella, Jayvee R.en_US
dc.contributor.authorMoll, Marken_US
dc.contributor.authorKavraki, Lydia E.en_US
dc.date.accessioned2018-06-27T13:50:15Zen_US
dc.date.available2018-06-27T13:50:15Zen_US
dc.date.issued2018en_US
dc.description.abstractThe ability to efficiently sample structurally diverse protein conformations allows one to gain a high-level view of a protein's energy landscape. Algorithms from robot motion planning have been used for conformational sampling, and several of these algorithms promote diversity by keeping track of "coverage" in conformational space based on the local sampling density. However, large proteins present special challenges. In particular, larger systems require running many concurrent instances of these algorithms, but these algorithms can quickly become memory intensive because they typically keep previously sampled conformations in memory to maintain coverage estimates. In addition, robotics-inspired algorithms depend on defining useful perturbation strategies for exploring the conformational space, which is a difficult task for large proteins because such systems are typically more constrained and exhibit complex motions. In this article, we introduce two methodologies for maintaining and enhancing diversity in robotics-inspired conformational sampling. The first method addresses algorithms based on coverage estimates and leverages the use of a low-dimensional projection to define a global coverage grid that maintains coverage across concurrent runs of sampling. The second method is an automatic definition of a perturbation strategy through readily available flexibility information derived from B-factors, secondary structure, and rigidity analysis. Our results show a significant increase in the diversity of the conformations sampled for proteins consisting of up to 500 residues when applied to a specific robotics-inspired algorithm for conformational sampling. The methodologies presented in this article may be vital components for the scalability of robotics-inspired approaches.en_US
dc.identifier.citationAbella, Jayvee R., Moll, Mark and Kavraki, Lydia E.. "Maintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methods." <i>Journal of Computional Biology,</i> 25, no. 1 (2018) Mary Ann Liebert, Inc.: https://doi.org/10.1089/cmb.2017.0164.en_US
dc.identifier.doihttps://doi.org/10.1089/cmb.2017.0164en_US
dc.identifier.urihttps://hdl.handle.net/1911/102294en_US
dc.language.isoengen_US
dc.publisherMary Ann Liebert, Inc.en_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Mary Ann Liebert, Inc.en_US
dc.subject.keywordconcurrent samplingen_US
dc.subject.keywordperturbation strategiesen_US
dc.subject.keywordprotein conformational samplingen_US
dc.subject.keywordrobotics-inspired samplingen_US
dc.titleMaintaining and Enhancing Diversity of Sampled Protein Conformations in Robotics-Inspired Methodsen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
abella2018.pdf
Size:
1.05 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Faculty Publications
Computer Science Publications