Show simple item record

dc.contributor.authorYao, Peggy
dc.contributor.authorZhang, Liangjun
dc.contributor.authorLatombe, Jean-Claude
dc.date.accessioned2016-02-28T05:53:14Z
dc.date.available2016-02-28T05:53:14Z
dc.date.issued2011-10-04
dc.identifier.citationYao P, Zhang L, Latombe J-C (2011) Sampling-based exploration of folded state of a protein under kinematic and geometric constraints. Proteins: Structure, Function, and Bioinformatics 80: 25–43. Available: http://dx.doi.org/10.1002/prot.23134.
dc.identifier.issn0887-3585
dc.identifier.pmid21971749
dc.identifier.doi10.1002/prot.23134
dc.identifier.urihttp://hdl.handle.net/10754/599552
dc.description.abstractFlexibility is critical for a folded protein to bind to other molecules (ligands) and achieve its functions. The conformational selection theory suggests that a folded protein deforms continuously and its ligand selects the most favorable conformations to bind to. Therefore, one of the best options to study protein-ligand binding is to sample conformations broadly distributed over the protein-folded state. This article presents a new sampler, called kino-geometric sampler (KGS). This sampler encodes dominant energy terms implicitly by simple kinematic and geometric constraints. Two key technical contributions of KGS are (1) a robotics-inspired Jacobian-based method to simultaneously deform a large number of interdependent kinematic cycles without any significant break-up of the closure constraints, and (2) a diffusive strategy to generate conformation distributions that diffuse quickly throughout the protein folded state. Experiments on four very different test proteins demonstrate that KGS can efficiently compute distributions containing conformations close to target (e.g., functional) conformations. These targets are not given to KGS, hence are not used to bias the sampling process. In particular, for a lysine-binding protein, KGS was able to sample conformations in both the intermediate and functional states without the ligand, while previous work using molecular dynamics simulation had required the ligand to be taken into account in the potential function. Overall, KGS demonstrates that kino-geometric constraints characterize the folded subset of a protein conformation space and that this subset is small enough to be approximated by a relatively small distribution of conformations. © 2011 Wiley Periodicals, Inc.
dc.description.sponsorshipGrant sponsor: NSF; Grant number: DMS-0443939; Grant sponsor: NSF Postdoctoral CIFellowship ( Computing Research Association); Grant number: 0937060; Grant sponsor: Academic Excellence Alliance Program ( King Abdullah University of Science & Technology); Grant number: Stanford University, Bio-X fellowship, BMI program ( Stanford University)
dc.publisherWiley
dc.subjectDiffusive sampling strategy
dc.subjectFolded protein conformation sampling
dc.subjectKinematic closure
dc.subjectKinematic constraints and geometric constraints
dc.subjectRigidity analysis
dc.titleSampling-based exploration of folded state of a protein under kinematic and geometric constraints
dc.typeArticle
dc.identifier.journalProteins: Structure, Function, and Bioinformatics
dc.contributor.institutionStanford University School of Medicine, Stanford, United States
dc.contributor.institutionStanford University, Palo Alto, United States
kaust.grant.programAcademic Excellence Alliance (AEA)
dc.date.published-online2011-10-04
dc.date.published-print2012-01


This item appears in the following Collection(s)

Show simple item record