Sampling-based exploration of folded state of a protein under kinematic and geometric constraints

Handle URI:
http://hdl.handle.net/10754/599552
Title:
Sampling-based exploration of folded state of a protein under kinematic and geometric constraints
Authors:
Yao, Peggy; Zhang, Liangjun; Latombe, Jean-Claude
Abstract:
Flexibility 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.
Citation:
Yao 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.
Publisher:
Wiley-Blackwell
Journal:
Proteins: Structure, Function, and Bioinformatics
Issue Date:
4-Oct-2011
DOI:
10.1002/prot.23134
PubMed ID:
21971749
Type:
Article
ISSN:
0887-3585
Sponsors:
Grant 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)
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorYao, Peggyen
dc.contributor.authorZhang, Liangjunen
dc.contributor.authorLatombe, Jean-Claudeen
dc.date.accessioned2016-02-28T05:53:14Zen
dc.date.available2016-02-28T05:53:14Zen
dc.date.issued2011-10-04en
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.en
dc.identifier.issn0887-3585en
dc.identifier.pmid21971749en
dc.identifier.doi10.1002/prot.23134en
dc.identifier.urihttp://hdl.handle.net/10754/599552en
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.en
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)en
dc.publisherWiley-Blackwellen
dc.subjectDiffusive sampling strategyen
dc.subjectFolded protein conformation samplingen
dc.subjectKinematic closureen
dc.subjectKinematic constraints and geometric constraintsen
dc.subjectRigidity analysisen
dc.titleSampling-based exploration of folded state of a protein under kinematic and geometric constraintsen
dc.typeArticleen
dc.identifier.journalProteins: Structure, Function, and Bioinformaticsen
dc.contributor.institutionStanford University School of Medicine, Stanford, United Statesen
dc.contributor.institutionStanford University, Palo Alto, United Statesen
kaust.grant.programAcademic Excellence Alliance (AEA)en

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