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De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology

Evans, John Spencer and Mathiowetz, Alan M. and Chan, Sunney I. and Goddard, William A., III (1995) De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology. Protein Science, 4 (6). pp. 1203-1216. ISSN 0961-8368. PMCID PMC2143148. doi:10.1002/pro.5560040618.

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We tested the dihedral probability grid Monte Carlo (DPG‐MC) methodology to determine optimal conformations of polypeptides by applying it to predict the low energy ensemble for two peptides whose solution NMR structures are known: integrin receptor peptide (YGRGDSP, Type II β‐turn) and S3 α‐helical peptide (YMSEDELKAAEAAFKRHGPT). DPG‐MC involves importance sampling, local random stepping in the vicinity of a current local minima, and Metropolis sampling criteria for acceptance or rejection of new structures. Internal coordinate values are based on side‐chain‐specific dihedral angle probability distributions (from analysis of high‐resolution protein crystal structures). Important features of DPG‐MC are: (1) Each DPG‐MC step selects the torsion angles (ϕ, ψ, χ) from a discrete grid that are then applied directly to the structure. The torsion angle increments can be taken as S = 60, 30, 15, 10, or 5°, depending on the application. (2) DPG‐MC utilizes a temperature‐dependent probability function (P) in conjunction with Metropolis sampling to accept or reject new structures. For each peptide, we found close agreement with the known structure for the low energy conformational ensemble located with DPG‐MC. This suggests that DPG‐MC will be useful for predicting conformations of other polypeptides.

Item Type:Article
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URLURL TypeDescription CentralArticle
Chan, Sunney I.0000-0002-5348-2723
Goddard, William A., III0000-0003-0097-5716
Additional Information:© 1995 The Protein Society. (RECEIVED December 20, 1994; ACCEPTED March 21, 1995) We thank Dr. Siddharth Dasgupta for helpful advice during this study. J.S.E. acknowledges a Postdoctoral National Research Service Award from the NIH (NIDR 1F32-DE-05445) and a fellowship award from AMGEN Pharmaceuticals. A.M.M. acknowledges a National Research Service Award/NIH Predoctoral Biotechnology Traineeship. These studies were supported by a grant from DOE-AICD. The facilities of the MSC are also supported by grants from the NSF (CHE91-00284 and ASC-9219368), Allied Signal, Asahi Chemical, Asahi Glass, BP America, Chevron, B.F. Goodrich, Teijin Ltd., Vestar, Xerox, Hughes Research Laboratories, and Beckman Institute. Some of these calculations were carried out on the NSF Pittsburgh Supercomputer and on the JPL Cray. This is Contribution 8949 from the Division of Chemistry and Chemical Engineering, California Institute of Technology.
Funding AgencyGrant Number
NIH Postdoctoral Fellowship1F32-DE-05445
NIH Predoctoral FellowshipUNSPECIFIED
Department of Energy (DOE)UNSPECIFIED
Hughes Research LaboratoriesUNSPECIFIED
Caltech Beckman InstituteUNSPECIFIED
Subject Keywords:computational chemistry; importance sampling; Monte Carlo; peptide conformation; protein confor­mation; protein folding
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Other Numbering System NameOther Numbering System ID
Division of Chemistry and Chemical Engineering8949
Issue or Number:6
PubMed Central ID:PMC2143148
Record Number:CaltechAUTHORS:20190301-092706779
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Official Citation:Evans, J. S., Chan, S. I., Mathiowetz, A. M. and Goddard, W. A. (1995), De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology. Protein Science, 4: 1203-1216. doi:10.1002/pro.5560040618
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:93388
Deposited By: George Porter
Deposited On:01 Mar 2019 18:19
Last Modified:16 Nov 2021 16:57

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