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De novo prediction of polypeptide conformations using dihedral probability grid Monte Carlo methodology
Author(s) -
Evans John Spencer,
Chan Sunney I.,
Mathiowetz Alan M.,
Goddard William A.
Publication year - 1995
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1002/pro.5560040618
Subject(s) - dihedral angle , monte carlo method , maxima and minima , torsion (gastropod) , grid , statistical physics , chemistry , crystallography , physics , mathematics , molecule , mathematical analysis , statistics , geometry , biology , zoology , hydrogen bond , organic chemistry
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.