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

Abstract

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 I1 P-turn) and S3 a-helical peptide (YMSEDEL KAAEAAFKRHGPT).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 (6, $, x) 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.Keywords: computational chemistry; importance sampling; Monte Carlo; peptide conformation; protein conformation; protein folding A full understanding of protein function requires knowledge of the three-dimensional structure. Unfortunately, experimentally determined structures for most proteins are unavailable. Consequently, it is essential to develop approaches for predicting secondary and tertiary structures of proteins.In the past decade, several approaches to protein structure prediction have evolved, including: (1) lattice search methods

Ab InitioFolding of Peptides by the Optimal-Bias Monte Carlo Minimization Procedure

“…These methods may be further subdivided according to the way in which random moves are made, their temperature scheduling, and their use of simulation history. The sampling bias, aimed at faster convergence, can be done according to the experimentally observed preferences [1,7,33] defined either as a continuous function [7] or as a discrete grid function [1,33]. Another productive idea aimed at improved sampling is to make local backbone moves [34,35], or restrict the random moves according to the kinetic escape time estimate (the so-called diffusion process-controlled Monte Carlo method [36]).…”

Dentin phosphoprotein sequence motifs and molecular modeling: conformational adaptations to mineral crystals

“…Ab initio structural data for appropriately coordinated phosphate are therefore scarce, and it is often necessary to include X-ray crystal data into the optimum parameter set as well. For larger molecules, Monte Carlo computational methods (24) have been used to predict solvent polarity dependence of dihedral angle distribution and backbone conformation in phosphoserine and aspartate-containing peptides (25). A fourth source of structural data wou ld have been highly desirable in this context: structure determination by a spectroscopic technique, preferably by nuclear magnetic resonance (NMR) (26,27).…”

Modeling of loops in protein structures

“…There are many such methods, exploiting different protein representations, energy function terms, and optimization or enumeration algorithms. The search algorithms include sampling of main-chain dihedral angles biased by their distributions in known protein structures~Moult & James, 1986!, minimum perturbation random tweak method~Fine Shenkin et al, 1987;Smith & Honig, 1994!, systematic conformational search~Bruccoleri & Karplus, 1987Bruccoleri et al, 1988;Brower et al, 1993;Bruccoleri, 1993!, global energy minimization by mapping a trajectory of local minima~Dudek & Scheraga, 1990;Dudek et al, 1998!, importance sampling by local minimization of randomly generated conformations~Lambert & Scheraga, 1989a, 1989b, 1989c!, local energy minimization~Mattos et al, 1994!, molecular dynamics simulations~Bruccoleri & Karplus, 1990Tanner et al, 1992;Rao & Teeter, 1993;Nakajima et al, 2000!, genetic algorithms~McGarrah & Judson, 1993Ring & Cohen, 1994!, biased probability Monte Carlo search~Abagyan & Totrov, 1994Evans et al, 1995;Thanki et al, 1997!, Monte Carlo with simulated annealing~Higo et al, 1992Carlacci & Englander, 1993Collura et al, 1993;Vasmatzis et al, 1994!, Monte Carlo and molecular dynamics~Rapp & Friesner, 1999!, extended-scaled-collective-variable Monte Carlo~Kidera, 1995!, scaling relaxation and multiple copy sampling~Rosenfeld et al, 1993Zheng et al, 1993aZheng et al, , 1993bRosenbach & Rosenfeld, 1995!, searching through discrete conformations by dynamic programming~Vajda &…”