Literature DB >> 16049911

Exhaustive Metropolis Monte Carlo sampling and analysis of polyalanine conformations adopted under the influence of hydrogen bonds.

Alexei A Podtelezhnikov1, David L Wild.   

Abstract

We propose a novel Metropolis Monte Carlo procedure for protein modeling and analyze the influence of hydrogen bonding on the distribution of polyalanine conformations. We use an atomistic model of the polyalanine chain with rigid and planar polypeptide bonds, and elastic alpha carbon valence geometry. We adopt a simplified energy function in which only hard-sphere repulsion and hydrogen bonding interactions between the atoms are considered. Our Metropolis Monte Carlo procedure utilizes local crankshaft moves and is combined with parallel tempering to exhaustively sample the conformations of 16-mer polyalanine. We confirm that Flory's isolated-pair hypothesis (the steric independence between the dihedral angles of individual amino acids) does not hold true in long polypeptide chains. In addition to 3(10)- and alpha-helices, we identify a kink stabilized by 2 hydrogen bonds with a shared acceptor as a common structural motif. Varying the strength of hydrogen bonds, we induce the helix-coil transition in the model polypeptide chain. We compare the propensities for various hydrogen bonding patterns and determine the degree of cooperativity of hydrogen bond formation in terms of the Hill coefficient. The observed helix-coil transition is also quantified according to Zimm-Bragg theory. (c) 2005 Wiley-Liss, Inc.

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Year:  2005        PMID: 16049911     DOI: 10.1002/prot.20513

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  6 in total

1.  Exploring the energy landscapes of protein folding simulations with Bayesian computation.

Authors:  Nikolas S Burkoff; Csilla Várnai; Stephen A Wells; David L Wild
Journal:  Biophys J       Date:  2012-02-21       Impact factor: 4.033

2.  Reconstruction and stability of secondary structure elements in the context of protein structure prediction.

Authors:  Alexei A Podtelezhnikov; David L Wild
Journal:  Biophys J       Date:  2009-06-03       Impact factor: 4.033

3.  AutoDock CrankPep: combining folding and docking to predict protein-peptide complexes.

Authors:  Yuqi Zhang; Michel F Sanner
Journal:  Bioinformatics       Date:  2019-12-15       Impact factor: 6.937

4.  Methods for Monte Carlo simulations of biomacromolecules.

Authors:  Andreas Vitalis; Rohit V Pappu
Journal:  Annu Rep Comput Chem       Date:  2009-01-01

5.  Efficient Parameter Estimation of Generalizable Coarse-Grained Protein Force Fields Using Contrastive Divergence: A Maximum Likelihood Approach.

Authors:  Csilla Várnai; Nikolas S Burkoff; David L Wild
Journal:  J Chem Theory Comput       Date:  2013-11-15       Impact factor: 6.006

6.  CRANKITE: A fast polypeptide backbone conformation sampler.

Authors:  Alexei A Podtelezhnikov; David L Wild
Journal:  Source Code Biol Med       Date:  2008-06-24
  6 in total

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