Literature DB >> 8268410

Biasing a Monte Carlo chain growth method with Ramachandran's plot: application to twenty-L-alanine.

J Bascle1, T Garel, H Orland, B Velikson.   

Abstract

In this paper, we explore the possibility of using experimental observations in the Monte Carlo chain growth method that we have previously developed. In this method, the macromolecule (peptide, protein, nuclei acid, etc.) is grown atom-by-atom (or residue-by-residue, etc.) and partial chains are replicated according to their Boltzmann weights. Once the molecule completed, we are left with a Boltzmann-distributed ensemble of configurations. For long molecules, an efficient sampling of the (extremely large) phase space is difficult for obvious reasons (existence of many local minima, limited computer memory, etc.). In the case in which one is mainly interested in the low energy conformations, we have incorporated in the growth scheme experimental observations taken from the Protein Data Banks. More precisely, we have considered the case of twenty-L-alanine and we have used the (experimental) Ramachandran's plot for this residue. The biased growth procedure goes as follows: (a) each time one adds along the main backbone chain, either a carbon atom belonging to a carbonyl group, or a nitrogen atom, its dihedral angle (theta) or (psi) is drawn with a probability law that reflects the experimental Ramachandran (theta, psi) plot; (b) the bias introduced in this way is canceled through an extra term in the energy (replication energy = true energy + bias energy); (c) the configurations, generated at T = 1000 K, are then energy minimized.(ABSTRACT TRUNCATED AT 250 WORDS)

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Year:  1993        PMID: 8268410     DOI: 10.1002/bip.360331210

Source DB:  PubMed          Journal:  Biopolymers        ISSN: 0006-3525            Impact factor:   2.505


  3 in total

1.  Calculation of the entropy of random coil polymers with the hypothetical scanning Monte Carlo method.

Authors:  Ronald P White; Hagai Meirovitch
Journal:  J Chem Phys       Date:  2005-12-01       Impact factor: 3.488

2.  Rapid sampling of all-atom peptides using a library-based polymer-growth approach.

Authors:  Artem B Mamonov; Xin Zhang; Daniel M Zuckerman
Journal:  J Comput Chem       Date:  2010-08-23       Impact factor: 3.376

3.  Absolute free energies estimated by combining precalculated molecular fragment libraries.

Authors:  Xin Zhang; Artem B Mamonov; Daniel M Zuckerman
Journal:  J Comput Chem       Date:  2009-08       Impact factor: 3.376

  3 in total

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