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A genetic algorithm with conformational memories for structure prediction of polypeptides.

Abstract：

:We have developed an iterative hybrid algorithm (HA) to predict the 3D structure of peptides starting from their amino acid sequence. The HA is made of a modified genetic algorithm (GA) coupled to a local optimizer. Each HA iteration is carried out in two phases. In the first phase several GA runs are performed upon the entire peptide conformational space. In the second phase we used the manifestation of what we have called conformational memories, that arises at the end of the first phase, as a way of reducing the peptide conformational space in subsequent HA iterations. Use of conformational memories speeds up and refines the localization of the structure at the putative Global Energy Minimum (GEM) since conformational barriers are avoided. The algorithm has been used to predict successfully the putative GEM for Met- and Leu-enkephalin, and to obtain useful information regarding the 3D structure for the 8mer of polyglycine and the 16 residue (AAQAA)(3)Y peptide. The number of fitness function evaluations needed to locate the putative GEMs are fewer than those reported for other heuristic methods. This study opens the possibility of using Genetic Algorithms in high level predictions of secondary structure of polypeptides.

journal_name

J Biomol Struct Dyn

journal_title

Journal of biomolecular structure & dynamics

authors

Garduño-Juárez R,Morales LB

doi

10.1080/07391102.2003.10506906

subject

Has Abstract

pub_date

2003-08-01 00:00:00

pages

65-87

issue

1

eissn

0739-1102

issn

1538-0254

pii

d=3013&c=4114&p=11764&do=detail

journal_volume

21

pub_type

杂志文章

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