Cargando…

A replica exchange Monte Carlo algorithm for protein folding in the HP model

BACKGROUND: The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function; it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein fol...

Descripción completa

Detalles Bibliográficos
Autores principales: Thachuk, Chris, Shmygelska, Alena, Hoos, Holger H
Formato: Texto
Lenguaje:English
Publicado: BioMed Central|1 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2071922/
https://www.ncbi.nlm.nih.gov/pubmed/17875212
http://dx.doi.org/10.1186/1471-2105-8-342
_version_ 1782137778813796352
author Thachuk, Chris
Shmygelska, Alena
Hoos, Holger H
author_facet Thachuk, Chris
Shmygelska, Alena
Hoos, Holger H
author_sort Thachuk, Chris
collection PubMed
description BACKGROUND: The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function; it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein folding problem is computationally challenging and has been shown to be [Formula: see text]-hard even when conformations are restricted to a lattice. In this work, we implement and evaluate the replica exchange Monte Carlo (REMC) method, which has already been applied very successfully to more complex protein models and other optimization problems with complex energy landscapes, in combination with the highly effective pull move neighbourhood in two widely studied Hydrophobic Polar (HP) lattice models. RESULTS: We demonstrate that REMC is highly effective for solving instances of the square (2D) and cubic (3D) HP protein folding problem. When using the pull move neighbourhood, REMC outperforms current state-of-the-art algorithms for most benchmark instances. Additionally, we show that this new algorithm provides a larger ensemble of ground-state structures than the existing state-of-the-art methods. Furthermore, it scales well with sequence length, and it finds significantly better conformations on long biological sequences and sequences with a provably unique ground-state structure, which is believed to be a characteristic of real proteins. We also present evidence that our REMC algorithm can fold sequences which exhibit significant interaction between termini in the hydrophobic core relatively easily. CONCLUSION: We demonstrate that REMC utilizing the pull move neighbourhood significantly outperforms current state-of-the-art methods for protein structure prediction in the HP model on 2D and 3D lattices. This is particularly noteworthy, since so far, the state-of-the-art methods for 2D and 3D HP protein folding – in particular, the pruned-enriched Rosenbluth method (PERM) and, to some extent, Ant Colony Optimisation (ACO) – were based on chain growth mechanisms. To the best of our knowledge, this is the first application of REMC to HP protein folding on the cubic lattice, and the first extension of the pull move neighbourhood to a 3D lattice.
format Text
id pubmed-2071922
institution National Center for Biotechnology Information
language English
publishDate 2007
publisher BioMed Central|1
record_format MEDLINE/PubMed
spelling pubmed-20719222007-11-09 A replica exchange Monte Carlo algorithm for protein folding in the HP model Thachuk, Chris Shmygelska, Alena Hoos, Holger H BMC Bioinformatics Research Article BACKGROUND: The ab initio protein folding problem consists of predicting protein tertiary structure from a given amino acid sequence by minimizing an energy function; it is one of the most important and challenging problems in biochemistry, molecular biology and biophysics. The ab initio protein folding problem is computationally challenging and has been shown to be [Formula: see text]-hard even when conformations are restricted to a lattice. In this work, we implement and evaluate the replica exchange Monte Carlo (REMC) method, which has already been applied very successfully to more complex protein models and other optimization problems with complex energy landscapes, in combination with the highly effective pull move neighbourhood in two widely studied Hydrophobic Polar (HP) lattice models. RESULTS: We demonstrate that REMC is highly effective for solving instances of the square (2D) and cubic (3D) HP protein folding problem. When using the pull move neighbourhood, REMC outperforms current state-of-the-art algorithms for most benchmark instances. Additionally, we show that this new algorithm provides a larger ensemble of ground-state structures than the existing state-of-the-art methods. Furthermore, it scales well with sequence length, and it finds significantly better conformations on long biological sequences and sequences with a provably unique ground-state structure, which is believed to be a characteristic of real proteins. We also present evidence that our REMC algorithm can fold sequences which exhibit significant interaction between termini in the hydrophobic core relatively easily. CONCLUSION: We demonstrate that REMC utilizing the pull move neighbourhood significantly outperforms current state-of-the-art methods for protein structure prediction in the HP model on 2D and 3D lattices. This is particularly noteworthy, since so far, the state-of-the-art methods for 2D and 3D HP protein folding – in particular, the pruned-enriched Rosenbluth method (PERM) and, to some extent, Ant Colony Optimisation (ACO) – were based on chain growth mechanisms. To the best of our knowledge, this is the first application of REMC to HP protein folding on the cubic lattice, and the first extension of the pull move neighbourhood to a 3D lattice. BioMed Central|1 2007-09-17 /pmc/articles/PMC2071922/ /pubmed/17875212 http://dx.doi.org/10.1186/1471-2105-8-342 Text en Copyright © 2007 Thachuk et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Thachuk, Chris
Shmygelska, Alena
Hoos, Holger H
A replica exchange Monte Carlo algorithm for protein folding in the HP model
title A replica exchange Monte Carlo algorithm for protein folding in the HP model
title_full A replica exchange Monte Carlo algorithm for protein folding in the HP model
title_fullStr A replica exchange Monte Carlo algorithm for protein folding in the HP model
title_full_unstemmed A replica exchange Monte Carlo algorithm for protein folding in the HP model
title_short A replica exchange Monte Carlo algorithm for protein folding in the HP model
title_sort replica exchange monte carlo algorithm for protein folding in the hp model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2071922/
https://www.ncbi.nlm.nih.gov/pubmed/17875212
http://dx.doi.org/10.1186/1471-2105-8-342
work_keys_str_mv AT thachukchris areplicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel
AT shmygelskaalena areplicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel
AT hoosholgerh areplicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel
AT thachukchris replicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel
AT shmygelskaalena replicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel
AT hoosholgerh replicaexchangemontecarloalgorithmforproteinfoldinginthehpmodel