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A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction

BACKGROUND: Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformat...

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Autores principales: Saleh, Sameh, Olson, Brian, Shehu, Amarda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953177/
https://www.ncbi.nlm.nih.gov/pubmed/24565020
http://dx.doi.org/10.1186/1472-6807-13-S1-S4
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author Saleh, Sameh
Olson, Brian
Shehu, Amarda
author_facet Saleh, Sameh
Olson, Brian
Shehu, Amarda
author_sort Saleh, Sameh
collection PubMed
description BACKGROUND: Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. METHODS: We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. RESULTS AND CONCLUSIONS: Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be key to enhancing sampling capability and obtaining a diverse ensemble of decoy conformations, circumventing premature convergence to sub-optimal regions in the conformational space, and approaching the native structure with proximity that is comparable to state-of-the-art decoy sampling methods. The results are shown to be robust and valid when using two representative state-of-the-art coarse-grained energy functions.
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spelling pubmed-39531772014-03-24 A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction Saleh, Sameh Olson, Brian Shehu, Amarda BMC Struct Biol Research BACKGROUND: Elucidating the native structure of a protein molecule from its sequence of amino acids, a problem known as de novo structure prediction, is a long standing challenge in computational structural biology. Difficulties in silico arise due to the high dimensionality of the protein conformational space and the ruggedness of the associated energy surface. The issue of multiple minima is a particularly troublesome hallmark of energy surfaces probed with current energy functions. In contrast to the true energy surface, these surfaces are weakly-funneled and rich in comparably deep minima populated by non-native structures. For this reason, many algorithms seek to be inclusive and obtain a broad view of the low-energy regions through an ensemble of low-energy (decoy) conformations. Conformational diversity in this ensemble is key to increasing the likelihood that the native structure has been captured. METHODS: We propose an evolutionary search approach to address the multiple-minima problem in decoy sampling for de novo structure prediction. Two population-based evolutionary search algorithms are presented that follow the basic approach of treating conformations as individuals in an evolving population. Coarse graining and molecular fragment replacement are used to efficiently obtain protein-like child conformations from parents. Potential energy is used both to bias parent selection and determine which subset of parents and children will be retained in the evolving population. The effect on the decoy ensemble of sampling minima directly is measured by additionally mapping a conformation to its nearest local minimum before considering it for retainment. The resulting memetic algorithm thus evolves not just a population of conformations but a population of local minima. RESULTS AND CONCLUSIONS: Results show that both algorithms are effective in terms of sampling conformations in proximity of the known native structure. The additional minimization is shown to be key to enhancing sampling capability and obtaining a diverse ensemble of decoy conformations, circumventing premature convergence to sub-optimal regions in the conformational space, and approaching the native structure with proximity that is comparable to state-of-the-art decoy sampling methods. The results are shown to be robust and valid when using two representative state-of-the-art coarse-grained energy functions. BioMed Central 2013-11-08 /pmc/articles/PMC3953177/ /pubmed/24565020 http://dx.doi.org/10.1186/1472-6807-13-S1-S4 Text en Copyright © 2013 Saleh 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. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Saleh, Sameh
Olson, Brian
Shehu, Amarda
A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title_full A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title_fullStr A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title_full_unstemmed A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title_short A population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
title_sort population-based evolutionary search approach to the multiple minima problem in de novo protein structure prediction
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3953177/
https://www.ncbi.nlm.nih.gov/pubmed/24565020
http://dx.doi.org/10.1186/1472-6807-13-S1-S4
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