Ab initio protein fold prediction using evolutionary algorithms: Influence of design and control parameters on performance

True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to t...

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Veröffentlicht in:	Journal of computational chemistry Jg. 27; H. 11; S. 1177 - 1195
Hauptverfasser:	Djurdjevic, Dusan P., Biggs, Mark J.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.08.2006 Wiley Subscription Services, Inc
Schlagworte:	Algorithms biomaterials biosensors Chromosomes - genetics Computer Simulation Enkephalin, Methionine - chemistry Enkephalin, Methionine - genetics Enkephalin, Methionine - metabolism Evolution, Molecular genetic algorithm (GA) Genetic algorithms interfaces met-enkephalin Models, Molecular Molecular structure Mutation - genetics Parameter optimization Peptides - chemistry Peptides - genetics Peptides - metabolism polyalanine Probability protein fold Protein Folding Protein Structure, Tertiary protein tertiary structure stochastic optimization Studies
ISSN:	0192-8651, 1096-987X
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Abstract	True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15‐residue polyalanine molecule—design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady‐state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady‐state designs based on real encoding and multipoint crossover. Application of the steady‐state design to met‐enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met‐enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1177–1195, 2006
AbstractList	True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15-residue polyalanine molecule-design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady-state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady-state designs based on real encoding and multipoint crossover. Application of the steady-state design to met-enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met-enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used.True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15-residue polyalanine molecule-design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady-state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady-state designs based on real encoding and multipoint crossover. Application of the steady-state design to met-enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met-enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15‐residue polyalanine molecule—design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady‐state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady‐state designs based on real encoding and multipoint crossover. Application of the steady‐state design to met‐enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met‐enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1177–1195, 2006 True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15-residue polyalanine molecule-design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady-state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady-state designs based on real encoding and multipoint crossover. Application of the steady-state design to met-enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met-enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15-residue polyalanine molecule - design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady-state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady-state designs based on real encoding and multipoint crossover. Application of the steady-state design to met-enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met-enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. [PUBLICATION ABSTRACT] True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for identifying the free energy global minimum. Various characteristics of evolutionary algorithms (EAs) mean they are in principle well suited to the latter. Studies to date have been less than encouraging, however. This is because of the limited consideration given to EA design and control parameter issues. A comprehensive study of these issues was, therefore, undertaken for ab initio protein fold prediction using a full atomistic protein model. The performance and optimal control parameter settings of twelve EA designs where first established using a 15‐residue polyalanine molecule—design aspects varied include the encoding alphabet, crossover operator, and replacement strategy. It can be concluded that real encoding and multipoint crossover are superior, while both generational and steady‐state replacement strategies have merits. The scaling between the optimal control parameter settings and polyalanine size was also identified for both generational and steady‐state designs based on real encoding and multipoint crossover. Application of the steady‐state design to met‐enkephalin indicated that these scalings are potentially transferable to real proteins. Comparison of the performance of the steady state design for met‐enkephalin with other ab initio methods indicates that EAs can be competitive provided the correct design and control parameter values are used. © 2006 Wiley Periodicals, Inc. J Comput Chem 27: 1177–1195, 2006
Author	Djurdjevic, Dusan P. Biggs, Mark J.
Author_xml	– sequence: 1 givenname: Dusan P. surname: Djurdjevic fullname: Djurdjevic, Dusan P. organization: Institute for Materials and Processes, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, United Kingdom – sequence: 2 givenname: Mark J. surname: Biggs fullname: Biggs, Mark J. email: M.Biggs@ed.ac.uk organization: Institute for Materials and Processes, University of Edinburgh, King's Buildings, Mayfield Road, Edinburgh EH9 3JL, United Kingdom
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Snippet	True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for... True ab initio prediction of protein 3D structure requires only the protein primary structure, a physicochemical free energy model, and a search method for...
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SubjectTerms	Algorithms biomaterials biosensors Chromosomes - genetics Computer Simulation Enkephalin, Methionine - chemistry Enkephalin, Methionine - genetics Enkephalin, Methionine - metabolism Evolution, Molecular genetic algorithm (GA) Genetic algorithms interfaces met-enkephalin Models, Molecular Molecular structure Mutation - genetics Parameter optimization Peptides - chemistry Peptides - genetics Peptides - metabolism polyalanine Probability protein fold Protein Folding Protein Structure, Tertiary protein tertiary structure stochastic optimization Studies
Title	Ab initio protein fold prediction using evolutionary algorithms: Influence of design and control parameters on performance
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Volume	27
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