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An adaptive evolution strategy for protein folding: Optimizing proteins through genetic algorithms - Couverture souple

Guardiani, Carlo

 
9783838374253: An adaptive evolution strategy for protein folding: Optimizing proteins through genetic algorithms
Couverture souple
ISBN 10 : 3838374258 ISBN 13 : 9783838374253
Editeur : LAP LAMBERT Academic Publishing, 2010

Synopsis

Proteins are a group of molecules involved in practically all biological functions that owe their versatility to the ability of binding structurally complementary ligands. Due to the limitations of experimental techniques, however, the number of resolved structures is lagging behind the number of known sequences, calling for the development of structural prediction algorithms. Since the native structure corresponds to the global minimum of the free energy function, the protein folding problem can be recast in a global optimization problem. Genetic algorithms (GA) are optimization tools that mimick the mechanism of darwinian evolution. In this work the possibility is studied to optimize the Conformational Space Annealing, a GA without mutation, with the Covariance Matrix Adaptation Evolution Strategy (CMAES), that adapts the probability distribution of mutations to the function to optimize. After improving CMAES through local minimizations and a recombination operator, the algorithm is applied to a few benchmark proteins. This book should be of interest to anyone working in computational biophysics and to those working on the improvement and application of GA.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Proteins are a group of molecules involved in practically all biological functions that owe their versatility to the ability of binding structurally complementary ligands. Due to the limitations of experimental techniques, however, the number of resolved structures is lagging behind the number of known sequences, calling for the development of structural prediction algorithms. Since the native structure corresponds to the global minimum of the free energy function, the protein folding problem can be recast in a global optimization problem. Genetic algorithms (GA) are optimization tools that mimick the mechanism of darwinian evolution. In this work the possibility is studied to optimize the Conformational Space Annealing, a GA without mutation, with the Covariance Matrix Adaptation Evolution Strategy (CMAES), that adapts the probability distribution of mutations to the function to optimize. After improving CMAES through local minimizations and a recombination operator, the algorithm is applied to a few benchmark proteins. This book should be of interest to anyone working in computational biophysics and to those working on the improvement and application of GA.

Biographie de l'auteur

Carlo Guardiani: after a Master of Science in Biology, he earned a PhD in Non-Linear Dynamics and Complex Systems. His research area is Molecular Modeling and Computational Biophysics. After six years at the Center for Complex Systems of the University of Florence, Italy, he is currently PostDoctoral fellow at the Georgia State University, USA.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2010
Langue
anglais
ISBN 10 
3838374258
ISBN 13 
9783838374253
Reliure
Broché
Nombre de pages
232
Coordonnées du fabricant
non disponible
Personne responsable
non disponible