article

Protein structure prediction using diversity controlled self-adaptive differential evolution with local search

Authors:

S. Miruna Amali,

S. KrishnaswamyAuthors Info & Claims

Soft Computing - A Fusion of Foundations, Methodologies and Applications, Volume 19, Issue 6

Pages 1635 - 1646

https://doi.org/10.1007/s00500-014-1353-2

Published: 01 June 2015 Publication History

Abstract

In this paper, Protein Structure Prediction problem is solved using Diversity Controlled Self-Adaptive Differential Evolution with Local search (DCSaDE-LS). DCSaDE-LS, an improved version of Self-Adaptive Differential Evolution (SaDE), use simple fuzzy system to control the diversity of individuals and local search to maintain a balance between exploration and exploitation. DCSaDE-LS with four different local search replacement strategies are used. SaDE is also implemented for comparison purposes. Algorithms are tested on a peptide Met-enkephalin for force fields ECEPP/2, ECEPP/3 and CHARMM22. Results show that both DCSaDE-LS and SaDE produce the best energy for both force fields. Among the four replacement strategies, DCSaDE-LS1 strategy reports better results than other strategies and SaDE in terms of number of function evaluations, mean energy and success rate. Best conformations obtained using DCSaDE-LS is compared with native structure 1PLW and GEM structure Scheraga. Nonparametric statistical tests for multiple comparisons ($$1\times N$$1 N) with control method are implemented for CHARMM22 observations. A set of unique 100 best conformations obtained from DCSaDE-LS are clustered into 3 independent clusters suggesting the robustness of this methodology and the ability to explore the conformational space available and to populate the near native conformations.

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Cited By

Parpinelli RWill Nda Silva R(2024)A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problemNatural Computing: an international journal10.1007/s11047-022-09916-z23:4(687-705)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s11047-022-09916-z
Sudha SBaskar SKrishnaswamy S(2022)Protein docking using constrained self-adaptive differential evolution algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03717-223:22(11651-11669)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s00500-018-03717-2
Morshedian ARazmara JLotfi S(2019)A novel approach for protein structure prediction based on an estimation of distribution algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3130-023:13(4777-4788)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3130-0
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Protein structure prediction using diversity controlled self-adaptive differential evolution with local search
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cover image Soft Computing - A Fusion of Foundations, Methodologies and Applications

Soft Computing - A Fusion of Foundations, Methodologies and Applications Volume 19, Issue 6

June 2015

311 pages

ISSN:1432-7643

EISSN:1433-7479

Issue’s Table of Contents

Copyright © Copyright © 2015 Springer-Verlag Berlin Heidelberg.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 01 June 2015

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Cited By

Parpinelli RWill Nda Silva R(2024)A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problemNatural Computing: an international journal10.1007/s11047-022-09916-z23:4(687-705)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1007/s11047-022-09916-z
Sudha SBaskar SKrishnaswamy S(2022)Protein docking using constrained self-adaptive differential evolution algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-03717-223:22(11651-11669)Online publication date: 11-Mar-2022
https://dl.acm.org/doi/10.1007/s00500-018-03717-2
Morshedian ARazmara JLotfi S(2019)A novel approach for protein structure prediction based on an estimation of distribution algorithmSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-018-3130-023:13(4777-4788)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00500-018-3130-0
Adnan SWang SIbrahim HNg T(2018)An Overview on the Application of Self-Adaptive Differential EvolutionProceedings of the 10th International Conference on Computer Modeling and Simulation10.1145/3177457.3177504(82-86)Online publication date: 8-Jan-2018
https://dl.acm.org/doi/10.1145/3177457.3177504
Rakhshani HIdoumghar LLepagnot JBrévilliers MRahati A(2018)Accelerating Protein Structure Prediction Using Active Learning and Surrogate-Based Optimization2018 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2018.8477805(1-6)Online publication date: 8-Jul-2018
https://dl.acm.org/doi/10.1109/CEC.2018.8477805
Zhang GZhou XYu XHao XYu L(2017)Enhancing Protein Conformational Space Sampling Using Distance Profile-Guided Differential EvolutionIEEE/ACM Transactions on Computational Biology and Bioinformatics10.1109/TCBB.2016.256661714:6(1288-1301)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1109/TCBB.2016.2566617
Guo ZLiu GLi DWang S(2017)Self-adaptive differential evolution with global neighborhood searchSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2029-x21:13(3759-3768)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s00500-016-2029-x
Zhou XZhang GHao XYu L(2016)A novel differential evolution algorithm using local abstract convex underestimate strategy for global optimizationComputers and Operations Research10.1016/j.cor.2016.05.01575:C(132-149)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.cor.2016.05.015
Zhou XZhang GHao XXu DYu L(2016)Enhanced differential evolution using local Lipschitz underestimate strategy for computationally expensive optimization problemsApplied Soft Computing10.1016/j.asoc.2016.06.04448:C(169-181)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.asoc.2016.06.044

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