Three-dimensional protein structure prediction based on memetic algorithms

•An algorithm to deal with the 3-D protein structure prediction problem is proposed.•The method is a multimodal evolutionary algorithm allied to a local search strategy.•The method was built as an incremental approach based on promising evolutionary components.•Results are topologically compatible w...

Full description

Saved in:
Bibliographic Details
Published in:Computers & operations research Vol. 91; pp. 160 - 177
Main Authors: Corrêa, Leonardo de Lima, Borguesan, Bruno, Krause, Mathias J., Dorn, Márcio
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.03.2018
Pergamon Press Inc
Subjects:
Algorithms
Bioinformatics
Complexity theory
Data banks
Evolutionary algorithms
Hybrid evolutionary algorithms
Metaheuristics
Multimodal optimization
Operations research
Operators
Predictions
Proteins
PSP problem
Searching
Structural Bioinformatics
Studies
Three dimensional analysis
PSP problem
Structural Bioinformatics
Multimodal optimization
Metaheuristics
Hybrid evolutionary algorithms
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•An algorithm to deal with the 3-D protein structure prediction problem is proposed.•The method is a multimodal evolutionary algorithm allied to a local search strategy.•The method was built as an incremental approach based on promising evolutionary components.•Results are topologically compatible with the experimentally determined structures. [Display omitted] Tertiary protein structure prediction is a challenging problem in Structural Bioinformatics and is classified according to the computational complexity theory as a NP-hard problem. In this paper, we proposed a first-principle method that makes use of a priori information about known protein structures to tackle the three-dimensional protein structure prediction problem. We do so by designing a multimodal memetic algorithm that uses an evolutionary approach with a ternary tree-structured population allied to a local search strategy. The method has been developed based on an incremental approach using the combination of promising evolutionary components to address the concerned multimodal problem. Three memetic algorithms focused on the problem are proposed. The first one modifies a basic version of a memetic algorithm by introducing modified global search operators. The second uses a different population structure for the memetic algorithm. And finally, the last algorithm consists of the integration of global operators and multimodal strategies to deal with the inherent multimodality of the protein structure prediction problem. The implementations take advantage of structural knowledge stored in the Protein Data Bank to guide the exploiting and restrict the protein conformational search space. Predicted three-dimensional protein structures were analyzed regarding root mean square deviation and the global distance total score test. Obtained results for the three versions outperformed the basic version of the memetic algorithm. The third algorithm overcomes the results of the previous two, demonstrating the importance of adapting the method to deal with the complexities of the problem. In addition, the achieved results are topologically compatible with the experimental correspondent, confirming the promising performance of our approach.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2017.11.015