APL: An angle probability list to improve knowledge-based metaheuristics for the three-dimensional protein structure prediction.
Tertiary protein structure prediction is one of the most challenging problems in structural bioinformatics. Despite the advances in algorithm development and computational strategies, predicting the folded structure of a protein only from its amino acid sequence remains as an unsolved problem. We present a new computational approach to predict the native-like three-dimensional structure of proteins. Conformational preferences of amino acid residues and secondary structure information were obtained from protein templates stored in the Protein Data Bank and represented as an Angle Probability List. Two knowledge-based prediction methods based on Genetic Algorithms and Particle Swarm Optimization were developed using this information. The proposed method has been tested with twenty-six case studies selected to validate our approach with different classes of proteins and folding patterns. Stereochemical and structural analysis were performed for each predicted three-dimensional structure. Results achieved suggest that the Angle Probability List can improve the effectiveness of metaheuristics used to predicted the three-dimensional structure of protein molecules by reducing its conformational search space.
Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil.
Bruno
B
Borguesan
Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil.
Mariel
M
Barbachan e Silva
Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil.
Bruno
B
Grisci
Departamento de Ingeniería Informática, Center for Biotechnology and Bioengineering, Universidad de Santiago de Chile, Av. Ecuador 3659, Santiago, Chile.
Mario
M
Inostroza-Ponta
Federal University of Rio Grande do Sul, Institute of Informatics, Av. Bento Gonçalves 9500, 91501-970 Porto Alegre, RS, Brazil. Electronic address: mdorn@inf.ufrgs.br.
Márcio
M
Dorn
D000465:Algorithms; D019295:Computational Biology; D051188:Knowledge Bases; D011487:Protein Conformation; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry*
D011506:Proteins
Amino acid conformational preferences; Metaheuristics; Three-dimensional protein structure prediction