In Structural Bioinformatics, several problems do not present a computational method that can guarantee the minimum quality of their solutions. Structural bioinformatics deals with problems where the rules governing biochemical processes and their relationships are partially known, making it challenging to develop efficient computational strategies. Among these problems, two are the most challenging: predicting the three-dimensional structure of macromolecules and the molecular docking problem. Predicting the structure of a polypeptide/protein just from its linear sequence of amino acid residues represents a challenging problem in the field of mathematical optimization, being classified in computational complexity as an NP-complete problem. The challenge occurs due to the explosion of possible conformations that a long chain of amino acid residues can assume. Another significant problem is related to the construction of algorithms for assisted drug/drug development. In this context, molecular docking (docking) algorithms are needed to determine the preferred orientation of a small molecule, drug against a receptor molecule (e.g., protein). With the increasing amount of biological molecule structures available, effective and efficient techniques are needed to treat these structures. Because of this, there is a great need to build more intelligent strategies that can deliberate better solutions to the problem. In this project, we are working on the development of metaheuristics for Structural Bioinformatics problems. Metaheuristics are among the most frequent and powerful techniques used in situations where knowledge about the problem is restricted and exact solutions are not currently computable. Metaheuristics do not guarantee the optimal solution, but they provide a good approximation solution with little computational effort. To tackle the above problems, we propose the development of multimodal and multi-objective metaheuristics models. The new metaheuristics models will enable better exploration of the search space compared to state-of-the-art strategies.
Researchers
Dr. Márcio Dorn - Coordinator
PPGC/INF/UFRGS - Brazil
Dr. Mario Inostroza-Ponta - Coordinator
DIINF/USACH - Chile
Dr. Rodrigo Ligabue Braun
DF/UFCSPA - Brazil
Dr. Bruno César Feltes
PPGC/INF/UFRGS - Brazil
Dr. Joice de Faria Poloni
PPGC/INF/UFRGS - Brazil
Dr. Pedro Henrique Narloch
PPGC/INF/UFRGS - Brazil
Mateus Boiani
Ph.D. Candidate
PPGC/INF/UFRGS - Brazil
Bruno Iochins Grisci
Ph.D. Candidate
PPGC/INF/UFRGS - Brazil
Publications
- ALIAGA-ROJAS, S. ; VILLALOBOS-CID, M. ; DORN, M. ; INOSTROZA-PONTA, M. . A multi-objective approach for the protein structure prediction problem. In: 2021 40th International Conference of the Chilean Computer Science Society (SCCC), 2021, La Serena, pp. 1-8.
- BARBACHAN e SILVA, M. ; NARLOCH, P. H. ; DORN, M. ; BROIN, P. O. . Optimisation of cancer status prediction pipelines using bio-inspired computing. In: IEEE Congress on Evolutionary Computation, 2021, Kraków. v. 1. p. 442-12.
- Narloch, P. H.; Dorn, M. Evaluating the Success-History based Adaptive Differential Evolution in the Protein Structure Prediction problem. In: International Conference on the Applications of Evolutionary Computation, Seville. Applications of Evolutionary Computation. EvoApplications 2021, v. 12694. p. 194-209.
- Narloch, P. H.; Krause, M. J.; Dorn, M. Multi-Objective Differential Evolution Algorithms for the Protein Structure Prediction Problem. In: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, Glasgow. IEEE Congress on Evolutionary Computation (CEC), 2020. v. 1. p. 1-8.
- Tavares, A. U.; Dorn, M. Determining the Conformational Flexibility of Disaccharides with an Adaptive Differential Evolution Approach. In: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, Glasgow. 2020 IEEE Congress on Evolutionary Computation (CEC), 2020. v. 1. p. 1-8.
- Correa, L.; Arantes, L.; Sens, P.; Inostroza-Ponta, M.; Dorn, M. A dynamic evolutionary multi-agent system to predict the 3D structure of proteins. In: 2020 IEEE Congress on Evolutionary Computation (CEC), 2020, Glasgow. 2020 IEEE Congress on Evolutionary Computation (CEC), 2020. v. 1. p. 1-8.
- Grahl, M.; Alcara, A.; Perin, A.P.; Moro, C.; Pinto, E.S.M.; Feltes, B.C.; Ghilardi, I.; Rodrigues, F.V.F.; Dorn, M.; Costa, J.C.; Norberto de Souza, O.; Ligabue-Braun, R.L. Evaluation of drug repositioning by molecular docking of pharmaceutical resources available in the Brazilian healthcare system against SARS-CoV-2. Informatics in Medicine Unlocked, v. 24, p. 100539, 2021.
- Pinto, E.S.M.; Feltes, B. C.; Pedebos, C.; Dorn, M. Modifying the catalytic preference of alpha-amylase towards n-alkanes for bioremediation purposes using in silico strategies. Journal of Computational Chemistry (Online), v. 42, p. 1, 2021.
- Narloch, P.H.; Dorn, M. . Differential Evolution Multi-Objective for Tertiary Protein Structure Prediction. In: International Conference on the Applications of Evolutionary Computation, 2020, Seville. Proceedings of the International Conference on the Applications of Evolutionary Computation, Lecture Notes in Computer Science. 1ed.Cham, Switzerland: Springer International Publishing, 2020, v. 12104, p. 165-180.