Algoritmo genético assistido por surrogate para avaliar e descobrir peptídeos contra o SARS-CoV-2

• Ano de defesa: 2022
• Autor(a) principal: Silva, Elias de Abreu Domingos
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: por
• Instituição de defesa: Universidade Federal de Uberlândia; Brasil; Programa de Pós-graduação em Ciência da Computação
• Palavras-chave em Português: SARS-CoV-2; Design de Peptídeo; Aprendizado de Máquina; COVID-19; Algoritmo Genético; Peptide Design; Machine Learning; Surrogate Models; Genetic Algorithms; CNPQ::CIENCIAS EXATAS E DA TERRA; Computação; COVID-19 (Doença); Aprendizado do computador; Algorítmos genéticos
• Link de acesso: https://repositorio.ufu.br/handle/123456789/36301; http://doi.org/10.14393/ufu.di.2022.571

The design of peptides capable of inhibiting the SARS-CoV-2 viral infection has been considered one of the potential strategies to reduce the transmission of SARS-CoV-2. However, a critical issue in peptide design is the large search space, which makes it impracticable to evaluate all possibilities. Furthermore, most related works adopt in silico molecular docking to select potential peptides, which is a time-consuming technique and highly dependent on the molecular structure of already known peptides and the target protein. Aiming to assist the evaluation, discovery and selection of peptides for docking calculation, we developed SAGAPEP, a Surrogate-Assisted Genetic Algorithm framework capable of finding peptides with potential to block the SARS-CoV-2 Spike protein. The surrogate model is used for fast and high-fidelity evaluation of the interaction energy between a peptide and the Spike protein, while the genetic algorithm seeks to discover and select high-potential peptides inspired by principles of genetics and natural selection. Experiments were conducted using a data set composed of several potential peptides obtained through molecular docking by bio-informatics specialists. As main results, SAGAPEP achieved low error predictions from its surrogate component trained over that data set, and was able to discover and select peptides with higher binding energy than all listed in the data set. Moreover, the noteworthy results of SAGAPEP suggest it may also have the potential to provide promising results for other peptide design problems.