Mutagraph: modelos e algoritmos para predição na afinidade de complexos proteicos através de Graph Kernel e métricas de redes complexas

• Ano de defesa: 2017
• Autor(a) principal: Laerte Mateus Rodrigues
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Minas Gerais; UFMG
• Palavras-chave em Português: Bioinformática; Mutação; Redes complexas; Proteinas; análise de regressão
• Link de acesso: http://hdl.handle.net/1843/BUOS-B97EFJ

Mutations in coding regions can aect the structure and the function of a protein leading to malfunction and still related to hereditary disorders and propensity to several cancers. Missense mutation types, where have the change of one amino acid to another it is a common type of genetic exchange could aect the proteins function by destabilizing and/or anity change between the protein and others partners, it will be small molecules and other proteins. In spite of relevant eorts describedintheliteratureinelucidatingtherelationshipbetweenthemissensemutation and your impact on protein stability structure and therefore your function, predicting your mutation in the anity of the protein quaternary complex is still a great challenge. Protein-protein interactions are essential for the performance of various functions in the body and are carefully regulated. Understanding how mutations can aect the anity of protein complexes may aid in understanding their role in diseases as well as providing the engineering of protein interfaces for biotechnological purposes. In this context, we present MutaGraph, a new computational, quantitative and three-dimensional approach based on the prediction of the eects of missense mutations on the anity of protein complexes based on graph kernels and complex network metrics. Using databases that describe mutations in protein complex interfaces with resolved structures and experimentally determined thermodynamic parameters of their eects, we use supervised learning techniques to train and evaluate predictive models. MutaGraph was able to successfully predict the eect of mutations in protein interfaces, achieving a Pearson correlation of up to 0,84 in cross-validation. The proposed method is freely available as a web server, which implements techniques for visualizing the eect of mutations and can be accessed at http://bioinfo.umfg.br/mutagraph.