Utilização de ferramentas computacionais para o estudo do impacto funcional e estrutural de nsSNPs em genes codificadores de proteínas

Abstract

Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation between humans, and have the potential to affect gene function, especially when they are located in coding or regulatory regions. Among the many types of SNPs, nonsynonymous SNPs (nsSNPs) are believed to have the greatest impact on protein function, often being associated to diseases, changes in drug response, and adverse drug reactions. The motivation of this work was the fact that a computational approach could be highly useful in the preliminary evaluation of the functional and structural impact of nsSNPs in protein encoding genes in humans, hence enabling the prioritization of candidate nsSNPs for experimental studies. For this purpose, nsSNP modeling was carried out in their corresponding native protein structures as coded by their genes, aiming to determine the impact caused by these variations using different computational methods, such as molecular docking and protein structure optimization. A database was built, relating results data from the computational analysis carried out with information which already exist, such as disease, metabolic pathways, drug targets, drugs, drug metabolizing enzymes, and protein sequence annotations, enabling the integration of results obtained by different methods used in the study of the impact of nsSNPs on protein function.