Banca de DEFESA: LAISE CAVALCANTI FLORENTINO

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : LAISE CAVALCANTI FLORENTINO
DATA : 31/10/2018
HORA: 14:00
LOCAL: BioME
TÍTULO:

Using RINs to understand cancer mutations: deleterious mutations are more commonly associated to highly connected amino acids.

PALAVRAS-CHAVES:

Residue Interaction Networks. Mutation effects. Deleterious and neutral mutations. Mutation predictors.

PÁGINAS: 44
GRANDE ÁREA: Ciências Biológicas
ÁREA: Biologia Geral
RESUMO:

In the last decades, advances in whole genomic approaches lead to the identification of a vast number of cancer-related mutations. High-throughput estimations of the impacts of cancer mutations in the protein structure are not an easy accomplishment, and most studies are limited to one-by-one whole structural analyzes. Moreover, there are still many challenges on the way to the precise and automated prediction of pathogenic mutations. Therefore, understanding the structural impact of a particular amino acid change is of great importance for cancer medical research. However, most studies have been emphasizing sequences and structural modifications based on chemical characteristics of amino acids and not fold features, in which the conservation of non-covalent interactions play a significant role. Henceforth, in the present study, we used residue interaction networks (RINs) for large-scale analysis of cancer missense mutations in order to infer their effects on the conservation of non-covalent interactions. We hypothesize that changes in highly connected amino acids are more likely to cause deleterious mutations. To evaluate this, we retrieved cancer missense mutations from COSMIC (cancer.sanger.ac.uk/cosmic) and TCGA (cancergenome.nih.gov) databases and mapped them to their respective structures retrieved from Protein Data Bank (rcsb.org). Then, RINs were constructed from the obtained pdb files, and network parameters such as the node's degree, edges' type, clustering coefficient, betweenness weighted were assessed and plotted using R scripts. Later, we compared these results against reported missense single nucleotide polymorphisms retrieved from dbSNP (www.ncbi.nlm.nih.gov/projects/SNP/) and to pathogenic and non-pathogenic cancer mutations from ClinVar (www.ncbi.nlm.nih.gov/clinvar/) databases. Our results demonstrate that the distribution of mutations per degree (node connectivity) varies significantly compared to random Monte Carlo simulations and also to the distribution of a set of human single nucleotide polymorphisms (SNPs), tending to remain at nodes with lower connectivity. Besides, the proportion of deleterious mutations was significantly increased in nodes with a high degree of connectivity when two different criteria were used for their classification: proportions of software predictors (Ndamage) and clinical classification obtained from ClinVar. Taking into account these results, we can conclude that the changes in the highly connected amino acids are indeed more likely to generate deleterious mutations, due their higher proportion of occurrence in these nodes. Our results also indicate that the conservation of non-covalent interactions is an important parameter to consider in assessing mutations effects and RINs analyses can be used as an additional parameter to aid in the prediction of deleterious mutations in cancer.

MEMBROS DA BANCA:
Presidente - 1513597 - JOAO PAULO MATOS SANTOS LIMA
Interno - 2170415 - JORGE ESTEFANO SANTANA DE SOUZA
Externo à Instituição - VALDIR BALBINO - UFPE