Bioinformatic Analysis of Protein Mutations

Many gene defects have been associated to genetic disorders, but the details of molecular mechanisms by which they contribute to the disease are often unclear. The study of mutation effects at the protein level can help elucidate the biological processes involved in the disease and the role of the protein in it. Bioinformatics can help to address this problem, being the connection between different disciplines including clinical, genetics, structural biology, and biochemistry. By using a computational approach I tackled the analysis of some examples of biomedical interesting proteins integrating various sources of data and addressing experimental and clinical investigations. Experimentally defined structures and molecular modelling were used as a basis to determine the protein structure-function relationship, which is essential to gain insights into disease genotype-phenotype correlation. Proteins have been further analyzed in their context, considering interactions that they take in specific cellular compartments. The results have been used to formulate functional hypotheses, which in some cases have been tested and confirmed by further investigations performed by cooperation groups. Mutations found in genes encoding these proteins have been evaluated for their impact on the protein structure and function by using several available prediction methods. These studies provided the idea for developing novel approaches, using residue interaction networks and an ensemble of methods. A novel strategy has been also designed to evaluate genomic data obtained by next generation sequencing technology. This consists in using available resources and software to prioritize rare functional variants and estimate their contribution to the disease. The novel approaches developed in this thesis have been applied and assessed at the Critical Assessment of Genome Interpretation (CAGI) experiment in 2011, providing in some cases very successful results