Algoritmos genéticos para identicação de sítios ativos em enzimas

Abstract

Currently, 25% of proteins annotated in the Protein Families Database (Pfam) have their function unknown. Experimental tests are expensive and time-consuming, and research has shown that the function of a protein can be successfully inferred based on the sequence or structure similarity of a hypothetical function and other functions of known function.A way of predicting the function of a protein is to consider its binding sites. Binding sites are regions in the surface of an enzyme designed to interact with other molecules. Due to its importance to enzyme function, the residues in the active site are more conserved than the sequence as a whole, providing important information for function prediction. Hence, active sites are a rich source of information for protein function prediction.Many methods have been previously proposed to identify active sites based on similarity. However, they do present some limitations, such as not being capable of dealing with conservative mutations (which occur when enzymes with the same function dier in terms of active site residues composition), having diculties in assigning the active siteto a chain or restricting the number of residues in the template. The main goal of this thesis is to propose a new method for searching for activesites similar using genetic algorithms based on protein structural data, namely Genetic Active Site Search (GASS). The method is based on a genetic algorithm, modeled to use structural information from an active site template in the search for enzymes with similar active sites. The method can nd active sites with residues in dierent chains and is ableto handle conservative mutations, apart from not imposing any restrictions on the number of residues in the active site and the distance between them. GASS results were compared with catalytic sites noted in the Catalytic Site Atlas (CSA) using four dierent data sets. When compared to other search methods of catalytic sites, the results showed that GASS identied correctly over 90% of the surveyed sites. Experiments were also performed using data of binding sites from the competitionCASP 10, and when compared with the 17 participants methods, GASS appeared in fourth, regardless of not being initially developed with this purpose.