Protein functional sites prediction based on the evolutionary information

Abstract

It is common belief that a few residues in a protein are important for its function and structure. Furthermore, these sites are usual targets to the protein engineering modifying or improving the functions of the protein. All proteins belong to a protein family, which have similar structure and sequence homology. From this concept, highly conserved sites are considered to be important as a result of natural selection. If those sites are mutated, the fitness of the protein dramatically drops, and protein sequences may be extincted in a gene pool. In other cases, hypervariability is observed in the bindining interface residues of the proteins which have diverse binding parters. Besides conservation and hypervariation, correlated mutation has been another important evolutionary information. Diverse studies have shown that correlated mutation (CM) is an important molecular evolutionary process. However, attempts to find the coevolving residue pairs under the structural and/or functional constraints are complicated by the fact that a large portion of covariance signals found in multiple sequence alignments are from correlations due to sharing common ancestry and stochastic noises. In this thesis, we develop a method to verify the functional sites from sequence information. Motivated by the correlated mutation, the residue-residue coevolution network (RRCN) analysis is developed. RRCN is an network whose nodes are residues and links are set when the coevolutionary interaction strengths between residues are sufficiently large. After constructing the RRCN, we identify residues that have high degree of connectivity and residues that play a central role in network flow of information. These residues are likely to be functionally important residues. Since this method is based on the coevolution and network analysis, the development of more accurate CMA algorithm is required. Assuming that the background noises can be estimated from the coevolutionary relationships amon...