Computational and Statistical Methods to Explore the Various Dimensions of Protein Evolution

Mario   A.   Fares

Abstract

Predicting genes and gene regions undergoing adaptive evolution is one of the most important aims of geneticists and of new emerging areas of investigation. As more genomes are being sequenced and computational tools to detect selection are being developed, the number of genes uncovered as being positively selected is overwhelming. Several statistical methods have been devised to test if specific amino acid regions have undergone adaptive mutations at some stage during the proteins evolution. Despite the sensitivity of these methods to detect selective constraints, they are still based on linear sequence alignments and therefore, examine only one dimension of the proteins evolution. Few methods have been designed to detect intra-molecular co-evolution between amino acid sites. However, no tests are performed to determine the adaptive value of these co-evolutionary events. Conclusions independently derived from both types of methods are ambiguous and seldom unequivocal, since evolution of protein sequences is most likely to be multifactorial. This review discusses and has briefly exposed the advantages and disadvantages of the many different methods and computational tools to detect adaptive evolution and co-evolution. Further, the potential that the combination of such methods has in providing more biologically meaningful results is highlighted.

Keywords: Adaptive evolution, coevolution, statistical methods, maximum likelihood, parsimony, covarion model

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