Abstract
The identification of residues that hold misleading phylogenetic signals and those that are of functional significance are intertwined. Advances in the one area can support the other mainly because misleading phylogenetic signals come from residues that are not evolving as a random process. This paper is a case study of the comparison of a widely accepted phylogenetic tree to trees that have been inferred from sequence data of five proteins. A new metric, RI Difference and based on Retention Index, is suggested measuring the relative support that individual sites provide for two trees. Through the identification of sites harboring disproportionally large misleading phylogenetic signal, we attempt to identify residues that are cooperating to define the function of the protein. This information is presented in the presence of the structure of the protein where clustering patterns (or lack of) are observed in the implicated residues. A new bioinformatic software tool, RI Compare, is presented implementing the metric and blending heterogeneous information from alignments, phylogenetic trees, and structure promoting this research. The results are offered followed by some speculation to what might be causing erroneous trees to be inferred. The relationship of the implicated residues to those of known importance is also discussed. While, regrettably, the results of this paper do not seem to suggest that the RI Difference measure is a general measure for the identification of functional important residues in all proteins, there is evidence to suggest it may be applicable to the large transmembrane class of proteins. Unfortunately, no experimental tests of the implicated residues have been performed at this time and judgment of the correctness of the results has been based solely on the proximity of the implicated residues to ligands, other chains, and residues of known importance. However, even if the RI Difference measure is identifying residues other than the functional significant ones, the fact that the cluster patterns are unlikely to occur at random is intriguing.