CYP4V25 was an ortholog to CYP4Vs yet was below the 55% sequence identity threshold used during standard nomenclature. All of the top BLAST hits for CYP4V25 were CYP4Vs from various species. Furthermore, CYP4Vs have been found in molluscs and crustaceans. Collectively, this information supports the placement of this sequence into the CYP4V family despite the low sequence identity to other gene members. Little is known of CYP4 function outside vertebrates. CYP4C has a role in juvenile hormone synthesis in the cockroach Blaberus discoidalis. In vertebrates, CYP4s primarily metabolize endogenous compounds, specifically fatty acids, although they do metabolize some exogenous pharmaceuticals. Yet, even in mammals the function of CYP4V is unknown. Determining which residues of a protein control its biological functions is a classical and unsolved problem in molecular biology. For example the biochemistry of allosteric enzymes has long been studied, but it is not in general known which residues produce the allosteric response, even for proteins that have been exceedingly well OTX015 abmole bioscience studied such as hemoglobin. The growth in the number of available sequences has given rise to the intriguing possibility of using the phenotypic diversity contained in multiple sequence alignments to address this question. Given both a sequence alignment containing a large number of homologous proteins, and a phenotype of interest, can an algorithm be developed to identify those residues that control this phenotype? By phenotype we mean the functional properties of a protein, such as melting temperature, interaction partners, or substrate specificity. Since protein phenotypes such as these are often controlled by a collection of residues, it is unlikely that patterns of individual mutations contain enough information to identify residues controlling the functional variation between different members of the same family. A pair of algorithms, featured in a number of recent papers, have provided compelling experimental evidence that detection of correlated pairs of residues can identify groups of residues that control different protein phenotypes. Using statistical coupling analysis Halabi et al. identify groups of residues that control the structural stability and enzyme activity of the serine proteases. SCA analysis was recently used to identify residues involved in the control of allosteric regulation both within and between protein domains and residues important for both function and adaptation. In addition, using mutual information Skerker et al. identify specificity-determining residues in bacterial signal transduction proteins. These sets of studies carry out extensive experiments to validate their predictions, which are obtained using two different algorithms to detect correlated residue pairs. To test the importance of the choice of algorithm, we repeated the analyses in with the algorithms swapped, namely using mutual information to analyze the serine proteases, and SCA to analysis the signal transduction proteins.