Computational Prediction of Phylogenetically Conserved Sequence Motifs for Five Different Candidate Genes in Type II Diabetic Nephropathy

Computational identification of phylogenetic motifs helps to understand the knowledge about known functional features that includes catalytic site, substrate binding epitopes, and protein-protein interfaces. Furthermore, they are strongly conserved among orthologs, indicating their evolutionary importance. The study aimed to analyze five candidate genes involved in type II diabetic nephropathy and to predict phylogenetic motifs from their corresponding orthologous protein sequences. AKR1B1, APOE, ENPP1, ELMO1 and IGFBP1 are the genes that have been identified as an important target for type II diabetic nephropathy through experimental studies. Their corresponding protein sequences, structures, orthologous sequences were retrieved from UniprotKB, PDB, and PHOG database respectively. Multiple sequence alignments were constructed using ClustalW and phylogenetic motifs were identified using MINER. The occurrence of amino acids in the obtained phylogenetic motifs was generated using WebLogo and false positive expectations were calculated against phylogenetic similarity. In total, 17 phylogenetic motifs were identified from the five proteins and the residues such as glycine, leucine, tryptophan, aspartic acid were found in appreciable frequency whereas arginine identified in all the predicted PMs. The result implies that these residues can be important to the functional and structural role of the proteins and calculated false positive expectations implies that they were generally conserved in traditional sense. The prediction of phylogenetic motifs is an accurate method for detecting functionally important conserved residues. The conserved motifs can be used as a potential drug target for type II diabetic nephropathy.