Effective estimation of the minimum number of amino acid residues required for functional divergence between duplicate genes.

One of hot research foci has always been predicting amino acid residues underlying functional divergence after gene duplication, as those predicted sites can be used as candidates for further functional experimentations. It is important and interesting to know how many sites, on average, may have been responsible for the functional divergence between duplicate genes. In this article, we studied two basic types of functional divergence (type-I and type-II) in depth in order to give an accurate estimation of functional divergence-related sites. Type-I divergences result from altered functional constraints (i.e., different evolutionary rates) between duplicate genes, whereas type-II divergences refer to residues that are conserved by functional constraints but exhibit different physicochemical properties (e.g., charge or hydrophobicity) between duplicates. An effective site number (NE) strategy was applied in our study, which implements a stepwise regression model to calculate the minimum number of residues responsible for functional divergence without choosing preset threshold. We found that NE-determined cut-off value varies among different duplicate pairs, suggesting that empirical cutoff value is not suitable for every case. Under our standard NE calculation method, we estimated less than 15% of residues that are required for paralogous gene functional divergence. Finally, we established a database, DIVERGE-D, as a public resource for the predicted NE sites between two paralogs in this study, which can be used as candidates for further biological engineering and experimentation.

Paralog-divergent Features May Help Reduce Off-target Effects of Drugs: Hints from Glucagon Subfamily Analysis.

Side effects from targeted drugs remain a serious concern. One reason is the nonselective binding of a drug to unintended proteins such as its paralogs, which are highly homologous in sequences and have similar structures and drug-binding pockets. To identify targetable differences between paralogs, we analyzed two types (type-I and type-II) of functional divergence between two paralogs in the known target protein receptor family G-protein coupled receptors (GPCRs) at the amino acid level. Paralogous protein receptors in glucagon-like subfamily, glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R), exhibit divergence in ligands and are clinically validated drug targets for type 2 diabetes. Our data showed that type-II amino acids were significantly enriched in the binding sites of antagonist MK-0893 to GCGR, which had a radical shift in physicochemical properties between GCGR and GLP-1R. We also examined the role of type-I amino acids between GCGR and GLP-1R. The divergent features between GCGR and GLP-1R paralogs may be helpful in their discrimination, thus enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.