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Yangyun Zou

Publications and source records attributed to Yangyun Zou.

2 recordsLinked to original sources

Case study: Paralog diverged features may help reduce off-target effects of drugs

Side effects from targeted drugs is 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 exhibit similar structures and drug-binding pockets. In this study, we analyzed amino acid residues with type-II functional divergence, i.e., sites that are conserved in sequence constraints but differ in physicochemical properties between paralogs, to identify targetable differences between two paralogs. We analyzed paralogous protein receptors in the glucagon-like subfamily, glucagon receptor (GCGR) and glucagon-like peptide-1 receptor (GLP-1R), which are clinically validated drug targets in patients with type 2 diabetes and exhibit divergence in ligands, showing opposing roles in regulating glucose homeostasis. We identified 8 residues related to type-II functional divergence, which are conserved in functional constraints but differ in physicochemical properties between GCGR and GLP-1R. We detected significant enrichment of predicted residues in binding sites of the antagonist MK-0893 to GCGR. We also identified a type-II functional divergence-related residue involved in ligand-specific effects that was critical for agonist-mediated activation of GLP-1R. We describe the important role of type-II functional divergence-related sites in paralog discrimination, enabling the identification of binding sites to reduce undesirable side effects and increase the target specificity of drugs.

Bioinformatics

A New Mutation-Profile-Based Method for Understanding the Evolution of Cancer Somatic Mutations

Human genes exhibit different effects on fitness in cancer and normal cells. Here, we present an evolutionary approach to measure the selection pressure on human genes, using the well-known ratio of the nonsynonymous to synonymous substitution rate in both cancer genomes (CN/CS) and normal populations (pN/pS). A new mutation-profile-based method that adopts sample-specific mutation rate profiles instead of conventional substitution models was developed. We found that cancer-specific selection pressure is quite different from the selection pressure at the species and population levels. Both the relaxation of purifying selection on passenger mutations and the positive selection of driver mutations may contribute to the increased CN/CS values of human genes in cancer genomes compared with the pN/pS values in human populations. The CN/CS values also contribute to the improved classification of cancer genes and a better understanding of the onco-functionalization of cancer genes during oncogenesis. The use of our computational pipeline to identify cancer-specific positively and negatively selected genes may provide useful information for understanding the evolution of cancers and identifying possible targets for therapeutic intervention.

Cancer Biology