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

Cross-oncopanel study reveals high sensitivity and accuracy with overall analytical performance depending on genomic regions.

Targeted sequencing using oncopanels requires comprehensive assessments of accuracy and detection sensitivity to ensure analytical validity. By employing reference materials characterized by the U.S. Food and Drug Administration-led SEquence Quality Control project phase2 (SEQC2) effort, we perform a cross-platform multi-lab evaluation of eight Pan-Cancer panels to assess best practices for oncopanel sequencing. All panels demonstrate high sensitivity across targeted high-confidence coding regions and variant types for the variants previously verified to have variant allele frequency (VAF) in the 5-20% range. Sensitivity is reduced by utilizing VAF thresholds due to inherent variability in VAF measurements. Enforcing a VAF threshold for reporting has a positive impact on reducing false positive calls. Importantly, the false positive rate is found to be significantly higher outside the high-confidence coding regions, resulting in lower reproducibility. Thus, region restriction and VAF thresholds lead to low relative technical variability in estimating promising biomarkers and tumor mutational burden. This comprehensive study provides actionable guidelines for oncopanel sequencing and clear evidence that supports a simplified approach to assess the analytical performance of oncopanels. It will facilitate the rapid implementation, validation, and quality control of oncopanels in clinical use.All SEQC2 participants freely donated their time, reagents, and computing resources for the completion and analysis of this project. Part of this work was carried out with the support of the Intramural Research Program of the National Institutes of Health (to Mehdi Pirooznia), National Institute of Environmental Health Sciences (to Pierre Bushel), and National Library of Medicine (to Danielle Thierry-Mieg, Jean Thierry-Mieg, and Chunlin Xiao). Leming Shi and Yuanting Zheng were supported by the National Key R&D Project of China (2018YFE0201600), the National Natural Science Foundation of China (31720103909), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01). Donald J. Johann, Jr. acknowledges the support by FDA BAA grant HHSF223201510172C. Timothy Mercer and Ira Deveson were supported by the National Health and Medical Research Council (NHMRC) of Australia grants APP1108254, APP1114016, and APP1173594 and Cancer Institute NSW Early Career Fellowship 2018/ECF013. This research has also been, in part, financially supported by the MEYS of the CR under the project CEITEC 2020 (LQ1601), by MH CR, grant No. (NV19-03-00091). Part of this work was carried out with the support of research infrastructure EATRIS-CZ, ID number LM2015064, funded by MEYS CR. Boris Tichy and Nikola Tom were supported by research infrastructure EATRIS-CZ, ID number LM2018133 funded by MEYS CR and MEYS CR project CEITEC 2020 (LQ1601).S