Exploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation

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dc.authoridMacKerell, Alex/0000-0001-8287-6804
dc.authoridKurkcuoglu, Ozge/0000-0003-0228-3211
dc.contributor.authorInan, Tugce
dc.contributor.authorYuce, Merve
dc.contributor.authorMacKerell Jr, Alexander D.
dc.contributor.authorKurkcuoglu, Ozge
dc.date.accessioned2025-03-09T10:49:01Z
dc.date.available2025-03-09T10:49:01Z
dc.date.issued2024
dc.departmentİstanbul Beykent Üniversitesi
dc.description.abstractG protein-coupled receptors (GPCRs) play a central role in cellular signaling and are linked to many diseases. Accordingly, computational methods to explore potential allosteric sites for this class of proteins to facilitate the identification of potential modulators are needed. Importantly, the availability of rich structural data providing the locations of the orthosteric ligands and allosteric modulators targeting different GPCRs allows for the validation of approaches to identify new allosteric binding sites. Here, we validate the combination of two computational techniques, the residue interaction network (RIN) model and the site identification by ligand competitive saturation (SILCS) method, to predict putative allosteric binding sites of class A GPCRs. RIN analysis identifies hub residues that mediate allosteric signaling within a receptor and have a high capacity to alter receptor dynamics upon ligand binding. The known orthosteric (and allosteric) binding sites of 18 distinct class A GPCRs were successfully predicted by RIN through a dataset of 105 crystal structures (91 ligand-bound, 14 unbound) with up to 77.8% (76.9%) sensitivity, 92.5% (95.3%) specificity, 51.9% (50%) precision, and 86.2% (92.4%) accuracy based on the experimental and theoretical binding site data. Moreover, graph spectral analysis of the residue networks revealed that the proposed sites were located at the interfaces of highly interconnected residue clusters with a high ability to coordinate the functional dynamics. Then, we employed the SILCS-Hotspots method to assess the druggability of the novel sites predicted for 7 distinct class A GPCRs that are critical for a variety of diseases. While the known orthosteric and allosteric binding sites are successfully explored by our approach, numerous putative allosteric sites with the potential to bind drug-like molecules are proposed. The computational approach presented here promises to be a highly effective tool to predict putative allosteric sites of GPCRs to facilitate the design of effective modulators.
dc.description.sponsorshipNational Center for High Performance Computing of Turkey (UHeM); University of Maryland Computer-Aided Drug Design Center [2211/C]; TUBITAK National Ph.D. Scholarship Program in the Priority Fields in Science and Technology [2211/C]; Istanbul Technical University Scientific Project [THD-2024-45545, TDK-2020-42717]; NIH [R35 GM131710]; TUBITAK ULAKBIM High Performance and Grid Computing Center (TRUBA)
dc.description.sponsorshipComputing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM), the TUBITAK ULAKBIM High Performance and Grid Computing Center (TRUBA), and the University of Maryland Computer-Aided Drug Design Center. M.Y. thanks to the TUBITAK National Ph.D. Scholarship Program in the Priority Fields in Science and Technology (2211/C). O.K. acknowledges Istanbul Technical University Scientific Project THD-2024-45545 and TDK-2020-42717 and A.D.M., Jr., acknowledges support from the NIH (R35 GM131710).
dc.identifier.doi10.1021/acsomega.4c06172
dc.identifier.endpage40171
dc.identifier.issn2470-1343
dc.identifier.issue38
dc.identifier.pmid39346853
dc.identifier.scopus2-s2.0-85204038038
dc.identifier.scopusqualityQ1
dc.identifier.startpage40154
dc.identifier.urihttps://doi.org/10.1021/acsomega.4c06172
dc.identifier.urihttps://hdl.handle.net/20.500.12662/4696
dc.identifier.volume9
dc.identifier.wosWOS:001313795900001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherAmer Chemical Soc
dc.relation.ispartofAcs Omega
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250310
dc.subjectProtein-Coupled Receptors
dc.subjectAllosteric Communication
dc.subjectBacterial Ribosome
dc.subjectHot-Spots
dc.subjectDynamics
dc.subjectPharmacology
dc.subjectSimulations
dc.subjectPathways
dc.subjectMotions
dc.subjectTargets
dc.titleExploring Druggable Binding Sites on the Class A GPCRs Using the Residue Interaction Network and Site Identification by Ligand Competitive Saturation
dc.typeArticle

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