2026-10-13T01:41:40Z https://api.figshare.com/v2/oai

oai:figshare.com:article/32641641 2026-10-01T16:21:34Z portal_1172 item_type_8 month_year_10_2026

Understanding allosteric and biased GPCR computer simulations and mutagenesis Abdul-Akim Guseynov (24170127) PUREID: 653237342 allosteric sites allosteric modulators G-protein coupled receptors GPCRs molecular dynamics simulations mutagenesis virtual screening computer-aided drug design structure-based drug design non-canonical GPCR activation Membrane-embedded G-protein coupled receptors (GPCRs) are a major class of drug targets, and their allosteric ligands molecules that bind outside the main receptor cavity can trigger unique receptor responses and can be designed against receptors whose natural ligands are not selective enough or not drug-like. Strongly biased allosteric ligands that modify the response pattern of the receptor can yield therapeutics with hiter efficacy or fewer side effects. Structure-based virtual screening methods scan molecular databases against a cavity of the receptor to select its like y ligands. For allosteric GPCR ligands, such design is limited by the ability to find drug-compatible pockets outside the main receptor cavity, by the ability to screen molecules based on receptor interactions, especially within membrane, and by the level of understanding of allosteric and (or) biased ligand mechanism of action. To help identify allosteric sites, we combine and compare in silico approaches to detect allosteric pockets for a promising GPCR and then evaluate their functional relevance by in vitro mutagenesis studies and combine experimental data with computer simulations to gain insights for possible ligand design. To help target allosteric pockets in virtual screening, we establish an open-source screening pipeline tested against the identified pockets and develop high-throughput and versatile in silico tools helping to find chemically diverse ligands. To aid in rational design of allosteric and biased ligands, we collaborate with structural biology and pharmacology groups to discover a way to achieve receptor signal bias and to reveal two novel mechanisms of receptor activation which lie in stark contrast with the traditional activation model. Thesis is embargoed until 31 December 2027. 2026-10-01T16:21:34Z Text Thesis 10.17034/32641641.v1 https://figshare.com/articles/thesis/Understanding_allosteric_and_biased_GPCR_computer_simulations_and_mutagenesis/32641641 All Rights Reserved Open Access after 2027-12-31