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It is suggested that Gα protein selectivity is an allosterically encoded property of GPCRs and provide a conceptual framework for designing ligands and receptors with tailored Gα protein-biased signaling.
This work developed an interpretable probabilistic framework that extracts residue-level dependencies from MD ensembles using Bayesian network modeling (BNM) and establishes a generalizable and interpretable framework for uncovering GPCR allosteric communication networks and discovering modulators that exploit these ne...
This study leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor.
A computational framework integrating molecular dynamics (MD)-derived structural, energetic, thermodynamic, and contact-based descriptors with machine learning may inform the design of inhibitors targeting secondary KRAS resistance mutations, pending validation in additional structurally independent mutant systems.
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