bioRxivBiochemistryPreprint
Beyond the 1:1 Ligand-Protein Paradigm: An In Silico Assay for Competitive Ligand Binding
Vince Bart CardenasiD, Stefano RanioloiD, Paolo ConflittiiD, Vittorio LimongelliiD
Abstract
Competitive binding assays (CBAs) are widely used in drug discovery to quantify and compare ligand/receptor affinities. However, their molecular interpretation is often limited by the inherent complexity of ligand-receptor interactions and the transient nature of binding events. All-atom molecular dynamics (MD) simulations offer valuable mechanistic insights in ligand binding studies, but remain computationally prohibitive for capturing the long-timescale, multiligand behavior characteristic of CBAs. Therefore, characterizing the molecular aspects of CBAs remains a fundamental challenge in molecular biophysics. Here, we introduce a coarse-grained MD (CGMD) approach capable of recapitulating CBA-like dynamics between ligands of opposing efficacy-- the full agonist NECA and the inverse agonist ZM241385-- at the adenosine A2A receptor, a prototypical G protein-coupled receptor and a key pharmacological target. By simulating ligand mixtures at varying molar ratios, we capture hallmark features of experimental CBAs, including spontaneous binding, unbinding, and direct competition at the orthosteric site. Our simulations reveal an extracellular vestibular site that modulates ligand access to the binding pocket. Occupation of this site by NECA facilitates ZM241385 entry and prolongs its residence time, revealing a cooperative mechanism within an otherwise competitive process. These findings offer a molecular perspective on lig- and competition at GPCRs and demonstrate the potential of CGMD as a viable method for probing multiligand dynamics in drug discovery.
- assay: structural biology
- research focus: drug response / drug discovery
- research focus: protein folding & dynamics
- research focus: cell signaling & trafficking
- research focus: chromatin & epigenetic regulation
- approach: deep learning
- approach: mechanistic / dynamical model
- Citations
- 1
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