Benchmarking Docking Protocols for GPCR Allosteric Modulators
Thompson, T. D.; Miao, Y.
Show abstract
G protein-coupled receptor (GPCR) allosteric modulators (AMs) offer significant therapeutic advantages over orthosteric drugs, yet structure-based virtual screening lacks validated protocols accounting for the conformational complexity of GPCR allosteric sites. We benchmark docking protocols using PDB experimental structures and structural ensembles derived from Gaussian accelerated Molecular Dynamics (GaMD) simulations across four Class A GPCRs (including the muscarinic M2 and M4 receptors, the {beta}2-adrenergic receptor, and the C-C chemokine receptor type 2) with four programs (Glide HTVS, AutoDock Vina, DOCK3.8, and Boltz-2) against experimentally validated modulator libraries and property-matched decoys. GaMD ensemble docking improved early AM enrichment across all four targets under at least one program. Glide ensemble docking was the only protocol to consistently improve early AM recovery across all four targets, ranking known actives almost exclusively within the top 0.5% of compounds at CCR2 and improving M2R active recovery nearly 9-fold relative to the PDB structure. GaMD free-energy landscape topology governed ensemble re-ranking strategy selection: population-skewed landscapes favored top binding energy ranking (BEmin) while flat, multi-populated landscapes favored average binding energy ranking (BEavg), and at targets with dominant low-energy states, a single GaMD cluster matched or exceeded full ensemble or PDB performance. Taking the union of top percentile hits identified by both ensemble re-ranking methods, BEmin / BEavg, maximizes chemical diversity at the earliest percentiles. Program-specific scaffold recovery biases further motivated a consensus BEmin / BEavg approach to maximize hit diversity. The Boltz-2 deep-learning program showed minimal sensitivity to GaMD templates and underperformed conventional docking, suggesting its affinity predictions complement rather than replace physics- and empirical-based docking approaches for GPCR AM screening.
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Merging Bioactivity Predictions from Cell Morphology and Chemical Fingerprint Models Using Similarity to Training Data 93%
- Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors 93%
- BitterMatch: Recommendation systems for matching molecules with bitter taste receptors 93%
Similar papers in this journal
- AI-Assisted Chemical Probe Discovery for the Understudied Calcium-Calmodulin Dependent Kinase, PNCK 94%
- Molecular mechanisms of fentanyl mediated β-arrestin biased signaling 93%
- Rational Discovery of Dual-Action Multi-Target Kinase Inhibitor for Precision Anti-Cancer Therapy Using Structural Systems Pharmacology 93%
Similar papers in this journal
- EVOSYNTH: Enabling Multi-Target Drug Discovery through Latent Evolutionary Optimization and Synthesis-Aware Prioritization 94%
- Using macromolecular electron densities to improve the enrichment of active compounds in virtual screening 93%
- A small molecule enhances arrestin-3 binding to the β2-adrenergic receptor 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.