Predicting Ligand Binding Modes by Scaffold-Guided Structure Refinement

Benchmarking Docking Protocols on Predicting Alternative Binding Modes

The quality of protein-ligand binding affinity prediction is often limited by the accuracy of positioning the ligand correctly inside the binding site. However, pose accuracy is a secondary concern in high-throughput virtual screening, which is the application scenario in mind when most docking protocols are developed. On the other hand, similar protocols are also applied to position ligands in the binding pocket prior to free-energy calculations, and the accuracy of docking protocols is not well known in this case, especially when structures of analogues are available as templates to guide the docking as is typical in lead optimization. Docking benchmarks typically focus on the ability of docking methods to generate poses with a low RMSD to crystal structures, but for physics-based affinity prediction methods, we also need the ability to identify potential alternate binding modes of a new ligand that might be viable. This is especially relevant for ligand modifications which break local symmetry, leading to multiple potential substituent orientations, such as substitutions of phenyl rings. Here, our focus is on assessment of pose prediction methods when the bound structure of a reference ligand is known (typical in structure-based drug design) and the likely binding mode(s) of a related compound are needed, and we focus on cases where the new compound has multiple potential binding modes. To assess templated docking protocols on their ability to identify binding modes when starting from a solid reference structure, we collected a set of 60 complex structures from the PDB which have more than one ligand binding mode – shown in the PDB records as alternative locations in the ligand. Our results suggest success rates of only 30-50% for finding alternative binding modes, which are modest compared to benchmarks of the same docking programs on the Astex diverse set (70-90% success rates). Overall, we conclude that docking methods would benefit from further tuning or improvement to become more effective in lead optimization.

Open-Sourced In Silico Drug Screening.

Mavchen-1: A Conformational Ensemble Platform for Protein–Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark

A category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein–ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes is presented.

Bridging between Structure-Based and Data-Driven Affinity Prediction.

This work introduces a method to smoothly transition from physics-based to knowledge-based predictions based on the uncertainty of each model and shows that combining structure-based and ML models significantly improves the prediction accuracy if training data is limited, whereas the weighting smoothly shifts from docking to ML as more data is acquired.

Computational Lead Optimization on BACE1: Relative Binding Free Energy Perturbation as the Terminal Refinement Layer

Structure-based drug discovery is known to apply computational methods in a tiered hierarchy, with each layer narrowing the candidate set and refining the binding picture before committing to the next, more expensive step. We present a four-tiered computational benchmarking study evaluating five engines against a panel of 36 compounds targeting β-secretase 1 (BACE1), a validated Alzheimer’s disease target with extensive co-crystal ground truth. This study evaluates Flexible Docking and Boltz2 Cofolding as the primary tier, followed by Ensemble Docking, and then Protein-Ligand MD with MM/PBSA and MM/GBSA post-processing. This is then concluded with Relative Binding Free Energy Perturbation (RevFEP) as the terminal refinement layer. Each method was benchmarked against the experimental binding free energies derived from the co-crystal structures spanning −7.85 to −11.35 kcal/mol. Our findings revealed that Flexible Docking reproduced the co-crystal binding mode for 35 of 36 ligands (97.2% within 2.0 Å RMSD) but did not rank potency at this resolution. Boltz2 CoFolding provided an orthogonal structural cross check with a receptor backbone RMSD of 0.293 Å against the experimental co-crystal structure. Ensemble Docking identified the optimal receptor conformation for downstream FEP setup. MD with MM/GBSA decomposition identified van der Waals complementarity as the primary potency driver (Pearson r = +0.855, R2 = 0.732 on a 10-compound subset). RevFEP delivered the highest affinity correlation of any method (Pearson r = +0.662, R2 = 0.438, Spearman ρ = +0.624, mean absolute error 1.02 kcal/mol across all 36 ligands), resolving potency differences within a narrow 3.5 kcal/mol congeneric window that no other engine could discriminate. We characterize what each engine contributes independently and where RevFEP delivers signals no other engine achieves.

Robust Ligand Docking into Challenging Hydrated Binding Pockets

Correctly positioning water molecules at the drug-target interface is a major challenge in structure-based drug design. Predicting how water structure reorganizes during drug docking into the target pocket is particularly difficult. Previously, a molecular dynamics-based protocol, HydroDock, was introduced and tested for docking small ligands into hydrated ion channels. Here, we extend the applicability of HydroDock to challenging targets with open binding pockets, populated by highly mobile water molecules that are the most troublesome for drug design. HydroDock improved docking accuracy, with increases of 28% (10%) in median structural (and ranking) performance. The comparison with other methods showed that HydroDock is a robust solution, even for problematic systems in which docking without explicit water molecules failed to find the correct drug-binding mode.