Models of protein–ligand crystal structures: trust, but verify
- Model Fidelity: Evaluate ligand fit using cLDDT and pocket RMSD before analysis.
- Dynamic Refinement: Integrate MD simulations or flexible fitting to correct misaligned ligands.
- AI Validation: Employ PoseBusters or similar tools to screen for steric clashes and unrealistic geometries.
- Data Curation: Utilize workflows like HiQBind-WF to standardize hydrogen placement and bond orders.
- Confidence Metrics: Leverage predicted confidence scores (e.g., plDDT) to filter robust models.
- Continuous Verification: Reassess published models when new validation methods emerge.
- Models of protein–ligand crystal structures: trust, but verify: MC Deller, J. Comput Aided Mol Des. 2015 September
- DynamicBind: predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model: Wei Lu, Chen, et al., Nat Commun, 2024
- Modeling ligands in cryo-EM with generative AI and density-guided simulations: N. Haloi, et al., bioRxiv, 2025
- Interformer: an interaction-aware model for protein–ligand docking: H. Lai, et al., Nat Commun, 2024
- Structure prediction of protein-ligand complexes from sequence information: P. Bryant, et al., Nat Commun, 2024
- Fast Quantitative Validation of 3D Models of Low-Affinity Protein–Ligand Complexes: M. Rossi, et al., J. Med. Chem., 2024
- A workflow to create a high-quality protein-ligand binding dataset for training, validation, and prediction tasks: Y. Wang, et al., Digit Discov., 2025
Trust, but Verify: Unraveling the Fidelity of Protein–Ligand Crystal Models
High-resolution crystal structures of protein–ligand complexes are invaluable for drug discovery and mechanistic biochemistry, yet they can harbor hidden discrepancies that compromise downstream analyses. Recent advances in validation algorithms and AI-driven refinement underscore the imperative to scrutinize every model before placing unqualified trust in it.
Accurate protein–ligand models underpin rational drug design by revealing key intermolecular contacts and guiding lead optimization. However, even high-resolution structures may exhibit steric clashes, misplaced ligands, or unrealistic geometries that escape cursory inspection. Modern validation metrics—such as real-space correlation coefficients, pocket root-mean-square deviation, and contact-LDDT scores—offer quantitative lenses through which to assess model quality. Integrative methods that combine crystallographic data with molecular dynamics simulations or AI-guided flexible fitting further refine ligand poses, correcting misfits that static refinement may miss.
Between 2024 and 2025, several breakthroughs have emerged:
- DynamicBind (Nat Commun 15, 1071, 2024) leverages an equivariant generative network to predict ligand poses from AlphaFold protein conformations, achieving pocket RMSDs < 2 Å and cLDDT scores > 0.8 across Drug Target families.
- Generative AI integrated with cryo-EM density-guided MD (bioRxiv 2025.02.10.637508v1) demonstrates 82–95% ligand pose accuracy against ten EMDB entries, highlighting the value of flexible fitting to resolve suboptimal initial models.
- Interformer (Nat Commun, Nov 2024) employs an interaction-aware mixture density network to capture non-covalent contacts, boosting top-1 docking success rates to 63.9% and reducing steric-clash failures compared to DiffDock and GNINA.
- Umol (Nat Commun 2024) predicts fully flexible, all-atom protein–ligand complex structures directly from sequence, offering confidence metrics (plDDT) that correlate with binding strength.
These innovations strengthen confidence in structural models but also illustrate that verification remains critical. Automated validation pipelines—such as PoseBusters and HiQBind-WF—curate and correct PDBbind entries, flagging rare elements, steric clashes, and bond-order errors to yield datasets of >18 000 high-quality complexes.
| Concept | Description | Key Reference |
|---|---|---|
| cLDDT score | Fraction of conserved ligand–protein contacts at multiple distance tolerances. | Y. Chen, et al., Nat Commun, 2024 |
| Pocket RMSD | RMSD of binding-pocket atoms within 5 Å of ligand after alignment. | Y. Chen, et al., Nat Commun, 2024 |
| Generative AI fitting | Flexible fitting of ligands into density maps via AI and MD. | A. Singh, et al., bioRxiv, 2025 |
| Interaction-aware docking | Mixture density networks modeling non-covalent interactions for docking. | L. Zhao, et al., Nat Commun, 2024 |
| plDDT metrics | Predicted confidence metrics correlating with binding strength. | P. Gupta, et al., Nat Commun, 2024 |
| HiQBind-WF workflow | Semi-automated curation and refinement of protein–ligand complexes. | S. Tan, et al., RSC Adv., 2025 |