Protein-Ligand Docking

Run a protein-ligand docking workflow for research questions about target binding, selectivity, and structural plausibility. Use this skill when the user ask...

Use this skill for research questions such as:

• "Can ligand X plausibly bind protein Y?"
• "Is this inhibitor likely to be selective between bacterial and human homologs?"
• "Should we continue to docking, or is sequence/structure divergence already too large?"

Keep the workflow practical. If an early step already rules out a meaningful docking analysis, stop and explain why instead of forcing the full pipeline.

Inputs To Collect First

Ask for or infer:

• target protein name and species
• ligand name and available structure format
• whether the user wants a quick feasibility screen or a fuller workflow
• whether an experimental structure already exists

Useful concrete inputs:

• UniProt ID or protein sequence
• ligand SDF or SMILES
• known PDB ID, if available
• comparison target, if this is a selectivity question

Workflow

1. Sequence Retrieval

• Retrieve the target sequence from UniProt when the user provides a protein name or UniProt ID.
• Save FASTA files with clear names because later scripts depend on them.
• If the question is about selectivity, retrieve both sequences before moving on.

2. Structure Search

• Search RCSB PDB for experimentally solved structures first.
• Prefer structures with a relevant ligand, catalytic domain, or biologically meaningful complex.
• If no suitable structure exists, plan to use AlphaFold or AlphaFold-Multimer in Colab.

3. Sequence Conservation Check

When the question involves homolog comparison, run scripts/step3_alignment.py.

• High similarity suggests the binding region may be conserved and docking can be informative.
• Borderline similarity means docking may still help, but interpretation must stay cautious.
• Very low similarity can support an early "binding pocket likely not conserved" conclusion.

Detailed interpretation thresholds live in references/decision-guide.md.

4. Structure Modeling

Use AlphaFold-Multimer only when a suitable experimental structure is missing and a complex model is still needed.

• The Colab template is in references/alphafold_multimer_colab.ipynb.
• Include only biologically relevant chains.
• Tell the user clearly when this step requires manual Colab execution.

5. Model Quality Assessment

Before docking an AlphaFold-derived structure, run scripts/step5_pae_analysis.py.

Focus on two questions:

• Is the fold itself credible enough to use?
• Is the interface or predicted docking region reliable enough to interpret?

If interface confidence is poor, stop and say docking would likely be misleading.

6. Docking

Run scripts/step6_vina_docking.py when all of the following are true:

• the receptor structure is usable
• the ligand structure is available
• the docking box is justified by structure or interface analysis

Prefer docking settings derived from the modeled or known interaction region, not arbitrary whole-protein boxes.

7. Report The Result

Use scripts/step7_summary_report.py when the user wants a structured deliverable.

The final answer should cover:

• binding affinity range, not just the single best score
• whether the pose lands in a biologically meaningful region
• whether the structure quality supports interpretation
• what the main uncertainty is
• what experimental validation would best test the claim

Decision Rules

Use these rules during execution:

• Do not treat docking as proof of binding.
• Do not continue if the structure or interface confidence is clearly too poor.
• Do not over-interpret small score differences across targets.
• If the user only needs a quick answer, stop once the evidence is sufficient.
• For biomedical research, always separate computational plausibility from experimental validation.

Thresholds, QC checks, and result wording guidance are in references/decision-guide.md.

Expected Outputs

Depending on the stage reached, provide some or all of:

• FASTA files for targets
• selected PDB IDs or modeled structures
• alignment summary JSON
• model quality JSON with grid box coordinates
• docking summary JSON
• a short written conclusion in plain language
• optional Summary.md, Summary.docx, and figure output

Dependencies

This skill may rely on:

• UniProt and RCSB web access
• Google Colab for AlphaFold-Multimer
• Python 3 plus Biopython, NumPy, RDKit, OpenBabel, and py3Dmol
• AutoDock Vina in WSL or Linux

Installation notes and recommended thresholds are in references/decision-guide.md.

Limits To State Explicitly

Always warn the user about the main limits:

• docking scores are approximate, not definitive
• static docking ignores induced fit and many solvent effects
• AlphaFold confidence does not guarantee a correct ligand-binding geometry
• experimental assays remain the standard for validation