Skill: zackz2025/protein-ligand-docking (version 2.3.0)

--- name: protein-ligand-docking description: | Run a protein-ligand docking workflow for research questions about target binding, selectivity, and structural plausibility. Use this skill when the user asks whether a ligand may bind a protein target, wants a docking-oriented comparison across species or homologs, or needs a stepwise in silico workflow that uses sequence retrieval, structure search, AlphaFold/Colab, and AutoDock Vina. --- # Protein-Ligand Docking 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](./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](./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](./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](./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](./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](./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](./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](./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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: suspicious (MEDIUM)