General

tooluniverse-protein-structural-annotation-pdb - Claude MCP Skill

Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.

SEO Guide: Enhance your AI agent with the tooluniverse-protein-structural-annotation-pdb tool. This Model Context Protocol (MCP) server allows Claude Desktop and other LLMs to given a pdb structure, produce a per-residue annotation table: which residues sit at a binding inter... Download and configure this skill to unlock new capabilities for your AI workflow.

🌟9 stars β€’ 254 forks
πŸ“₯0 downloads

Documentation

SKILL.md
# Protein structural annotation from a PDB

For each residue of a target protein chain, classify whether it sits at a
binding interface, in a ligand pocket, is buried vs solvent-exposed, and
(optionally) which secondary-structure element it belongs to. This is the
annotation track that anchors any DMS heatmap or per-residue interpretation
to the protein's actual physical context.

---

## When to use this skill

- Building a per-residue annotation track for a DMS heatmap
- Deciding whether a variant of interest is **in an interface, in a pocket, or
  in the core** β€” context that often distinguishes plausible mechanisms
- Reading SAE feature drops at a position against the protein's known biology
  (a feature drop at a ligand-pocket residue means something different from a
  drop at a surface residue)

**Not for**:
- Multi-conformer ensembles or NMR structure comparisons β†’ use
  `tooluniverse-computational-biophysics`
- Whole-structure pocket detection without a known ligand β†’ use a docking
  tool (e.g. SwissDock) or PDBe's pre-computed pockets

---

## Required inputs

| Input | Format | Example |
|---|---|---|
| PDB ID | 4 characters | `6VJJ` (KRAS-RAF1-GTP analogue) |
| Target chain | single character | `A` |
| Partner chain(s) | list of chain IDs | `["B"]` |
| Ligand resnames | 3-letter PDB names | `["GNP", "MG"]` |

Optional:
- `distance_cutoff` (default 5.0 Γ…)
- `core_rsa_cutoff` (default 0.25)
- `include_secondary_structure` (default false; uses PDBe REST if true)
- `pdb_content` instead of `pdb_id` for local / predicted structures

---

## Workflow

### Step 1 (optional): Find the relevant PDB

If you only have a UniProt accession or gene symbol, pick a structure first:

```python
# PDBe's curated UniProt→PDB mapping (recommended; ranks by coverage + resolution)
PDBeSIFTS_get_best_structures(uniprot_accession="P01116")
# Returns a ranked list of PDB IDs for KRAS with chain mapping

# Or full list (unranked)
PDBeSIFTS_get_all_structures(uniprot_accession="P01116")

# RCSB advanced search (free-text, when you don't have a UniProt yet)
RCSBAdvSearch_search_structures(query="KRAS GTP complex")
```

Pick the structure that contains the **right complex**: include the binding
partner chain you care about, the relevant ligand, and a resolution adequate
for distance-based classification (≀ 3 Γ… is a safe default).

### Step 2: Run the annotation

```python
Structure_annotate_per_residue(
    pdb_id="6VJJ",
    target_chain="A",
    partner_chains=["B"],
    ligand_resnames=["GNP", "MG"],
    distance_cutoff=5.0,
    core_rsa_cutoff=0.25,
    include_secondary_structure=False,
)
```

Returns `annotations: List[{position, aa, dist_partner, dist_ligand, rsa,
region, is_core, ss_element?}]` for every residue of the target chain. For
KRAS in 6VJJ, this yields 168 rows.

### Step 3: Verify the numbering before joining

PDB residue numbers carry **silent offsets** β€” crystal constructs add
N-terminal cloning residues, and published figures sometimes shift the track
relative to the panel sequence. Always verify with a landmark:

```python
# Get the canonical reference sequence
UniProt_get_sequence_by_accession(accession="P01116")
# Then spot-check: KRAS canonical position 12 should be glycine
assert annotations[11]["aa"] == "G"  # 1-indexed position 12, 0-indexed index 11
```

If the landmark mismatches, record the offset explicitly (e.g. `pdb_pos =
uniprot_pos + offset`) before any downstream join. **Do not silently rebase
positions.**

### Step 4: Combine with secondary structure (optional)

If you set `include_secondary_structure=True`, the tool fetches per-residue
helix/strand/coil from PDBe REST. Alternatively, use the dedicated PDBe
secondary-structure tool separately:

```python
pdbe_get_entry_secondary_structure(pdb_id="6VJJ")
# Returns per-chain helix + strand ranges
```

### Step 5: Use the annotation

The returned table is keyed by 1-based canonical residue number. Typical
downstream uses:

| Use case | Field to read |
|---|---|
| Is variant X in a pocket? | `by_pos = {a["position"]: a for a in annotations}; by_pos[X]["region"] in ("ligand", "both")` β€” index by position field, NOT list index (PDB residue numbers may not start at 1 or be contiguous) |
| Build a DMS heatmap annotation track | `[(r["position"], r["region"], r["is_core"], r.get("ss_element"))]` |
| Filter SAE hotspot features to ligand-binding residues | filter clusters by `region == "ligand"` |
| Compare buried vs surface signal | group statistics by `is_core` |

---

## Interpretation table

| Region label | Biological meaning | Common functional role |
|---|---|---|
| `interface` | Within `distance_cutoff` of a partner chain | Protein-protein binding residue; variants often disrupt complex formation |
| `ligand` | Within `distance_cutoff` of a ligand heavy atom | Pocket residue; variants often disrupt substrate / cofactor / drug binding |
| `both` | Both | Allosteric or shared-surface residue |
| `other` | Neither | Surface (if not `is_core`) or core (if `is_core`) β€” variants impact through stability or distal effects |
| `is_core=true` | RSA < `core_rsa_cutoff` (0.25 by default) | Buried residue; variants often destabilize the fold |

---

## Honest limitations

1. **One conformer only**. A static crystal structure does not capture
   alternative conformations or induced-fit binding. A residue may be at the
   pocket in one conformer and away in another. Pick the structure whose
   bound state matches your question.
2. **Numbering is fragile**. Crystal constructs add N-terminal tags or skip
   disordered N/C termini. **Always verify** with a landmark before joining
   to a sequence or to another annotation source. The skill cannot detect
   silent offsets for you.
3. **Distance cutoff is a convention, not a truth**. 5.0 Γ… is the literature
   default. Tighten to 4.0 Γ… for stricter pocket
   calls; loosen to 6.0 Γ… to include 2nd-shell residues.
4. **freesasa RSA can exceed 1.0** for small / unusual structures because the
   max-ASA reference is calibrated for a typical protein context. For real
   well-folded proteins values cluster in [0, 1.2]; reading them as a
   fraction is fine, but treat extreme values as a flag to inspect.
5. **`partner_chains=[]` is permitted** but then all `dist_partner` values
   are `null` β€” interface analysis is skipped entirely.
6. **HETATM ligands only**. Modified residues (e.g. phosphoresidues already
   in the chain) are not detected as ligands β€” they are part of the chain.

---

## Cross-references

| Tool | Role | Use it for |
|---|---|---|
| `Structure_annotate_per_residue` | This skill's atomic tool | The annotation itself |
| `PDBeSIFTS_get_best_structures` | UniProt β†’ ranked PDB list | Step 1 |
| `PDBeSIFTS_get_all_structures` | UniProt β†’ full PDB list | Step 1 |
| `RCSBAdvSearch_search_structures` | Free-text RCSB search | Step 1 |
| `UniProt_get_sequence_by_accession` | Canonical sequence | Step 3 (numbering verification) |
| `pdbe_get_entry_secondary_structure` | SS alone | Step 4 alternative |
| `tooluniverse-residue-functional-mechanism-interpretation` | Downstream consumer | Use this annotation as the structural evidence layer when interpreting DMS hotspots; the skill also plots an annotated DMS heatmap in its Step 7 |

Signals

Avg rating⭐ 0.0
Reviews0
Favorites0

Information

Repository
mims-harvard/ToolUniverse
Author
mims-harvard
Last Sync
9/5/2026
Repo Updated
9/5/2026
Created
5/26/2026

Reviews (0)

No reviews yet. Be the first to review this skill!

Related Skills

cursorrules

CrewAI Development Rules

⭐ 43932Has guide

firecrawl-build-search

Integrate Firecrawl `/search` into product code and agent workflows. Use when an app needs discovery before extraction, when the feature starts with a query instead of a URL, or when the system should search the web and optionally hydrate result content.

⭐ 35092

firecrawl-build-onboarding

Get Firecrawl credentials and SDK setup into a project. Use when an application needs `FIRECRAWL_API_KEY`, when an agent should add Firecrawl to `.env`, when the user wants to authenticate Firecrawl for app code, or when choosing the first SDK and docs for a new Firecrawl integration. This skill includes its own browser auth flow, so it does not depend on the website onboarding skill.

⭐ 35092

firecrawl-build

Integrate Firecrawl into application code whenever a product, agent, or workflow needs web data inside the app β€” web search, live search results, page scraping, structured extraction, or browser interaction. Use when building any feature that needs data from the web in code, even if the user does not mention Firecrawl explicitly and only describes wanting web data, website content, search, scraping, or interaction in an application. Trigger for Firecrawl requests, "fire girl" shorthand, and generic app-level web-data needs that should map to `/scrape`, `/search`, or `/interact`. Do not use this skill for one-off terminal-only web tasks during the current session; use `firecrawl/cli` for those.

⭐ 35092

Related Guides