Alphafold Pocket Evaluator

Agent Skill: AlphaFold2 3D Binding Pocket & SASA Evaluator Skill

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📌 Description

Parses AlphaFold2 PDB files, computes per-residue pLDDT confidence scores, and evaluates Solvent Accessible Surface Area (SASA) of active site pockets.

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🤖 Agent Execution Protocol

When an AI Agent is tasked with alphafold-pocket-evaluator:

1. Input Validation: Verify that the required input files or coordinates are supplied.
2. Environment Check: Ensure dependencies (Biopython, Py3Dmol, FreeSASA, SciPy) are installed.
3. Execution: Run the protocol pipeline snippet below.
4. Output Generation: Produce actionable Markdown/JSON summaries with publication figures.

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💻 Protocol Code Snippet

def evaluate_pocket(pdb_file, pocket_residues):
    plddt_list = []
    with open(pdb_file, 'r') as f:
        for line in f:
            if line.startswith("ATOM") and line[12:16].strip() == "CA":
                res_id = int(line[22:26].strip())
                if res_id in pocket_residues:
                    plddt_list.append(float(line[60:66].strip()))
    return sum(plddt_list) / len(plddt_list) if plddt_list else 0.0

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📥 Input & Output Specifications

Input Contract

• Target Files: Valid input data matching domain formats.
• Parameters: Quality thresholds and cutoffs.

Output Contract

• Results Table: Structured summary dataframe or matrix.
• Visualization: Rendered SVG/PNG figures.

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📄 License

Distributed under the MIT License. See LICENSE for details.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: YuliaNuzhnenko
• Source: YuliaNuzhnenko/bioinformatics-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.