MCP server for PDB structure queries and protein resolution using AnAge database
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# atomica-mcp [](https://www.python.org/downloads/) [](https://opensource.org/licenses/MIT) MCP (Model Context Protocol) server for ATOMICA longevity proteins dataset and PDB structure analysis. ## What is ATOMICA? **ATOMICA** is a geometric deep learning model that learns atomic-scale representations of intermolecular interactions across proteins, small molecules, ions, lipids, and nucleic acids. Trained on 2M+ interaction complexes, it generates embeddings that capture physicochemical features shared across molecular classes. ### Why ATOMICA Embeddings Are Useful - **Interaction Scoring**: ATOMICA embeddings quantify interface similarity and predict binding partners based on learned physicochemical patterns - **Critical Residues**: Identifies residues that most influence interactions (low ATOMICA scores = high impact on binding) - **Cross-Modal Transfer**: Knowledge learned from protein-protein interactions helps predict protein-ligand binding - **Visualization**: PyMOL commands highlight interaction-critical regions for structural analysis ### This MCP Server Provides This server provides access to the ATOMICA longevity proteins dataset from Hugging Face, which contains comprehensive structural analysis of key aging-related proteins using the ATOMICA deep learning model. The server also provides auxiliary functions for resolving arbitrary PDB structures and UniProt IDs. ## Features - **ATOMICA Dataset Access**: Query the curated dataset of 94 longevity-related protein structures - **Automatic Dataset Management**: Downloads dataset from Hugging Face on first use - **Comprehensive Metadata**: Access PDB metadata, ATOMICA interaction scores, critical residues, and PyMOL scripts - **Gene-Based Queries**: Search structures by gene symbols (NFE2L2, KEAP1, SOX2, APOE, OCT4) - **Organism Queries**: Filter structures by organis…
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