EV+OneHot MCP Server

Fitness prediction combining evolutionary coupling and one-hot encoding via Docker

An MCP (Model Context Protocol) server for protein fitness prediction with 2 core tools:

• Train a Ridge regression fitness model combining evolutionary (EV) features and one-hot encoding
• Predict fitness for new protein sequences using a trained model

Quick Start with Docker

Approach 1: Pull Pre-built Image from GitHub

The fastest way to get started. A pre-built Docker image is automatically published to GitHub Container Registry on every release.

# Pull the latest image
docker pull ghcr.io/macromnex/ev_onehot_mcp:latest

# Register with Claude Code (runs as current user to avoid permission issues)
claude mcp add ev_onehot -- docker run -i --rm --user `id -u`:`id -g` -v `pwd`:`pwd` ghcr.io/macromnex/ev_onehot_mcp:latest

Note: Run from your project directory. `pwd` expands to the current working directory.

Requirements:

• Docker
• Claude Code installed

That's it! The EV+OneHot MCP server is now available in Claude Code.

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Approach 2: Build Docker Image Locally

Build the image yourself and install it into Claude Code. Useful for customization or offline environments.

# Clone the repository
git clone https://github.com/MacromNex/ev_onehot_mcp.git
cd ev_onehot_mcp

# Build the Docker image
docker build -t ev_onehot_mcp:latest .

# Register with Claude Code (runs as current user to avoid permission issues)
claude mcp add ev_onehot -- docker run -i --rm --user `id -u`:`id -g` -v `pwd`:`pwd` ev_onehot_mcp:latest

Note: Run from your project directory. `pwd` expands to the current working directory.

Requirements:

• Docker
• Claude Code installed
• Git (to clone the repository)

About the Docker Flags:

• -i — Interactive mode for Claude Code
• --rm — Automatically remove container after exit
• --user `id -u`:`id -g` — Runs the container as your current user, so output files are owned by you (not root)
• -v — Mounts your project directory so the container can access your data

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Verify Installation

After adding the MCP server, you can verify it's working:

# List registered MCP servers
claude mcp list

# You should see 'ev_onehot' in the output

In Claude Code, you can now use all 2 EV+OneHot tools:

• ev_onehot_train_fitness_predictor
• ev_onehot_predict_fitness

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Next Steps

• Detailed documentation: See detail.md for comprehensive guides on:
  • Available MCP tools and parameters
  • Local Python environment setup (alternative to Docker)
  • Data format requirements
  • Example workflows and use cases
  • Model architecture details

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Usage Examples

Once registered, you can use the EV+OneHot tools directly in Claude Code. Here are some common workflows:

Example 1: Train a Fitness Model

I have a PLMC model in /path/to/plmc/ directory and training data at /path/to/data.csv with wild-type sequence /path/to/wt.fasta. Can you train an ev+onehot fitness model using ev_onehot_train_fitness_predictor with 5-fold cross-validation and save to /path/to/model/?

Example 2: Predict Fitness for New Sequences

I have a trained ev+onehot model in /path/to/model/ and new sequences in /path/to/new_sequences.csv. Can you predict fitness for all sequences using ev_onehot_predict_fitness and save the predictions to /path/to/predictions/?

Example 3: Full Fitness Modeling Workflow

I have experimental fitness data for subtilisin variants at /path/to/subtilisin/data.csv, wild-type at /path/to/subtilisin/wt.fasta, and a PLMC model at /path/to/subtilisin/plmc/. Please train an ev+onehot model with cross-validation and report the Spearman correlation performance.

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Troubleshooting

Docker not found?

docker --version  # Install Docker if missing

Claude Code not found?

# Install Claude Code
npm install -g @anthropic-ai/claude-code

PLMC model required?

• This tool requires a pre-trained PLMC model in the model directory
• Use the plmc_mcp to generate PLMC models from MSA files

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License

Research use — see original repository for details