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yjcyxky
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abodybuilder3

Predict antibody structures from FASTA heavy and light chains using protein language model embeddings.

Updated Aug 23, 2026
Search Tags:#Docker#GPU#batch-processing#ESM#structure-prediction#antibody#protein-language-model
One-click install
npx skills add https://github.com/yjcyxky/biominer-skills --skill abodybuilder3
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: abodybuilder3
Source: https://github.com/yjcyxky/biominer-skills/tree/main/abodybuilder3
Command: npx skills add https://github.com/yjcyxky/biominer-skills --skill abodybuilder3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

The skill streamlines scalable antibody structure prediction by leveraging protein language model embeddings to infer inter-residue geometries and 3D coordinates, enabling rapid analysis for research and therapeutic development.

Core Features & Use Cases

  • GPU-accelerated prediction via Docker image ghcr.io/open-prophetdb/abodybuilder3:arm64-blackwell
  • Batch processing for high-throughput antibody panels
  • Supports paired heavy and light chain input in FASTA format for end-to-end predictions
  • Use Case: Therapeutic antibody design, structural analysis, and computational immunology workflows

Quick Start

To run a single prediction, mount your input FASTA and run the Docker image with the inference script as shown. docker run --rm --gpus all -v $(pwd):/data ghcr.io/open-prophetdb/abodybuilder3:arm64-blackwell python /app/src/abodybuilder3/stages/inference.py --model language --output-dir /data/output For batch predictions, mount data directories and run: docker run --rm --gpus all -v /data:/data ghcr.io/open-prophetdb/abodybuilder3:arm64-blackwell python /app/src/abodybuilder3/stages/inference.py --model language --output-dir /data/outputs

Frequently Asked Questions about abodybuilder3

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I batch predict antibody structures using a GPU?▼

Batch antibody structure prediction runs on GPUs by mounting data directories into a Docker container and executing the inference script to output 3D coordinates for high-throughput panels.

Do I need Docker and GPU access for antibody structure prediction?▼

Yes, antibody structure prediction requires a Docker-enabled environment with GPU access to run the containerized image and execute protein language model inference.

What FASTA input format is required for paired antibody chains?▼

Paired antibody structure prediction requires FASTA input containing both heavy and light chains to run end-to-end structural inference using protein language model embeddings.

How do protein language models predict antibody structures?▼

Protein language models predict antibody structures by embedding sequence data to infer inter-residue geometries and generate 3D coordinates for computational immunology workflows.

Can I use this antibody structure prediction tool for therapeutic development?▼

Yes, antibody structure prediction supports therapeutic development workflows by providing rapid structural analysis of antibody panels at scale.

What is the best way to process high-throughput antibody panels for structural analysis?▼

High-throughput antibody panels are processed by mounting data directories into the GPU-accelerated Docker container and running batch inference to output predicted 3D structures.

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