Boltz-2 AI Skill: Protein Structure Prediction & Binding

Boltz-2 AI Skill: Protein Structure Prediction & Binding

The Boltz-2 AI Skill facilitates the prediction of protein structures, nucleic-acid interactions, and small-molecule complexes. It serves as an alternative to AlphaFold3, offering options for binding-affinity prediction in validating designed binders against a target. This skill is aligned with the open-source principles, with tools and weights freely available via Python's package index PyPI (`boltz`) and GitHub.

What this skill does

• Supports structure prediction for complex biological entities, including proteins, DNA, RNA, and ligands.
• Uses diffusion co-folding, closest in functionality to AlphaFold3, to analyze inputs and produce structural confidence metrics such as pTM/ipTM/pLDDT.
• Provides capabilities to co-fold a protein with a SMILES or CCD-described ligand for detailed molecular interaction insights.
• Facilitates binding-affinity prediction, aiding in binder-validation campaigns.

Who it is for

• Developers and scientific research teams utilizing AI coding agents like Claude Code, Cursor, and Codex.
• Biotechnology and pharmaceutical researchers working on protein engineering, drug discovery, and molecular interaction studies.
• Academics and scientists focusing on structural biology and computational chemistry.

Use cases

• Validating the design of protein binders against specific biological targets.
• Running simulations to co-fold proteins with ligands for drug design research.
• Substituting closed-source solutions with an open-weight, accessible approach for academic studies and enterprise use.

Technical details

• Integrated with GitHub for code and weights distribution (github.com/jwohlwend/boltz).
• Leverages a YAML-based configuration for defining the molecular complex and specifying protein, nucleic-acid, and ligand sequences for prediction tasks.
• Requires MSAs for each protein chain, available through external services queried via `--use_msa_server` with `api.colabfold.com`.
• Offers configurable parameters like `--recycling_steps` and `--diffusion_samples` for optimizing structure prediction processes.

Source & Licence

This package is built on open-source work published by aipoch (aipoch/open-science) and distributed under Apache-2.0. The original licence text and copyright notice are included in your download.

Personal and commercial use, modification and redistribution are permitted under the Apache License 2.0, which also includes an express patent grant. Attribution and any NOTICE file must be retained.

Your purchase covers curation, licence verification, packaging, documentation and instant delivery. It does not grant exclusive rights to the underlying open-source code, which remains available under its original licence.

Delivery & Support

• Delivery: instant — a secure download link is emailed to you as soon as payment is confirmed.
• Format: ZIP archive containing the skill files, documentation and the original licence.
• Support: support@mcpcart.com — we aim to reply within 2 business days.
• Updates: updates are included only where stated on this page.

Refunds

This is a digital product delivered immediately after purchase. By completing your order you request immediate delivery and acknowledge that, once the download has been accessed, the statutory right to cancel no longer applies to the extent permitted by law. Refund requests are handled in accordance with our published Refund Policy.

Claude, Codex, Gemini and Cursor are trademarks of their respective owners. MCP Cart is an independent marketplace and is not affiliated with, endorsed by, or sponsored by any of them. Compatibility references describe interoperability only.