Predict protein, nucleic-acid, and small-molecule complex structures with Boltz-2.
npx skills add https://github.com/PKU-YuanGroup/OpenAI4S --skill boltz-pku-yuangroup
Please help me install this Agent Skill. Skill: boltz Source: https://github.com/PKU-YuanGroup/OpenAI4S/tree/main/skills/boltz Command: npx skills add https://github.com/PKU-YuanGroup/OpenAI4S --skill boltz-pku-yuangroup
This Skill automates structure prediction for protein, nucleic-acid, and small-molecule complexes, providing validation tools and alternative models for research.
Use the boltz skill to predict the structure of a protein complex from the provided sequences.
High-intent search queries and answers about installing and using this skill.
You can predict protein complex structures and binding affinity using Boltz-2, an open-source alternative to AlphaFold3 that utilizes diffusion and MSA models to generate accurate complex structures.
Yes, you can co-fold proteins with ligands and validate designed binders against a target protein by predicting both the complex structure and optional binding affinity using the Boltz-2 model.
Running Boltz-2 for small-molecule and nucleic-acid complex prediction requires a GPU for performance, along with the torch and boltz dependencies installed in your environment.
Yes, Boltz-2 supports structure prediction for nucleic acids and small molecules, allowing researchers to model diverse complexes and validate interactions without relying solely on AlphaFold3.
Using diffusion and MSA models for complex structure prediction provides an open-source alternative to AlphaFold3, offering optional binding-affinity prediction to validate designed binders and co-fold proteins with ligands.
Predicting complex structures without a GPU is a limitation because Boltz-2 requires GPU hardware for performance when utilizing diffusion and MSA models to generate protein, nucleic-acid, and small-molecule complexes.
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