Fold biomolecular complexes from sequences and ligand specifications into CIF structures.
npx skills add https://github.com/LiorZ/protein-design-skills --skill protenix-liorz
Please help me install this Agent Skill. Skill: protenix Source: https://github.com/LiorZ/protein-design-skills/tree/main/skills/protenix Command: npx skills add https://github.com/LiorZ/protein-design-skills --skill protenix-liorz
Protenix provides end-to-end all-atom structure prediction for biomolecular complexes, enabling users to fold proteins, DNA/RNA, ligands, and ions in a single pass from descriptive inputs.
Run a GPU-accelerated prediction from an input JSON using a Protenix model with default parameters.
High-intent search queries and answers about installing and using this skill.
Protein-DNA/RNA-ligand folding is handled by processing an AlphaFold-Server-style input JSON through a Protenix model. This end-to-end all-atom prediction outputs CIF structures and per-sample confidence scores for the entire biomolecular complex.
An AlphaFold3-style structure folding model is a trainable diffusion-model architecture that predicts all-atom structures for biomolecular complexes. It applies MSA templates and soft-pocket constraints to generate realistic chemical interfaces and CIF structures.
Yes, folding protein-DNA-RNA-ligand systems requires a containerized runtime like Apptainer to execute GPU-accelerated predictions. This environment isolates dependencies required by the diffusion model to process input JSON and generate CIF structure outputs.
Yes, you can model post-translational modifications and covalent bonds in protein-ligand complexes. The folding pipeline explicitly supports these realistic chemistry features alongside soft constraints to accurately predict structural interfaces and CIF outputs.
Yes, the structure prediction pipeline supports MSA templates and RNA-MSA inputs. It uses these evolutionary cues with specific presets to rank the generated all-atom structures via AF3-style confidence scores and output CIF files.
001TMFPredict protein, complex, and protein-ligand structures from sequence inputs with confidence metrics.
zongtingweiPredict protein complex structures and generate CIF models with confidence scores.
PKU-YuanGroupPredict protein structures for monomers and multimers with AlphaFold2.
PKU-YuanGroupPredict protein, nucleic-acid, and small-molecule complex structures with Boltz-2.
