alterlab-ligandmpnn

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Design protein sequences around bound ligands, metals, and nucleic acids with LigandMPNN (Dauparas 2023) — inverse folding that conditions on non-protein context, so binding-pocket and metal-site residues are chosen to fit the actual ligand. Use when designing a small-molecule or metal binding pocket, redesigning residues that contact a ligand/ion/nucleic acid, or doing enzyme active-site design where the substrate matters. For backbone sequence design with NO ligand/metal context prefer alterlab-proteinmpnn; to GENERATE a backbone or scaffold a functional site prefer alterlab-rfdiffusion; to validate a design by refolding prefer alterlab-alphafold; to co-fold or dock the ligand prefer alterlab-boltz or alterlab-diffdock. Part of the AlterLab Academic Skills suite.

AI & Automation 66 stars 13 forks Updated 1 weeks ago MIT

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Skill Content

# LigandMPNN (ligand-aware sequence design) ## Overview **LigandMPNN** (Dauparas et al. 2023; `dauparas/LigandMPNN`) extends ProteinMPNN's inverse folding to **condition on non-protein context** — small-molecule ligands, metal ions, and nucleic acids. Because the model *sees* the ligand/metal atoms, the residues it designs for a **binding pocket** or **metal site** are chosen to complement what is actually bound, which plain ProteinMPNN (protein-atoms-only) cannot do. Use it whenever the design target is a **site that contacts a ligand or ion**. For sequence design of a backbone with no bound context, use `alterlab-proteinmpnn`. ## When to Use This Skill Use this skill when the user wants to: - Design a **small-molecule binding pocket** so the residues fit the ligand. - Design a **metal-coordinating site** (e.g. Zn/Fe) with the ion in context. - Redesign residues that **contact a ligand, ion, or nucleic acid**. - Do **enzyme active-site** design where the substrate/cofactor should guide the choice. ### Does NOT Trigger | Scenario | Use instead | |----------|-------------| | Sequence design for a backbone with **no** ligand/metal context | `alterlab-proteinmpnn` | | **Generate** a backbone or scaffold a functional motif | `alterlab-rfdiffusion` | | Validate a design by refolding | `alterlab-alphafold` | | Co-fold the protein WITH the ligand from scratch | `alterlab-boltz` | | Dock a ligand into a fixed pocket (pose, not sequence) | `alterlab-diffdock` | ## Core Capabil...

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Author
AlterLab-IEU
Repository
AlterLab-IEU/AlterLab-Academic-Skills
Created
5 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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AI & Automation Featured

alterlab-proteinmpnn

Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inverse-folding a backbone PDB into sequences, redesigning selected positions, imposing symmetry across chains, or generating the sequence step of a design→fold→score loop. For pocket/interface design WITH a bound ligand, metal, or nucleic acid prefer alterlab-ligandmpnn; to GENERATE a new backbone prefer alterlab-rfdiffusion; to refold and validate a design prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU
AI & Automation Featured

alterlab-rfdiffusion

Generate de-novo protein backbones with RFdiffusion (Watson 2023) — a diffusion model for unconditional monomer generation, motif scaffolding, binder design against a target, and symmetric oligomers. Use when generating a new protein backbone from scratch, scaffolding a functional motif into a fold, designing a binder backbone to a target surface, or building symmetric assemblies; RFdiffusion produces the STRUCTURE, then alterlab-proteinmpnn designs its sequence and alterlab-alphafold validates it. For sequence design of an existing backbone prefer alterlab-proteinmpnn (or alterlab-ligandmpnn with a ligand); to fold a known sequence prefer alterlab-alphafold; for generative multimodal design prefer alterlab-esm. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU
AI & Automation Featured

alterlab-boltz

Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a small-molecule ligand, predicting a holo (ligand-bound) complex or its binding affinity, or co-folding protein–DNA/RNA assemblies. For protein-only or protein–protein folding without ligands prefer alterlab-alphafold; for antibody–antigen complexes prefer alterlab-chai; to dock a ligand into a FIXED receptor structure prefer alterlab-diffdock; to look up an existing structure prefer alterlab-pdb. Part of the AlterLab Academic Skills suite.

66 Updated 1 weeks ago
AlterLab-IEU