Does this skill support small molecule or ligand-aware design?

While the core ProteinMPNN focuses on protein-only structures, this skill includes guidance and wrappers for LigandMPNN, which handles small molecules and cofactors.

Can I keep certain residues fixed during the design process?

Yes, you can use the --fixed_positions_jsonl parameter to specify exact residue positions that should remain unchanged while the rest of the sequence is redesigned.

How do I control the diversity of the generated sequences?

Sequence diversity is controlled by the sampling temperature. A low temperature like '0.1' produces sequences with high structural recovery, while '0.3' or higher increases diversity.

What is the primary use of the ProteinMPNN skill?

ProteinMPNN is used for inverse folding, which is the process of designing an amino acid sequence that will fold into a specific, pre-defined 3D protein structure.

What are the hardware requirements for running ProteinMPNN?

It is recommended to use a GPU with at least 8GB of VRAM (such as an NVIDIA T4) for efficient sequence generation, typically processing 50-100 sequences per minute.

ProteinMPNN Sequence Design

Name: ProteinMPNN Sequence Design
Author: adaptyvbio

byadaptyvbio

•

Ciencia de Datos y ML

Designs optimized protein sequences from structural backbones using deep learning-based inverse folding.

The ProteinMPNN skill empowers researchers and bioengineers to solve the inverse folding problem by generating high-quality protein sequences for specific 3D backbones. It is highly versatile, supporting sequences for RFdiffusion backbones, the redesign of existing proteins, and the optimization of sequences for expression or stability. With features like fixed-residue design, multi-chain tying, and adjustable sampling temperatures, it provides a robust framework for exploring protein sequence space while maintaining structural integrity. Whether performing de novo design or interface redesign, this skill streamlines the transition from structural coordinates to viable biological sequences.

Características Principales

01High-accuracy inverse folding for structural backbones

02Integration with LigandMPNN and SolubleMPNN variants

03Support for fixed-residue and chain-specific design

04Multi-state and negative design capabilities

05Adjustable sampling temperature for sequence diversity control

0675 GitHub stars

Casos de Uso

01Redesigning protein-protein interfaces to improve binding affinity

02Generating sequences for de novo protein backbones created via RFdiffusion

03Optimizing protein sequences for better solubility and expression in the lab

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add adaptyvbio/protein-design-skills proteinmpnn

For use in Claude.ai and ChatGPT

Download Skill