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scientific-protein-design

Automate computational protein design with ESM scoring, backbone generation, and in silico validation.

3|1|Updated Feb 11, 2026
Search Tags:#esm#backbone-generation#sequence-design#protein-design#de-novo#in-silico-validation#stability-prediction
One-click install
npx skills add https://github.com/nahisaho/satori --skill scientific-protein-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-protein-design
Source: https://github.com/nahisaho/satori/tree/main/src/.github/skills/scientific-protein-design
Command: npx skills add https://github.com/nahisaho/satori --skill scientific-protein-design

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Computational protein design often requires integrating modern protein language models, backbone design, and in silico validation to rapidly explore design spaces and predict stability.

Core Features & Use Cases

  • ESM-based sequence analysis and scoring to guide design decisions.
  • De novo backbone generation and sequence design using RFdiffusion and ProteinMPNN, followed by in silico validation.
  • End-to-end design workflows for binders, scaffolds, and enzymes, with developability assessment and expression considerations.

Quick Start

Design a target protein and run the end-to-end de novo design workflow to generate and evaluate candidate sequences.

Frequently Asked Questions about scientific-protein-design

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I run an end-to-end computational protein design workflow using ESM and RFdiffusion?▼

The workflow applies ESM-based sequence analysis for scoring, RFdiffusion for de novo backbone generation, and ProteinMPNN for sequence design, concluding with in silico validation to evaluate candidate stability.

What is de novo protein backbone generation and how does it integrate with sequence design?▼

De novo backbone generation creates novel protein frameworks using RFdiffusion, integrated with ProteinMPNN for sequence design and ESM scoring to evaluate and optimize the resulting structural candidates.

Can I use this workflow for enzyme redesign and stability prediction?▼

Yes, the computational protein design workflow supports enzyme redesign, binder, and scaffold tasks, applying ESM scoring and in silico validation to deliver stability predictions and developability assessments.

Do I need access to ESM models to run in silico validation for protein design?▼

Yes, running end-to-end protein design and in silico validation requires access to ESM models for language-model scoring, alongside backbone design tools and scoring metrics to evaluate candidate sequences.

What are the limitations of using language-model scoring for computational protein design?▼

While ESM language-model scoring guides design decisions, complete computational protein design requires integrating RFdiffusion backbone generation and in silico validation to fully assess stability, developability, and expression considerations.

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