Peptide Design Lab — Build & Analyze Peptides

🧬 AI Peptide Advisor Powered by Claude

Describe a peptide property goal or paste a sequence for analysis. The advisor will compute biochemical properties, compare against published therapeutic peptide examples, and discuss the published literature on relevant sequence modifications. The output is a literature-search summary, not a design recommendation.

For educational purposes only. AI-generated analysis should not be used for clinical decisions, drug development, or self-medication. Novel peptides require extensive laboratory testing, animal studies, and clinical trials before any therapeutic use. Always consult qualified researchers and healthcare providers.

AI Research Tools — The Real Pipeline

These are the actual tools researchers and biotech companies use to design novel peptides and predict protein structures. Use them alongside our Design Lab to go from idea to experimentally testable sequence.

The Research Workflow

Step 1

Define Goal

ThePeptideBond Design Lab

→

Step 2

Predict Properties

Peptides.ai / Design Lab

→

Step 3

Design 3D Structure

RFdiffusion

→

Step 4

Optimize Sequence

ProteinMPNN

→

Step 5

Predict Folding

AlphaFold 3

→

Step 6

Validate

Lab Synthesis & Testing

Free

🧬

AlphaFold 3

Google DeepMind

Paste an amino acid sequence and get its predicted 3D structure — how it folds, where the helices and sheets form, and how it interacts with other proteins, DNA, RNA, and small molecules. The most accurate structure prediction tool ever built. Won the 2024 Nobel Prize in Chemistry.

Use for: Predicting how your designed peptide will fold. Checking if two proteins will bind to each other. Validating that your sequence produces the intended structure.

AlphaFold Server → GitHub

Advanced

🔬

RFdiffusion

Baker Lab (University of Washington)

The gold standard for de novo protein design. Instead of predicting how existing proteins fold, RFdiffusion designs entirely new protein structures from scratch — structures that have never existed in nature. It works by running the protein folding process in reverse (diffusion → denoising).

Use for: Designing novel protein binders, scaffolds, and functional peptides. Creating proteins that bind to specific targets (like a virus spike protein). This is what David Baker's lab used to win the 2024 Nobel Prize.

Neurosnap (Easy) → GitHub (Pro) Google Colab

Advanced

🧩

ProteinMPNN

Baker Lab (University of Washington)

The companion to RFdiffusion. After RFdiffusion designs a 3D shape, ProteinMPNN solves the inverse folding problem: given a desired 3D structure, what amino acid sequence will fold into that shape? It outputs the exact sequence you'd need to synthesize to create the designed structure.

Use for: Converting a 3D protein design into a synthesizable amino acid sequence. Optimizing existing protein sequences to fold more reliably. Typically used after RFdiffusion in the design pipeline.

HuggingFace Demo → GitHub

Free

⚡

Peptides.ai

Independent / Beginner-Friendly

A simpler, more accessible tool for predicting peptide properties — solubility, charge, hydrophobicity, cell penetration potential, and stability — before you commit to synthesizing anything. Works well alongside our Design Lab for a more comprehensive analysis.

Use for: Quick property predictions for beginners. Checking if a peptide will be soluble. Estimating cell-penetrating potential. Getting a second opinion on properties calculated in our Design Lab.

Peptides.ai →

How These Tools Fit Together

Start in our Design Lab to explore peptide properties and get AI-powered suggestions. Use Peptides.ai to validate solubility and stability predictions. If you're designing something novel, use RFdiffusion to generate a 3D structure, then ProteinMPNN to find the optimal sequence. Finally, paste that sequence into AlphaFold 3 to confirm it folds correctly. This is the same pipeline used by labs at the forefront of peptide drug discovery.