Peptide design

What is peptide design?

Peptide design is the process of creating short amino-acid sequences intended to bind a target or satisfy another functional and physicochemical objective. Compared with larger proteins, peptides have different conformational flexibility, proteolytic stability, cyclization, permeability, charge, and synthesis constraints. Sequence generation is therefore only one stage in a workflow that must also address structure, activity, solubility, aggregation, and experimental tractability.

Target-conditioned language models can generate linear peptide binders from a protein sequence, while structure-aware methods and optimization workflows can incorporate an interface or conformation. Length, termini, modifications, cyclization, and noncanonical residues must match the method’s documented scope.

Fast sequence filters help remove obvious liabilities, but flexible peptides can bind in conformations that are difficult to predict. Synthesis and assays should test binding, specificity, stability, and function under conditions relevant to the intended use.

When to use peptide design

• Best fit. Target-binding peptides and sequence-first peptide lead generation
• Required starting evidence. A target sequence or property objective, length range, chemistry constraints, and an assay

Benefits of peptide design

• Focused search. Searches compact sequence space quickly
• Connected evidence. Supports target-conditioned generation
• Testable candidates. Pairs naturally with synthesis panels

Primary limitations

• Model scope. Peptides can be highly flexible
• Score uncertainty. Proteolysis and clearance may dominate
• Experimental requirement. Many chemistries fall outside model scope

Peptide design methods

PepMLM conditions masked peptide generation on a target protein sequence. Its candidates are hypotheses for linear binders; the model does not by itself specify a unique bound conformation or experimental affinity.

Property-oriented generation and post-generation filters can shape charge, hydrophobicity, length, or predicted activity. Hard chemistry constraints should be applied before ranking so infeasible candidates do not consume the selection budget.

How to run peptide design online

Use the workflow as an inspectable computational funnel. Preserve the native output of each method, apply explicit acceptance gates, and keep the evidence behind every selected and rejected candidate.

1. Define target. Confirm the target sequence or structure and define the intended mechanism, site, and assay.
2. Set peptide constraints. Set peptide length, termini, allowed residues, charge, modifications, and synthesis constraints.
3. Generate sequences. Generate diverse candidates with a method that supports the selected peptide class.
4. Screen liabilities. Screen activity signals, solubility, aggregation, proteolysis, and physicochemical properties.
5. Synthesize and test. Synthesize a diverse panel and measure binding, specificity, stability, and functional response.

How to evaluate peptide design results

Compare sequence diversity, predicted activity, solubility, aggregation, charge, mass, cleavage susceptibility, and any available structural evidence. Avoid selecting only near-duplicate top-scoring sequences.

Experimental panels should include sequence-diverse candidates, negative controls, target-binding assays, specificity controls, serum or protease stability, and a function-relevant readout.

Experimental validation and handoff

Keep target sequence, peptide constraints, model settings, every generated candidate, and selection rules. Validate binding, specificity, stability, and function experimentally.

Export structures, sequences, settings, scores, logs, and selection criteria together. A reproducible handoff makes computational assumptions visible to the team planning synthesis, expression, biophysical characterization, and functional assays.

How peptide design works

Generate linear peptide binders with PepMLM, then review predicted activity, solubility, and physicochemical properties.

1. Define target. Confirm the target sequence or structure and define the intended mechanism, site, and assay.
2. Set peptide constraints. Set peptide length, termini, allowed residues, charge, modifications, and synthesis constraints.
3. Generate sequences. Generate diverse candidates with a method that supports the selected peptide class.
4. Screen liabilities. Screen activity signals, solubility, aggregation, proteolysis, and physicochemical properties.
5. Synthesize and test. Synthesize a diverse panel and measure binding, specificity, stability, and functional response.

Inputs and outputs

Check formats before running, then inspect and download the result from every workflow step.

Inputs

• Design input. PDB FASTA JSON TXT A target protein FASTA sequence plus peptide length and chemistry constraints.

Outputs

• Design and review outputs. PDB FASTA CSV JSON Candidate peptide FASTA sequences, activity estimates, solubility and property tables, and downloadable rankings.