Protein sequence design

What is protein sequence design?

Protein sequence design is the computational process of finding amino-acid sequences expected to satisfy one or more structural or functional objectives. Some methods condition on a fixed backbone, while protein language models can generate sequences with weaker or no explicit structural input. Projects may optimize folding, stability, solubility, expression, binding, catalysis, or combinations of these properties, but each objective needs its own evidence and validation.

Sequence design is broader than inverse folding. Inverse folding specifically asks which sequences fit a supplied backbone; sequence-design projects may instead generate from a language model, edit an existing protein, or combine structural and property objectives in an iterative search.

Define immutable residues, allowed mutations, diversity targets, and acceptance gates before sampling. Multi-objective design should retain the separate component scores because a single aggregate rank can hide a severe failure in stability, function, or developability.

When to use protein sequence design

Start from a defined sequence objective and a way to measure it:

• A backbone is available for fixed-structure redesign.
• Specific residues must remain unchanged while surrounding positions vary.
• You want to compare folding and property signals before expression tests.

Strengths and limitations

Sequence design can sample many solutions around one structure while holding catalytic or interface residues fixed. Refolding and separate stability, solubility and aggregation checks show whether an attractive sequence score survives other constraints.

A sequence predictor cannot guarantee expression, correct oligomerization or biological activity. Property tools may disagree and often describe different experimental contexts. The starting backbone, fixed-position mask and training domain can bias the entire candidate set.

Protein sequence design methods

Backbone-conditioned models use local and global geometry to propose residues, while protein language models learn statistical regularities from natural sequence data. These approaches can be complementary but their scores do not share a universal scale.

Optimization algorithms can search several objectives, yet the weighting scheme encodes project priorities. Hard requirements should be treated as gates where possible rather than diluted inside one weighted average.

Applications of protein sequence design

Backbone-conditioned design can improve a scaffold while preserving residues required for catalysis or binding. Sampling several sequence families tests whether the structural objective tolerates chemical diversity.

A sequence panel can also help compare predicted stability, solubility and aggregation liabilities before expression. These predictors use different inputs and endpoints, so disagreements are informative rather than something to hide in a combined score.

How to run protein sequence design online

The featured template starts from a protein backbone, redesigns it with ProteinMPNN and follows selected sequences through refolding and property checks.

1. Prepare the backbone. Supply the reviewed PDB and identify positions that must remain fixed.
2. Generate variants. Use ProteinMPNN to sample compatible sequences and retain the fixed-position mask and sampling settings.
3. Refold selected sequences. Use ESMFold and compare candidate structures with the design backbone.
4. Review separate property signals. Inspect Protein Stability outputs in their own context. Check NetSolP-1.0 for E. coli expression-related predictions.
5. Inspect structure liabilities. Review Aggrescan3D aggregation context and SASA Calculator surface exposure on the refolded models.
6. Select a test set. Keep every component score and choose diverse variants for expression, structural and functional assays.

How to interpret protein sequence design results

Review diversity, similarity to training or parent sequences, conserved functional residues, refold agreement, stability, solubility, aggregation, and any task-specific predictive endpoint.

Experimental selection should cover more than the numerical top rank. A diverse panel is more informative about which design assumptions generalize and can reveal score failures.

Experimental validation and handoff

Keep all objective definitions, fixed and mutable positions, sampling settings, component scores, and rejected sequences. Test structure and intended function experimentally.

Send the exact backbone, fixed-position mask and sequence identifiers with candidate constructs. Expression, folding and functional assays reveal which computational signals were useful in the actual system.

How protein sequence design works

Redesign a backbone with ProteinMPNN, refold sequences, and screen stability, solubility, and aggregation.

1. Define objectives. Define the structural, functional, and developability objectives and how each will be evaluated.
2. Set constraints. Specify fixed residues, mutable regions, sequence identity limits, and forbidden motifs.
3. Generate sequences. Generate a diverse sequence set with a backbone-conditioned or sequence-generative method.
4. Screen properties. Evaluate refolding, stability, solubility, aggregation, and task-specific function without hiding component scores.
5. Test candidates. Select a diverse experimental panel and measure expression, structure, and the intended function.

Inputs and outputs

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

Inputs

Design input

PDBFASTAJSONTXT

A protein backbone or starting sequence plus fixed positions, mutable regions, and design objectives.

Outputs

Design and review outputs

PDBFASTACSVJSON

Designed FASTA sequences, per-objective scores, refolded models, property tables, and candidate rankings.