De novo protein design

What is de novo protein design?

De novo protein design is the process of creating new protein structures and sequences from scratch rather than modifying a natural protein that already performs the intended role. Generative models can propose backbones under geometric or functional constraints, after which sequence-design methods assign residues expected to stabilize those structures. The result is a set of computational hypotheses that still requires structural and experimental validation.

A complete campaign separates backbone generation from sequence assignment and validation. This matters because a visually plausible backbone does not guarantee that any amino-acid sequence will fold into it, and a high model score does not establish soluble expression, monodispersity, stability, or function.

Use unconditional generation for new structural space, or add symmetry, motif, target, and functional constraints when the design must satisfy a specific geometry. Preserve rejected candidates and score distributions so selection is based on stated gates rather than one attractive model.

When to use de novo protein design

• Best fit. New folds, assemblies, functional scaffolds, and constrained structural concepts
• Required starting evidence. A design objective, structural constraints, candidate count, and experimental acceptance criteria

De novo protein design methods

Diffusion and other generative models sample structures from learned protein geometry. Conditioning can steer that sampling toward a motif, target, symmetry, or shape, but stronger constraints can reduce diversity or create incompatible requirements.

Sequence assignment is a separate inverse problem. Multiple sequences per backbone help reveal whether a structural proposal has a broad compatible sequence space or depends on a narrow, fragile solution.

How to run de novo protein 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. Specify intent. Define the desired size, topology, symmetry, motif, or functional constraints before generation.
2. Generate backbones. Generate a sufficiently diverse backbone set and retain model settings and random seeds.
3. Assign sequences. Assign several sequences to each accepted backbone instead of treating one sequence as definitive.
4. Refold candidates. Refold sequences independently and compare predicted structures with their design backbones.
5. Select for testing. Apply geometry, stability, solubility, and experiment-specific gates before synthesis.

How to evaluate de novo protein design results

Compare designed and independently predicted structures using global and local agreement, then inspect clashes, secondary structure, buried polar atoms, exposed hydrophobics, aggregation risk, and sequence diversity.

Prospective validation should measure the property the design was intended to create. Expression and folding checks are necessary, but they do not replace binding, catalytic, assembly, or other functional assays.

Experimental validation and handoff

Retain generation settings, seeds, every sequence–backbone pairing, refolded structures, and rejection criteria. Confirm folding and intended 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.

Benefits of de novo protein design

• Focused search. Explores structure beyond natural templates
• Connected evidence. Supports explicit geometric constraints
• Testable candidates. Produces diverse testable hypotheses

Limitations of de novo protein design

• Model scope. Computational success does not imply expression
• Score uncertainty. Scores are model-dependent
• Experimental requirement. Experimental hit rates may be low

How de novo protein design works

Generate unconditional backbones with RFdiffusion3, assign sequences with ProteinMPNN, refold with ESMfold, and inspect geometry with MolProbity.

1. Specify intent. Define the desired size, topology, symmetry, motif, or functional constraints before generation.
2. Generate backbones. Generate a sufficiently diverse backbone set and retain model settings and random seeds.
3. Assign sequences. Assign several sequences to each accepted backbone instead of treating one sequence as definitive.
4. Refold candidates. Refold sequences independently and compare predicted structures with their design backbones.
5. Select for testing. Apply geometry, stability, solubility, and experiment-specific gates before synthesis.

Inputs and outputs

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

Inputs

Design input

PDBFASTAJSONTXT

Design constraints and, when applicable, a target, motif, or symmetry definition.

Outputs

Design and review outputs

PDBFASTACSVJSON

Designed backbone PDB files, candidate FASTA sequences, refolded models, scores, and review files.