Inverse folding

What is inverse folding?

Inverse folding is the task of finding amino-acid sequences that are compatible with a supplied three-dimensional protein backbone. Unlike structure prediction, which maps sequence to structure, inverse folding holds the backbone geometry fixed and predicts residue identities or sequence probabilities. It is widely used for fixed-backbone redesign, sequence recovery, stability-oriented diversification, and sequence assignment after backbone generation.

The backbone defines much of the structural context, but it does not uniquely determine a sequence. ProteinMPNN, ESM-IF1, and related models can return different high-probability solutions because they use different representations, training data, and decoding procedures.

Generate multiple sequences at controlled sampling temperatures, preserve per-residue probabilities, and avoid overinterpreting native-sequence recovery as proof of design quality. Refolding and developability filters can reject obvious failures, while experiments establish whether candidates actually adopt the intended structure.

When to use inverse folding

• Best fit. Fixed-backbone redesign, sequence recovery, and assigning sequences to designed structures
• Required starting evidence. A clean PDB backbone with intended chains, residues, and any fixed positions identified

Benefits of inverse folding

• Focused search. Directly conditions on 3D geometry
• Connected evidence. Samples many sequences per backbone
• Testable candidates. Supports residue-level constraints

Primary limitations

• Model scope. Treats the backbone as largely fixed
• Score uncertainty. Model probabilities are not experimental fitness
• Experimental requirement. Missing context can mislead design

Inverse folding methods

Inverse-folding networks encode backbone geometry and estimate compatible residue identities. Autoregressive and masked approaches differ in how sequence positions condition one another, so model comparison can expose candidates that depend on a single scoring assumption.

Sampling temperature controls the tradeoff between high-probability residues and sequence diversity. Fixed-position masks should preserve residues whose chemistry or interactions are essential rather than asking the model to rediscover every constraint.

How to run inverse folding 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. Prepare backbone. Clean the backbone and confirm chain identities, residue numbering, missing atoms, and intended oligomeric context.
2. Define fixed residues. Specify residues that must remain fixed, including catalytic, binding, disulfide, or interface positions.
3. Sample sequences. Run complementary inverse-folding models and sample multiple sequences at documented temperatures.
4. Refold designs. Refold candidates and compare local and global agreement with the supplied backbone.
5. Rank candidates. Review diversity, stability, solubility, and experimental constraints before selecting sequences.

How to evaluate inverse folding results

Review sequence log-probabilities, diversity, conserved positions, refold agreement, clashes, and local confidence. Compare candidates within the same model and settings because raw scores are not necessarily calibrated across methods.

A fixed-backbone design can fail when the real protein relaxes, changes oligomeric state, binds a partner, or encounters cellular constraints. Test structure, stability, and the intended function with appropriate experiments.

Experimental validation and handoff

Keep the exact backbone, fixed-position masks, sampling temperature, model version, sequence scores, and refolded models. Validate selected designs 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 inverse folding works

Design sequences from the same backbone with ProteinMPNN and ESM-IF1, then refold both branches independently.

1. Prepare backbone. Clean the backbone and confirm chain identities, residue numbering, missing atoms, and intended oligomeric context.
2. Define fixed residues. Specify residues that must remain fixed, including catalytic, binding, disulfide, or interface positions.
3. Sample sequences. Run complementary inverse-folding models and sample multiple sequences at documented temperatures.
4. Refold designs. Refold candidates and compare local and global agreement with the supplied backbone.
5. Rank candidates. Review diversity, stability, solubility, and experimental constraints before selecting sequences.

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 protein backbone in PDB format plus optional fixed-position and chain-design constraints.

Outputs

• Design and review outputs. PDB FASTA CSV JSON Designed FASTA sequences, model scores, sequence alignments, refolded PDB files, and comparison results.