What input does protein fold recognition require?

Protein sequence, external fold-recognition report, candidate model, and selected template structure.

What does protein fold recognition return?

External threading provenance plus FoldSeek matches, USAlign scores, residue correspondence, and superposed coordinates.

How should protein structures be prepared?

Confirm accession, model, chain, biological assembly, domain boundaries, residue numbering, missing regions, alternate conformations, and prediction confidence. Repair coordinates only when necessary and retain both the original file and every preparation decision.

Which structure-alignment score should I use?

Use method-native scores together rather than selecting one universal number. TM-score emphasizes length-normalized global fold similarity, RMSD reports geometric deviation over the aligned atoms, and coverage shows how much of each structure actually corresponds.

Does structural similarity prove shared function?

No. Similar folds can support different functions, and local similarity can occur without shared global architecture. Review residue-level correspondence, domains, ligands, oligomeric state, taxonomy, sequence evidence, curated annotations, and experiments.

How much does protein fold recognition cost?

A complete protein fold recognition project is generally quote-based. Current providers describe fold recognition and protein-structure analysis as customized services covering data review, method selection, modeling or comparison, validation, and interpretation rather than publishing one universal project price. The cost depends on structure or sequence count, database scope, model preparation, method comparison, manual inspection, figures, annotation, and whether experimental follow-up is included. Open-source FoldSeek, US-align, and FoldMason can remove a software-license fee, but they do not remove expert analysis or compute requirements. ProteinIQ self-service starts at $29 per month for academic Plus and $99 per month for commercial Pro, with the configured protein fold recognition workflow estimated in credits before submission. Done-for-you analysis is scoped separately.

Protein fold recognition online

What is protein fold recognition?

Protein fold recognition is a computational method for matching a protein sequence to a known three-dimensional fold even when direct sequence similarity is weak. Threading methods score how well the sequence fits candidate structural templates and return fold or template hypotheses for further modeling and validation.

Use fold recognition when profile searches suggest remote homology but do not provide a confident template. Sequence-to-structure compatibility, predicted secondary structure, residue environment, evolutionary profiles, and model-specific energy terms can contribute to the ranking.

ProteinIQ does not currently run a dedicated threading engine. Its review workflow begins with an externally generated candidate model and selected template, then uses FoldSeek and USAlign to inspect structural neighborhood, coverage, TM-score, RMSD, and superposition.

When to use protein fold recognition

Best fit. Remote homologs and difficult template selection when sequence identity is low
Required evidence. A protein sequence, external threading results, a candidate model, and selected template coordinates
Execution boundary. The threading calculation remains external; ProteinIQ reviews its structural candidates rather than recreating the fold-recognition score.

Benefits of protein fold recognition

Structural sensitivity. Detects remote structural relationships
Connected evidence. Provides templates for difficult modeling targets
Reusable output. Combines sequence and structural evidence

Primary limitations

Coverage limit. Cannot recover a fold absent from the template universe
Method dependence. Domain boundaries and alignments can dominate model quality
Interpretive limit. A recognized fold does not establish protein function

Protein fold recognition methods

Profile–profile methods compare evolutionary profiles, while threading evaluates sequence compatibility with structural environments or template-derived potentials. Modern systems may combine both evidence types.

A high fold-recognition rank is method-specific. Preserve the candidate template, alignment, coverage, score definition, database version, and any generated model so another reviewer can reconstruct the decision.

Protein fold recognition applications

Fold recognition supports remote-homology detection, template selection for comparative modeling, domain annotation, and hypothesis generation for proteins whose closest sequence matches are uninformative.

It is least reliable for novel folds, disordered proteins, uncertain multidomain boundaries, and sequences whose relevant state depends on partners or membranes absent from the template.

How to run protein fold recognition online

The threading calculation remains external; ProteinIQ reviews its structural candidates rather than recreating the fold-recognition score.

Curate the sequence. Check sequence boundaries, signal peptides, transmembrane regions, low complexity, and likely domain architecture.
Run external threading. Run a dedicated fold-recognition or threading method and preserve database version, templates, scores, and alignments.
Select candidates. Retain several plausible templates rather than selecting only the top score when methods disagree.
Review structures. Load the candidate model and selected template into the review workflow for FoldSeek and USAlign comparison.
Validate the hypothesis. Inspect coverage, superposition, geometry, conserved residues, sequence evidence, and alternative folds before downstream use.

How to interpret protein fold recognition results

Compare several candidates across alignment coverage, structural plausibility, conserved functional positions, geometry, and agreement with independent sequence evidence. Do not equate a threading score with experimental structure accuracy.

Fold-level similarity can persist after sequence and function diverge. Transfer active-site, ligand, oligomeric, or functional annotation only when local correspondence and biological context support it.

How protein fold recognition works

The threading calculation remains external; ProteinIQ reviews its structural candidates rather than recreating the fold-recognition score.

Curate the sequence. Check sequence boundaries, signal peptides, transmembrane regions, low complexity, and likely domain architecture.
Run external threading. Run a dedicated fold-recognition or threading method and preserve database version, templates, scores, and alignments.
Select candidates. Retain several plausible templates rather than selecting only the top score when methods disagree.
Review structures. Load the candidate model and selected template into the review workflow for FoldSeek and USAlign comparison.
Validate the hypothesis. Inspect coverage, superposition, geometry, conserved residues, sequence evidence, and alternative folds before downstream use.

Inputs and outputs

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

Inputs

Structure-analysis inputs. PDB mmCIF FASTA TSV Protein sequence, external fold-recognition report, candidate model, and selected template structure.

Outputs

Reviewable results. PDB CSV TSV JSON FILES External threading provenance plus FoldSeek matches, USAlign scores, residue correspondence, and superposed coordinates.