Protein structure prediction

What is protein structure prediction?

Protein structure prediction is the computational process of estimating a protein’s local secondary structure or three-dimensional shape from its amino-acid sequence. Methods can incorporate evolutionary relationships, structural templates, or learned molecular representations; the appropriate model depends on whether the question concerns local states, a monomer, an assembly, or a specialized molecular class.

Protein structure prediction is not one interchangeable task. Homology modeling transfers information from related experimental structures, single-sequence models infer a fold without a user-supplied alignment, and complex predictors jointly model multiple chains. Antibodies, peptides, and membrane proteins require more specific input and interpretation.

A coordinate file is a hypothesis, not a biological conclusion. Confidence can vary by residue, domain, interface, conformational state, and similarity to the evidence a predictor learned from; retain native confidence and compare predictions where the decision warrants it.

When to use protein structure prediction

• No experimental structure is available. Generate a structural hypothesis for construct design, interpretation, docking, engineering, or experiment planning.
• The molecular system needs a specialist method. Use complex-, antibody-, peptide-, or membrane-aware interpretation instead of treating every sequence as a soluble monomer.
• Comparable model evidence is needed. Run complementary predictors from consistent inputs and retain structures, confidence, settings, and files.

Benefits of protein structure prediction

• Structure before experiment. Develop an inspectable model for hypothesis generation and experimental planning.
• Method comparison. Preserve complementary predictions and their native confidence rather than collapsing them into one score.
• Connected downstream work. Carry reviewed models into analysis, docking, design, or simulation workflows.

Primary limitations

• Local reliability is uneven. Confidence varies across domains, loops, termini, and interfaces.
• Biological context can be missing. Ligands, membranes, partners, modifications, and conformational states may be absent.
• Experimental validation remains necessary. A convincing model cannot by itself establish mechanism or binding.

Types of protein structure prediction

The maintained prediction types reflect distinct researcher workflows. Homology modeling requires templates and an alignment; secondary-structure prediction returns local states; single-sequence prediction starts from one sequence; and complex prediction adds chain composition and interfaces. Antibodies, peptides, and transmembrane proteins introduce constraints that justify specialist interpretation.

Threading and fold recognition remain important terms, but they are not presented as prediction spokes because ProteinIQ does not provide dedicated engines for them. A general predictor would not faithfully substitute for those methods.

How to predict protein structure online

A defensible run preserves the exact sequence or assembly, selected model and settings, every returned confidence output, and the evidence used to accept or reject regions.

1. Define the structural question. Decide whether you need local states, a monomer, a complex, or a specialist molecular-class prediction.
2. Prepare the biological input. Confirm sequence boundaries, chain identity, stoichiometry, templates, or constraints required by the method.
3. Run matched predictors. Choose tools whose documented inputs and methodology match the question.
4. Inspect confidence and agreement. Review local confidence, PAE, interfaces, geometry, and model-to-model differences rather than one headline score.
5. Validate and export. Keep structures, settings, provenance, and caveats together, then test important claims with orthogonal evidence.

How the featured workflow works

The hub workflow compares general sequence-to-structure models; each specialist page provides a workflow matched to that prediction type.

1. Define the question. Confirm that a general monomer comparison matches the biological system.
2. Prepare the sequence. Use one clean, ungapped protein sequence with the intended construct boundaries.
3. Run four predictors. Submit the same sequence to AlphaFold2, ESMFold, OpenFold-3, and Boltz-2.
4. Compare predictions. Review local confidence, domain arrangement, geometry, and structural agreement.
5. Validate and export. Select models in relation to the downstream question and export provenance.

Inputs and outputs for the featured workflow

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

Inputs

Protein sequence

FASTATXT

One protein sequence for the general comparison, or the specialist inputs described on each spoke.

Outputs

Structures and confidence

PDBmmCIFJSONCSV

Model-native structures, rankings, confidence scores, PAE where supported, logs, and downloads.