Protein structure prediction

What is protein structure prediction?

Protein structure prediction is the computer-based process of estimating a protein’s secondary or three-dimensional shape from its amino-acid sequence. Methods may also use evolutionary relationships, structural templates, or learned molecular representations. The appropriate method depends on whether the question concerns local secondary structure, a monomer fold, a multi-chain 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 predict without a user-supplied alignment; complex predictors jointly model multiple chains; and specialized methods account for antibodies, peptides, or membrane-protein context.

Choose the page that matches the biological object and the evidence you actually have. A predicted coordinate file is a hypothesis whose confidence varies by residue, domain, interface, conformational state, and similarity to training data.

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.
• You need comparable model evidence. Run complementary predictors from consistent inputs and retain model-native structures, confidence, settings, and files.

Benefits of protein structure prediction

• Structure before experiment. Develop an inspectable model for hypothesis generation and experiment 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

• Uneven local reliability. Confidence varies across structured domains, loops, termini, and interfaces.
• Missing biological context. Ligands, membranes, partners, modifications, and conformational states may be absent.
• Experimental validation remains necessary. A convincing model cannot by itself establish biological mechanism or binding.

Types of protein structure prediction

These prediction types have distinct search intent, inputs, outputs, methodologies, or researcher decisions; choose the narrowest page that fits the actual task.

Homology modeling

Builds a target model from one or more experimentally determined structures of related proteins.

Best for: Targets with defensible structural templates
Requires: A target sequence, selected templates, and a reviewed alignment

Protein secondary structure prediction

Predicts residue-level helix, strand, and coil states rather than a complete atomic structure.

Best for: Fast sequence annotation and fold-context checks
Requires: One protein sequence

Single-sequence protein structure prediction

Infers a three-dimensional protein model directly from one sequence without a user-supplied MSA.

Best for: Fast folding when homologous sequence evidence is sparse or unavailable
Requires: One protein sequence

Protein complex structure prediction

Predicts the structures and interfaces of assemblies containing two or more protein chains.

Best for: Interaction and oligomerization hypotheses
Requires: Sequences and stoichiometry for the intended protein partners

Antibody structure prediction

Uses antibody-specialized models to predict variable-domain frameworks and complementarity-determining regions.

Best for: Antibody engineering and paratope-focused structural analysis
Requires: Paired VH and VL sequences, or a supported single-domain sequence

Peptide structure prediction

Predicts conformations for short, often flexible linear or cyclic amino-acid chains.

Best for: Peptide therapeutics, binders, and conformational hypotheses
Requires: A peptide sequence and correct cyclization or constraint context

Transmembrane protein structure prediction

Predicts membrane-protein folds while interpreting hydrophobic segments and membrane-topology context.

Best for: Receptors, channels, transporters, and other integral membrane proteins
Requires: A protein sequence and independent membrane-orientation context

Types of protein structure prediction

The most useful taxonomy combines methodology with distinct researcher workflows. Homology modeling requires templates and an alignment; secondary-structure prediction returns local states; single-sequence prediction starts from one sequence; complex prediction adds chain composition and interfaces. Antibodies, peptides, and transmembrane proteins introduce inputs and interpretation that justify specialist pages.

Threading and fold recognition remain important scientific terms, and ab initio prediction has historical search demand. They are not included as spokes here because ProteinIQ does not currently provide faithful core engines for those methods, and substituting a general predictor would misrepresent the task.

How to predict protein structure online

A defensible workflow preserves the exact sequence or assembly, the 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 as required by the method.
3. Run appropriate predictors. Use one or more 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 protein structure prediction 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

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

Inputs

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

Outputs

• Structures and confidence. PDB mmCIF JSON CSV Model-native structures, rankings, confidence scores, PAE where supported, logs, and downloads.