Protein engineering
Peptide structure prediction
Predict plausible peptide conformations while preserving cyclization, sequence length, flexibility, and model-specific structural assumptions.
Inputs
1 required
Methods
3 connected
- 01HighFold
- 02ESMFold
- 03Boltz-2
Run HighFold, ESMFold, and Boltz-2 from one peptide sequence and compare model-specific conformations.
Use this templateWhat is peptide structure prediction?
Peptide structure prediction is the computational process of estimating one or more three-dimensional conformations for a short amino-acid chain. Peptides often have shallow energy landscapes and may remain disordered or adopt different conformations in solution, membranes, and bound complexes.
Peptides are not simply small globular proteins. Short chains can populate ensembles, and a single predicted structure may represent only one plausible state. Cyclization and disulfides materially change the conformational problem.
The workflow compares a cyclic-peptide-aware model with general structure predictors. Researchers should match the tool to the chemistry and preserve whether a returned conformation is unbound, constrained, or predicted in a complex.
When to use peptide structure prediction
- Suitable peptide structure prediction question. Linear and cyclic peptide conformational hypotheses before docking, design, or experimental characterization
- Inputs and evidence are available. A peptide sequence plus explicit cyclization, disulfide, or environmental constraints
- A validation plan is in place. A single low-energy-looking conformation is not an ensemble. Use NMR, circular dichroism, crystallography, or binding-state evidence where the conformational claim matters.
Benefits of peptide structure prediction
- Supports linear and cyclic contexts.
- Makes model disagreement visible.
- Produces structures for downstream analysis.
Primary limitations
- One model rarely captures the full ensemble.
- General protein models may mishandle short peptides.
- Environment and binding partners can reshape the peptide.
Peptide structure prediction methods and interpretation
Cyclic peptide prediction requires the model to represent non-linear residue connectivity or positional offsets. HighFold is designed for this setting, while general predictors provide useful comparisons but may not enforce the same chemistry.
For flexible linear peptides, an ensemble or bound-state prediction can be more informative than one unbound model. Compare compactness, secondary structure, intramolecular contacts, and compatibility with experimental NMR or circular-dichroism evidence.
How to run peptide structure prediction online
The ProteinIQ workflow keeps the inputs, actual tool runs, method-native files, and comparison outputs together. Follow these steps while preserving the scientific boundary described above.
- Define peptide chemistry. Define the peptide sequence and record cyclization, disulfides, and noncanonical chemistry.
- Encode sequence and constraints. Run HighFold when the supported cyclic constraints match the molecule.
- Run complementary models. Run ESMFold and Boltz-2 as complementary general-model branches.
- Compare conformations. Compare backbone conformations, compactness, confidence, and constraint satisfaction.
- Validate the relevant state. Export plausible alternatives and validate the biologically relevant state with experiment or complex context.
How peptide structure prediction works
Run HighFold, ESMFold, and Boltz-2 from one peptide sequence and compare model-specific conformations.
- Define peptide chemistry. Define the peptide sequence and record cyclization, disulfides, and noncanonical chemistry.
- Encode sequence and constraints. Run HighFold when the supported cyclic constraints match the molecule.
- Run complementary models. Run ESMFold and Boltz-2 as complementary general-model branches.
- Compare conformations. Compare backbone conformations, compactness, confidence, and constraint satisfaction.
- Validate the relevant state. Export plausible alternatives and validate the biologically relevant state with experiment or complex context.
Inputs and outputs
Check formats before running, then inspect and download the result from every workflow step.
Inputs
- Research input.
FASTAPDBmmCIFAn amino-acid sequence with documented head-to-tail cyclization, disulfides, or other supported constraints.
Outputs
- Prediction and review outputs.
PDBmmCIFCSVJSONPredicted peptide structures, model-native confidence, and downloadable PDB or mmCIF files.
Tools for peptide structure prediction
Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.
HighFold
Predict cyclic peptide structures with cyclic constraints
ESMfold
Predict a sequence-derived peptide conformation
Boltz-2
Predict peptide or peptide-complex structures
Chai-1
Model multi-component peptide systems
OpenFold-3
Generate an alternative all-atom prediction
PeptideBuilder
Build a geometry-controlled coordinate baseline
PDBFixer
Repair compatible peptide PDB files
MolProbity
Review all-atom geometry
Ramachandran plot
Inspect peptide backbone torsions
SASA calculator
Calculate solvent exposure
Radius of gyration
Compare conformational compactness
USAlign
Compare predicted conformations
Other protein engineering workflows
Compare related approaches based on the molecular system, available evidence, required inputs, and decision you need to support.
Homology modeling
Builds a target model from one or more experimentally determined structures of related proteins.
Protein secondary structure prediction
Predicts residue-level helix, strand, and coil states rather than a complete atomic structure.
Single-sequence protein structure prediction
Infers a three-dimensional protein model directly from one sequence without a user-supplied MSA.
Protein complex structure prediction
Predicts the structures and interfaces of assemblies containing two or more protein chains.
Antibody structure prediction
Uses antibody-specialized models to predict variable-domain frameworks and complementarity-determining regions.
Transmembrane protein structure prediction
Predicts membrane-protein folds while interpreting hydrophobic segments and membrane-topology context.
Frequently asked questions
Peptide structure prediction is the computational process of estimating one or more three-dimensional conformations for a short amino-acid chain. Peptides often have shallow energy landscapes and may remain disordered or adopt different conformations in solution, membranes, and bound complexes.
Linear and cyclic peptide conformational hypotheses before docking, design, or experimental characterization. The required starting evidence is a peptide sequence plus explicit cyclization, disulfide, or environmental constraints.
Keep each tool’s native confidence definition. Confidence estimates expected model error or consistency; it is not a probability that a biological hypothesis is true.
A single low-energy-looking conformation is not an ensemble. Use NMR, circular dichroism, crystallography, or binding-state evidence where the conformational claim matters.
Short peptide runs may be smaller than full-protein jobs, but cyclic constraints, ensembles, and complex prediction can increase compute use. ProteinIQ Plus is $29 per month and Pro is $99 per month. Compute-heavy predictions consume credits according to the selected model and sequence length; expert project support is scoped separately.
Start with a workflow you can inspect and edit
Add your inputs, review the settings, and keep every structure, score, table, and file connected to the step that produced it.