Protein engineering
Protein complex structure prediction
Predict multi-chain protein assemblies and compare interface geometry, stoichiometry assumptions, confidence, and model agreement.
What is protein complex structure prediction?
Protein complex structure prediction is the computational process of building the three-dimensional arrangement and interfaces of two or more protein chains from their sequences and specified composition. It differs from docking because a complex predictor may predict partner folds and assembly jointly rather than begin with fixed monomers.
The calculation depends on more than individual fold quality. Define biologically relevant chains and stoichiometry, then ask whether interface confidence, model recurrence, known residues, and cellular context support the predicted interface.
A plausible high-confidence interface is not proof of an in-vivo interaction. Incorrect paralogs, missing cofactors, alternative oligomeric states, and familiarity with training evidence can yield neat but misleading assemblies.
When to use protein complex structure prediction
- Partner sequences are known. Use it for oligomeric assemblies and interaction hypotheses.
- Stoichiometry can be stated. Supply the intended chain multiplicity explicitly.
- Interface evidence can be tested. Compare models with biochemical and cellular evidence.
Benefits of protein complex structure prediction
- Folds and interfaces are predicted jointly. The assembly can be evaluated as a whole.
- Alternative models can be compared. Shared inputs support meaningful comparison across predictors.
- Interaction hypotheses are explicit. Returned interfaces identify contacts for follow-up.
Primary limitations
- Stoichiometry must be correct. Wrong chain counts can distort the assembly.
- Confident interfaces can be irrelevant. Model confidence does not establish biology.
- Large assemblies can exceed limits. Model and compute constraints grow with system size.
Complex-model interpretation
Multimer predictors place chains using learned geometric and evolutionary information. Chain-pair PAE, ipTM-like values, and interface-residue confidence are generally more relevant than monomer-wide confidence for an assembly.
Compare alternate stoichiometries only when biologically plausible. Larger assemblies gain contacts simply because more chains were supplied, so use co-purification, crosslinking, mutagenesis, or cryo-EM to assess a decisive claim.
How to run protein complex structure prediction online
- Define partners and stoichiometry. Create one FASTA record per chain and duplicate only when required.
- Prepare chain sequences. Confirm boundaries and intended biological composition.
- Run multimer models. Submit the same input to AlphaFold2 Multimer, ESMFold2, Boltz-2, and Protenix.
- Inspect interface evidence. Review clashes, buried surface, PAE blocks, interface confidence, and agreement.
- Validate the assembly. Prioritize models supported by biochemical or cellular evidence and retain provenance.
How protein complex structure prediction works
Run four multimer-capable predictors from the same multi-record FASTA and compare their interface confidence and assembly geometry.
- Define partners and stoichiometry. Create one FASTA record per chain and duplicate records only when the proposed stoichiometry requires it.
- Prepare chain sequences. Run AlphaFold2 Multimer, ESMFold2, Boltz-2, and Protenix from the same input.
- Run multimer models. Retain ranked models and every chain-aware confidence output.
- Inspect interface confidence. Inspect interfaces, clashes, buried surface, PAE blocks, and agreement across model families.
- Compare biological support. Prioritize assemblies that agree with biochemical and cellular evidence, then export models and provenance.
Inputs and outputs
Check formats before running, then inspect and download the result from every workflow step.
Inputs
Research input
FASTAPDBmmCIFA multi-record protein FASTA containing each intended chain with the correct multiplicity.
Outputs
Prediction and review outputs
PDBmmCIFCSVJSONRanked complex models, chain-aware confidence metrics, PAE or interface scores, and downloadable structures.
Tools for protein complex structure prediction
Use these methods to prepare inputs, run the core analysis, inspect outputs, and validate the evidence described in this workflow.
AlphaFold2
AlphaFold2 Multimer complex prediction
ESMFold2
Sequence-based multi-chain prediction
Boltz-2
All-atom biomolecular complex prediction
Chai-1
Multi-component structure prediction
OpenFold-3
Open all-atom complex prediction
Protenix v2
Multi-chain biomolecular prediction
RosettaFold3
Protein and biomolecular complex prediction
IPSAE
Analyze interface confidence from predicted alignments
DockQ
Benchmark complexes against a native reference
PDBsum
Summarize interfaces and contacts
MolProbity
Check stereochemical plausibility
USAlign
Compare alternative complex structures
Other protein engineering workflows
Compare related approaches based on the molecular system, available evidence, required inputs, and decision you need to support.
Frequently asked questions
Protein complex structure prediction is the computational process of building the three-dimensional arrangement and interfaces of two or more protein chains from their sequences and specified composition. It differs from docking because the model may predict partner folds and assembly jointly rather than starting from fixed monomer structures.
Oligomeric assemblies and protein interaction hypotheses when partner sequences are known. The required starting evidence is correct partner sequences, chain multiplicity, and intended stoichiometry.
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.
Use interface-specific confidence and orthogonal interaction evidence. Validate decisive contacts with mutagenesis, crosslinking, binding measurements, or experimental structure determination.
Complex predictions are usually more compute-intensive than monomer runs; public full-service structure analysis can be priced by expert day rather than by model. 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.