Predict biomolecular complex structures and binding affinities for proteins, ligands, DNA, and RNA. Learn more
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Boltz-2 webserver overview
Boltz-2 predicts all-atom 3D structures for biomolecular complexes and estimates binding affinity for protein-small-molecule interactions. The ProteinIQ webserver accepts sequences, ligands, templates, and optional constraints, then returns predicted structures, confidence metrics, and downloadable result files. It supports protein-ligand cofolding, protein complex prediction, protein-DNA and protein-RNA modeling, affinity prioritization, and template- or constraint-guided prediction.
Pricing
Boltz-2 jobs start at 50 credits. The calculator scales mainly with the number and length of input molecules, inference settings, and requested samples, while additional samples have a smaller incremental effect. The exact credit price is calculated before submission.
Inputs
ProteinIQ displays chain IDs for constraint definitions and template mappings. A job must include at least one protein, DNA, or RNA chain; ligands, templates, and precomputed MSAs cannot be submitted by themselves. The total number of residues across all inputs is limited to 5,000, and each small-molecule ligand is limited to 300 heavy atoms.
| Input | Accepted formats | Limits and behavior |
|---|---|---|
| Protein | FASTA or FA text, .fasta or .fa file, .pdb file, or RCSB chain | Up to 10 protein inputs; files up to 50 MB. RCSB fetches require a chain specifier. Protein sequences use single-letter residues, and recognized modified residues include MSE, SEC, and PYL. |
| Precomputed protein MSA | A3M text or .a3m file | Up to 10 MSAs; files up to 100 MB. Each alignment is assigned to protein chains in submission order. |
| Ligand | SMILES text, .smi or .smiles file, .sdf, .mol, or .mol2 file, or a PubChem record | Up to 10 ligands; files up to 10 MB. Each ligand can contain at most 300 heavy atoms. Use a protein chain instead of a ligand input for peptide ligands. |
| Ligand (CCD) | One CCD code such as ATP, NAD, HEM, or SAH | Up to 10 CCD ligands. Codes are 1-4 alphanumeric characters. This input accepts CCD codes, not custom SMILES or structure files. |
| DNA | FASTA or FA text, .fasta or .fa file | Up to 10 DNA molecules; files up to 10 MB. |
| RNA | FASTA or FA text, .fasta or .fa file | Up to 10 RNA molecules; files up to 10 MB. |
| Template | .pdb or .cif file, or an RCSB structure | Up to 5 templates; files up to 50 MB. CIF and mmCIF templates must include full metadata. Templates act as structural guides and can be assigned to query chains with the template settings below. |
| Job name | Text | Optional label used to identify the run. |
Settings
The settings below cover prediction, MSA generation, affinity estimation, diagnostics, constraints, templates, and per-molecule controls. Defaults and ranges follow the current Boltz-2 webserver definition. Some advanced fields appear only when their parent option is enabled; unset optional fields use the native model defaults.
Prediction and output
| Setting | Default or range | Description |
|---|---|---|
| Number of samples | 1; 1-20 | Number of independent structure samples to generate. |
| Set confidence threshold | Off | Applies a post-inference confidence filter to generated samples. |
| Confidence threshold | 0.5; 0-1 in 0.05 steps | Threshold used when Set confidence threshold is enabled. |
| Recycling steps | 3; 1-10 | Number of recycling iterations during structure inference. |
| Sampling steps | 200; 50-500 in 10-step increments | Number of diffusion sampling steps for structure inference. |
| Step scale (temperature) | Native default; 1.0-2.0 in 0.05 steps | Sampling temperature or step scale. When not set, the native default is 1.5 for Boltz-2 and 1.638 for Boltz-1. |
| Output format | CIF | Predicted structures can be returned as CIF or PDB files. CIF is the recommended format. |
MSA generation
| Setting | Default or range | Description |
|---|---|---|
| Generate MSA | Off | Generates an MSA with ColabFold for supported protein inputs. It can improve accuracy but increases runtime. |
| MSA depth | Normal; Shallow 2048, Normal 8192, Deep 16384 sequences | Sets the MSA sequence count when automatic MSA generation is enabled. Normal is recommended. |
| MSA pairing strategy | Greedy | Selects Greedy or Complete pairing for paired MSAs. |
| Max MSA sequences | 8192; 512-16384 in 512-sequence steps | Sets the maximum MSA size. MSA depth overrides this value when a depth preset is selected. |
| Subsample MSA | Off | Subsamples a large MSA to reduce runtime. |
| Subsampled sequences | 1024; 256-4096 | Number of sequences retained when MSA subsampling is enabled. |
| MSA server URL | Not set | Optional custom endpoint for MSA generation. When not set, the default ColabFold endpoint is used. |
| MSA server username | Not set | Optional basic-auth username for a custom MSA server. Use it together with an MSA server password. |
| MSA server password | Not set | Optional basic-auth password for a custom MSA server. Use it together with an MSA server username. |
| MSA API key header | Not set | Optional header name for token authentication with a custom MSA server. Use it with an API key value. |
| MSA API key value | Not set | Optional API token for a custom MSA server. Do not combine token authentication with username and password authentication. |
Affinity, diagnostics, and model
| Setting | Default or range | Description |
|---|---|---|
| MW-corrected affinity | Off | Applies a molecular-weight correction when comparing affinity predictions for ligands of different sizes. |
| Affinity sampling steps | 200; 50-500 in 10-step increments | Number of diffusion sampling steps used for affinity prediction. |
| Affinity diffusion samples | 5; 1-20 | Number of diffusion samples used for affinity prediction. |
| Save PAE matrix | Off | Writes the full predicted aligned error matrix to the result files. |
| Save PDE matrix | Off | Writes the full predicted distance error matrix to the result files. |
| Random seed | Not set | Uses a random seed when not set. Set a fixed seed for reproducible sampling. |
| Model version | Boltz-2 | Selects Boltz-2 or the legacy Boltz-1 structure-only model. |
| Method conditioning | None | Optional experimental-method conditioning for Boltz-2: X-ray diffraction, electron microscopy (cryo-EM), solution NMR, or molecular dynamics (MD). None disables method conditioning. |
Constraints and templates
| Setting | Default or range | Description |
|---|---|---|
| Use constraint potentials | Off | Constraints are passed to inference either way. When enabled, force=true in pocket or contact constraints adds an inference-time potential; covalent bonds do not use this force flag. |
| Pocket constraints | Not set | Format: binder|contacts|max_distance|force. Example: C|A:45,A:46|6.0|true. The force field is strict only when constraint potentials are enabled. |
| Covalent bonds | Not set | Format: chain:residue:atom,chain:residue:atom. Example: A:12:SG,B:1:C22. Supports canonical protein, DNA, and RNA residues plus Ligand (CCD); custom SMILES, MOL, SDF, and MOL2 ligands are not supported here. |
| Contact constraints | Not set | Format: chain:residue,chain:residue|max_distance|force. The force field is strict only when constraint potentials are enabled. |
| Enforce template backbone | Off | Applies a template backbone constraint during inference. |
| Template deviation threshold (Å) | 2.0 Å; 0.5-5.0 Å | Maximum allowed template deviation when Enforce template backbone is enabled. |
| Template chain mapping | Not set | Maps template chains to query chains, one mapping per line. Example: 0:A,B. |
Per-molecule controls
| Setting | Default or range | Description |
|---|---|---|
| Copies | 1; 1-10 | Available for proteins, ligands, Ligand (CCD), DNA, and RNA. Copies duplicate a molecule and assign separate chain IDs. |
| Cyclic | Off | Available for protein, DNA, and RNA inputs. Marks the molecule as head-to-tail cyclic. |
| Residue modification | None; up to 10 per molecule | Available for protein, DNA, and RNA inputs. Each modification uses a residue position and a CCD code, with no more than one modification at the same position. |
A default run generates one sample with automatic MSA generation off, 3 recycling steps, 200 structure and affinity sampling steps, CIF output, and no confidence filter, diagnostic matrices, or constraints. Adjust the number of samples before increasing low-level sampling settings when the goal is to compare alternative predictions.
Outputs
Each run opens in the structure viewer and includes data and file views for inspecting metrics and downloading results. The available outputs depend on the selected model, inputs, samples, and diagnostic settings.
| Output | Description |
|---|---|
| Predicted structures | One CIF or PDB structure file for each requested sample. |
| 3D structure viewer | Interactive inspection of the predicted complex and its chains. |
| Confidence metrics | Overall confidence, local confidence, pTM, and ipTM metrics where applicable. |
| Binding probability | affinity_probability_binary for protein-small-molecule complexes; this is a model prediction, not an experimental measurement. |
| Affinity estimate | affinity_pred_value for protein-small-molecule complexes, reported as a predicted log10(IC50) in micromolar units. More negative values indicate stronger predicted binding within a comparison set. |
| Error matrices | PAE and PDE matrices when the corresponding full-matrix settings are enabled. |
| Downloadable files | Predicted structures, metric data, diagnostic files, and the run log. |
Important limitations
Affinity outputs are intended for prioritization among related protein-small-molecule predictions and are not experimental binding measurements. Other complex types still receive structure and confidence outputs, but they do not receive the same affinity estimate. Memory use increases with the size and number of input chains, so long sequences and many chains can require substantially more resources. Covalent-bond constraints use standardized CCD atom names and are supported for canonical polymer residues and Ligand (CCD) inputs; use the regular ligand input for custom small-molecule structures. For a longer walkthrough with examples, see How to use Boltz-2 online.
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