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### Protenix Structure Prediction (`protenix-prediction`) Predicts the 3D structure of a biomolecular complex. Each input complex can combine protein, DNA, RNA, and ligand chains, with optional post-translational and nucleotide modifications; the assembly is folded by Protenix and returned as a predicted `Structure` per complex with confidence metrics: average pLDDT, pTM, interface pTM, per-chain and pairwise-chain scores, a global predicted distance error, and predicted aligned error. #### API Reference

Source List of complexes to predict structures for. Inherited from `StructurePredictionInput`. Each complex can contain multiple chains of proteins, DNA, RNA, and/or ligands. Chains in the complex, in input order. Pre-computed MSAs, one entry per complex. Each entry is a `ComplexMSAs` (per-chain MSAs keyed by chain index); `paired=True` marks rows taxonomy-aligned across chains. Populated by preprocess() or supplied directly.

Source Protenix model variant to use. Available models: Available options: `protenix_base_default_v1.0.0`, `protenix_base_20250630_v1.0.0`, `protenix_base_default_v0.5.0`, `protenix_base_constraint_v0.5.0`, `protenix_mini_esm_v0.5.0`, `protenix_mini_ism_v0.5.0`, `protenix_mini_default_v0.5.0`, `protenix_tiny_default_v0.5.0`, `protenix-v2` Random seeds for structure sampling. Each seed produces `num_diffusion_samples` independent structure samples. Multiple seeds increase diversity of the sampled conformations. A single seed is sufficient for most use cases; more seeds may help for challenging docking tasks such as antibody-antigen complexes. Independent structure samples per seed; only the best by ranking score is returned. Higher = more thorough but slower. Default 5 (matches upstream). Denoising steps in the diffusion process. `None` uses the upstream schedule: 200 for base/constraint, 5 for mini/tiny. Default `None`. Pairformer refinement passes through the model. `None` uses the upstream schedule: 10 for base/constraint, 4 for mini/tiny. Default `None`. Whether to print status messages during execution including MSA generation, model loading, and prediction progress. Inherited from `StructurePredictionConfig`. Default: `False`. Device to run the model on (e.g., `"cuda"`, `"cpu"`). Inherited from `StructurePredictionConfig`. Default: `"cuda"`. Maximum execution time in seconds. Base models need \~10-15 minutes on slower GPUs. `None` waits indefinitely. Default: 1200. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. Attach `pae` (`avg_pae` always emitted). Default: `False`. Whether to generate and use Multiple Sequence Alignments (MSAs) for protein chains using MMseqs2 homology search. Supplied MSAs are always used and override `use_msa=False`. Inherited from `MSAStructurePredictionConfig`. Default: `True`. Configuration for MMseqs2 homology search (MSA generation). Only used when `use_msa=True`. Inherited from `MSAStructurePredictionConfig`. Default: `None`. Whether heterocomplex protein chains should use taxonomy-paired MSA generation. Inherited from `MSAStructurePredictionConfig`. Default: `True`.

Source Predicted structures, each carrying a :class:`ProtenixMetrics` instance on `.metrics`. Raw structure content in PDB or CIF format. Format of the content string (auto-detected if omitted). What the B-factor column represents. Optional source identifier (filepath or tool name). Associated metrics (e.g., pLDDT, pTM scores, per-chain lists, pairwise matrices). None values are stripped at construction. **Metrics** (one set per `structures` item) | Metric | Type | Range | Availability | | ------------------ | ------------------- | ---------- | ------------------------------ | | `confidence_score` | float | unbounded | always | | `ptm` | float | 0.0 to 1.0 | always | | `iptm` | float | 0.0 to 1.0 | always | | `avg_plddt` | float | 0.0 to 1.0 | always | | `gpde` | float | ≥ 0.0 | always | | `avg_pae` | float | ≥ 0.0 | always | | `pae` | list\[list\[float]] | ≥ 0.0 | when include\_pae\_matrix=True | | `chain_ptm` | list\[float] | 0.0 to 1.0 | depends on model output | | `chain_plddt` | list\[float] | 0.0 to 1.0 | depends on model output | | `chain_pair_iptm` | list\[list\[float]] | 0.0 to 1.0 | depends on model output | | `has_clash` | bool | unbounded | depends on model output |

#### Applications This tool predicts the structure of multi-component assemblies such as protein-DNA and protein-RNA complexes, protein-ligand binding poses, and chains carrying modified residues. For a multi-chain complex it also reports how confidently the chains are placed relative to one another: interface pTM (ipTM) gives a single 0-to-1 score for the overall inter-chain arrangement, per-chain-pair ipTM scores each individual interface, and the cross-chain blocks of the PAE matrix show which specific inter-chain regions are positioned confidently versus uncertainly. These let you rank or filter predicted complexes and judge whether a docking pose or binding interface is reliable before trusting it downstream. #### Usage Tips * **`model_name` selects the accuracy/speed trade-off.** The default `protenix_base_default_v1.0.0` is the most accurate (10 Pairformer cycles, 200 diffusion steps); the `mini` and `tiny` variants are far faster with fewer cycles and steps, and `protenix_mini_esm_v0.5.0` / `protenix_mini_ism_v0.5.0` use protein language-model embeddings for MSA-free prediction. * **`protenix-v2` weights are gated by ByteDance.** They are not currently distributed publicly; if you have a copy, drop it into the resolved weights directory before selecting `model_name="protenix-v2"`. See [`notes/storage.md`](../../../../notes/storage.md) for path resolution. * **`use_msa` defaults to `True`.** A ColabFold search generates an MSA for each protein chain. Set it `False` to fold single-sequence, or use an ESM/ISM mini variant to skip alignments entirely. Precomputed MSAs attached to the input are always used and override `use_msa=False`. * **Diffusion sampling is controlled by `seeds` and `num_diffusion_samples`.** Protenix draws `num_diffusion_samples` (default `5`) structures per seed and keeps the best by ranking score; the total number of candidates is `len(seeds)` times `num_diffusion_samples`. Setting `seed` overrides `seeds` with a single value for reproducibility. * **`num_pairformer_cycles` and `num_diffusion_steps` trade accuracy for time.** Defaults are checkpoint-aware: base and constraint variants use `10` cycles and `200` steps, while mini and tiny variants use `4` cycles and `5` steps, matching each checkpoint's native upstream schedule. Override either field to apply a custom schedule regardless of `model_name`. * **Confidence is reported as pLDDT, pTM, ipTM, gPDE, and PAE.** `confidence_score`, the ranking score and primary metric, selects the best sample; `avg_plddt` is on a 0 to 1 scale and PAE and gPDE are in angstroms. `has_clash` flags steric clashes. Set `include_pae_matrix` to attach the full per-token PAE matrix. * **Modified residues are supported.** Protein PTMs and DNA/RNA modifications are passed through as CCD codes, as in AlphaFold3.