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### AlphaFold DB Fetch (`alphafold-db-fetch`) Retrieves a single AlphaFold DB prediction record by UniProt accession and returns the predicted sequence and its 1-indexed coordinates, gene and organism metadata, mean pLDDT, the AFDB artifact URLs, the full JSON record, and an optional parsed `Structure` carrying per-residue pLDDT and optional pAE on `structure.metrics`. #### API Reference

Source UniProt accession to look up (e.g. 'P04637'). Isoform number to select from the multi-record AFDB response. `None` (default) returns the canonical entry (`AF-{accession}-F1`); `2` selects `AF-{accession}-2-F1`, etc. AFDB typically exposes isoforms 2-9 for human proteins. Raises `ValueError` if the requested isoform doesn't exist.

Source Structure file format. Available options: `pdb`, `cif` If True (default), fetch the structure body and the per-residue pLDDT array, returning a parsed `Structure` on the output. Set to False for metadata-only probes (URLs, mean pLDDT, gene, sequence) — saves \~100-500 KB per call, meaningful for batch sweeps. If True, also fetch the PAE (predicted aligned error) matrix and attach it to `output.structure.metrics["pae"]`. Disabled by default — PAE files can be tens of MB for long proteins. No-op when `include_structure=False`. If True, fetch the A3M MSA used as input to the AlphaFold prediction. Disabled by default — A3M files can be hundreds of KB to several MB for highly conserved proteins. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the tool on. Maximum execution time in seconds. `None` waits indefinitely. 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.

Source Primary UniProt accession that was looked up. AlphaFold entry identifier (e.g. 'AF-P04637-F1'). Gene symbol from the AlphaFold record. Source organism scientific name. NCBI taxonomy ID. Amino-acid sequence covered by the prediction. Length of the predicted sequence. 1-indexed start residue of the prediction (relative to the full UniProt sequence; >1 only for non-first fragments of very long proteins). 1-indexed inclusive end residue of the prediction. Latest version of the AlphaFold DB prediction (this is the version of the served prediction; AlphaFold DB always serves the latest). ISO 8601 timestamp when this prediction was generated. Mean per-residue pLDDT for the prediction (AlphaFold DB's globalMetricValue field). Always populated from the metadata response, regardless of `include_structure`; when `include_structure=True` it is also mirrored at `structure.metrics["avg_plddt"]`. URL to the PDB structure file on AlphaFold DB. URL to the mmCIF structure file on AlphaFold DB. URL to the BinaryCIF structure file; `None` on legacy entries that predate the bcif export. URL to the PAE JSON document on AlphaFold DB. URL to the per-residue pLDDT JSON document on AlphaFold DB. URL to the rendered PAE PNG on AlphaFold DB. URL to the MSA A3M used for prediction, when present. AlphaMissense pathogenicity CSV URL (sequence coords); None for non-human or unscored entries. AlphaMissense annotations on GRCh37. AlphaMissense annotations on GRCh38. CRC64 checksum of the predicted sequence. Parsed AlphaFold structure (PDB or mmCIF body in `structure_format`, `b_factor_type=BFactorType.PLDDT`) with an :class:`AlphaFoldDBMetrics` `metrics` container carrying `avg_plddt`, `plddt_per_residue`, and (when `include_pae=True`) `pae`. None when `include_structure=False`. A3M-format MSA contents used as input to the AlphaFold prediction. None when `include_msa` is False or when the entry has no associated MSA URL. AlphaFold DB API URL used for the metadata lookup. Complete AlphaFold DB JSON record for advanced programmatic access. **Metrics** | Metric | Type | Range | Availability | | ------------------- | ------------------- | ------------ | ---------------------- | | `avg_plddt` | float | 0.0 to 100.0 | always | | `plddt_per_residue` | list\[float] | 0.0 to 100.0 | always | | `pae` | list\[list\[float]] | ≥ 0.0 | when include\_pae=True |

#### Applications Use this to pull an AlphaFold-predicted structure into a pipeline when no experimental entry is needed: fetch a target by accession before inverse folding, docking, or binder design, screen accessions for AFDB coverage with metadata-only requests, or assess per-residue and pairwise confidence before structure-based work. The returned `Structure` feeds directly into structure-consuming tools such as TM-align, US-align, and structure scoring. The [UniProt](https://bio-pro.mintlify.app/tools/database-retrieval/uniprot) tool supplies the UniProt accession from a gene name and organism, and the [PDB](https://bio-pro.mintlify.app/tools/database-retrieval/pdb) tool provides the experimental counterpart when one exists. #### Usage Tips * **Coverage is broad but not universal.** When AFDB has no prediction for an accession the tool raises `ValueError`. Catch that error and fall back to predicting the structure from sequence. * **A high `mean_plddt` can hide locally unreliable regions.** Inspect the per-residue pLDDT on `structure.metrics` before trusting any specific residue. * **`latest_version` advances when AFDB refreshes a prediction.** Cache it alongside any structure you persist and refetch when it moves past the cached value. * **Multiple records signal isoforms or fragments.** The canonical record is selected by default and a warning lists the alternatives. To select a non-canonical isoform, pass the `isoform` input, and check `entry_id`, `sequence_start`, and `sequence_end` to confirm which record was returned. * **Low-confidence regions are usually real disorder, not a prediction error.** Disordered or flexibly linked regions get very low per-residue confidence (pLDDT) and high predicted aligned error (pAE) between regions because they have no single fixed shape. Find those residue ranges from the per-residue pLDDT array and trim or down-weight just those residues. Do not throw away the whole prediction, because the confident domains are still reliable.