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NA-MPNN
UW
License: NA-MPNN uses MIT for code and BSD-3-Clause for model weights and may require explicit attribution when utilized. Please refer to the code license and model weights license for full terms.

Proto is not affiliated with University of Washington. This toolkit is open source and builds on the implementation produced by this organization. Product names, logos, and trademarks are the property of their respective owners.

baker-laboratory/NA-MPNN
baker-laboratory/NA-MPNN
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RNA sequence design and protein—DNA specificity prediction with NA-MPNN
Andrew Kubaney
bioRxiv (2025)
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evo-design/proto-tools/proto_tools/tools/sequence_scoring/na_mpnn_specificity
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Toolkit contributors

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Background

Sequence-specific DNA-binding proteins recognize their target sites through a combination of direct base contacts and indirect shape readout. Predicting the base preference a protein imposes at each position of a bound DNA site is central to engineering transcription factors, characterizing binding specificity, and designing synthetic regulatory elements such as repressors and promoters. NA-MPNN (Kubaney et al., 2025) is a message-passing neural network that represents proteins, DNA, and RNA within a single unified biopolymer graph, framing both RNA sequence design and protein–DNA binding specificity as nucleic-acid inverse-folding problems (recovering likely sequences for a fixed three-dimensional structure). For specificity prediction, the model conditions on a fixed-docked protein–DNA complex and emits per-position nucleotide probabilities over the DNA chains, which this toolkit canonicalizes into a DNA-only L x 4 PPM together with the recovered true sequence and chain labels.

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Learning Resources

  • baker-laboratory/NA-MPNN. Official repository with the training and inference code, installation instructions, and usage examples.

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Tools

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NA-MPNN Specificity (na-mpnn-specificity)

Predicts the DNA base preference of a bound protein from one or more protein–DNA complex structures. For each input PDB, the tool runs NA-MPNN specificity inference, restricts the output to valid DNA positions, and returns a canonical PPM (L x 4, A,C,G,T order), the recovered true DNA sequence (indices 0..3), per-row masks, and chain labels. Each result is also written to a canonical .npz file whose path is returned in output_npz_path.

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API Reference

Source
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List[string]
required
PDB file paths for protein-DNA complexes to score. A bare string is normalized into a single-element list.
Source
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integer
default:"8"
Batch size per inference call.
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integer
default:"1"
Number of prediction batches to run.
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number
default:"0.1"
Sampling temperature for NA-MPNN.
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string
default:""
Residues to omit during NA-MPNN sampling.
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boolean
default:"True"
Restrict design tokens to nucleic acid positions.
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string
Optional directory for canonical NPZ artifacts.
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boolean
default:"False"
Keep intermediate raw NA-MPNN output directories.
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integer
default:"0"
Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). True is coerced to 1 and False to 0.
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string
default:"cuda"
Device to run inference on (inherited).
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integer
default:"3600"
Maximum execution time in seconds. None waits indefinitely.
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integer
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
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List[NAMPNNSpecificityResult]
required
Canonicalized result per input structure, index-aligned with pdb_paths.

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Applications

This tool is appropriate for scoring and ranking candidate protein–DNA binder designs by their predicted specificity, for comparing the base preference of a designed binder against a target motif inside an optimization loop, and for characterizing the specificity landscape of natural or engineered DNA-binding proteins. The per-position PPM can be compared to a desired motif to drive specificity-based selection.

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Usage Tips

  • The NA-MPNN checkout and checkpoint are provisioned for you. The standalone setup clones a pinned revision of the repository (which ships the specificity checkpoint in-tree) into the managed weights cache, so no repository or checkpoint path is configured. Point PROTO_NA_MPNN_SPECIFICITY_WEIGHTS_DIR at an existing checkout to reuse it. A run that uses device='proto' fails fast because these local resources cannot be staged.
  • predicted_ppm rows are DNA-only and renormalized. Only positions that are both valid and DNA are kept, and each row is renormalized over A,C,G,T, so the returned matrix already excludes protein and masked positions.
  • temperature controls sampling sharpness. Lower values (default 0.1) concentrate probability on the most-preferred base; raise it to soften the distribution.
  • output_directory and keep_intermediate control artifacts. Leave output_directory unset to write canonical .npz files to a temporary directory, or set it to persist them. Set keep_intermediate=True to retain the raw NA-MPNN output for debugging.

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Toolkit Notes

  • NA-MPNN runs as an isolated standalone environment that shells out to the upstream NA-MPNN inference CLI; the heavy model dependencies stay out of the main environment.
  • The standalone setup clones a pinned NA-MPNN revision (with its in-tree specificity checkpoint) into the managed weights cache. If the clone cannot reach GitHub, the environment setup signals a clean test skip rather than a hard failure.
Example notebook: See the full working example for a copy-paste-ready walkthrough.

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Infrastructure Guides

The following guides cover how to run tools efficiently and at scale.

Tool Persistence

Keep a tool’s model warm across calls instead of reloading it every invocation.

Device Management

How GPUs are allocated to tools and how to target specific devices.

Parallel Execution

Fan a batch of inputs out across multiple GPUs.