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```bibtex theme={null} @article{mitra2024deeppbs, title={Geometric deep learning of protein--DNA binding specificity}, author={Mitra, Raktim and Li, Jinsen and Sagendorf, Jared M and Jiang, Yibei and Cohen, Ari S and Chiu, Tsu-Pei and Glasscock, Cameron J and Rohs, Remo}, journal={Nature Methods}, volume={21}, number={9}, pages={1674--1683}, year={2024}, publisher={Nature Publishing Group}, doi={10.1038/s41592-024-02372-w} } @article{lu20033dna, title={3DNA: a software package for the analysis, rebuilding and visualization of three-dimensional nucleic acid structures}, author={Lu, Xiang-Jun and Olson, Wilma K}, journal={Nucleic Acids Research}, volume={31}, number={17}, pages={5108--5121}, year={2003}, publisher={Oxford University Press}, doi={10.1093/nar/gkg680} } @article{lu20083dna, title={3DNA: a versatile, integrated software system for the analysis, rebuilding and visualization of three-dimensional nucleic-acid structures}, author={Lu, Xiang-Jun and Olson, Wilma K}, journal={Nature Protocols}, volume={3}, number={7}, pages={1213--1227}, year={2008}, publisher={Nature Publishing Group}, doi={10.1038/nprot.2008.104} } ```

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### DeepPBS Specificity (`deeppbs-specificity`) Runs DeepPBS preprocessing and prediction on each input protein-DNA structure and returns, per structure, a canonical DNA PPM (`L x 4`, `A,C,G,T` order), the true DNA sequence indices, residue and DNA masks, per-base chain labels, and the path to a canonical `.npz` artifact. When a required DeepPBS dependency (the X3DNA `x3dna-dssr`/`analyze` binaries) is missing, or preprocessing or prediction fails to produce output, the tool raises by default. Set `allow_fallback=True` to instead emit a conservative fallback result: a uniform PPM (`0.25` per base) derived from the DNA residues in the input PDB, flagged with `used_fallback=True` and a human-readable `fallback_reason`. #### API Reference

Source PDB paths for protein-DNA structures to score. A single path string is normalized to a one-element list. At least one path is required.

Source Optional directory for canonical NPZ artifacts. A temporary directory is used when unset. Keep intermediate process and predict files. Pass --no\_cleanp to DeepPBS preprocessing to skip pdb2pqr-dependent protein cleaning. On missing DeepPBS deps/outputs, return a uniform fallback PPM instead of raising. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run DeepPBS inference on (inherited). 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 Canonicalized results per input, index-aligned with `inputs.pdb_paths`. Basename of the scored input structure. Source method tag, always "deeppbs". Path to canonical NPZ output. Canonical DNA PPM in A,C,G,T order. Canonical DNA truth indices in 0..3. Valid residue mask for canonical rows. DNA residue mask for canonical rows. Canonical chain IDs for canonical rows. Whether canonical output came from fallback logic. Reason for fallback when used\_fallback is true.

#### Applications * Estimating the DNA base preference of a designed or natural protein-DNA complex. * Scoring protein-DNA designs for specificity against a target motif. * Generating canonical PPMs for downstream motif comparison. #### Usage Tips * **Inputs are full protein-DNA PDB structures.** Provide a clean co-crystal containing both DNA strands; missing strands or non-standard residues can trigger the fallback path. * **DeepPBS, X3DNA, and the weights are provisioned for you.** The standalone setup clones a pinned revision of the DeepPBS repository (which bundles the X3DNA/DSSR binaries, the process/predict configs, and the trained inference weights) into the managed weights cache and installs it, so no repository or X3DNA path is configured. Point `PROTO_DEEPPBS_SPECIFICITY_WEIGHTS_DIR` at an existing checkout to reuse it. * **Fallback is opt-in.** By default a missing dependency or failed run raises; pass `allow_fallback=True` to get a uniform fallback PPM instead. A `used_fallback=True` result carries a uniform PPM, not a real prediction.