SignalP 6.0 and DeepTMHMM (optional tools)

SignalP 6.0 and DeepTMHMM (optional tools)#

run_dbcan can add signal peptide and transmembrane topology columns to overview.tsv during CAZyme_annotation. These features rely on SignalP 6.0 and/or DeepTMHMM, which are not distributed or installed as dependencies of the dbcan package. You must install them yourself, verify that they run in your environment, and only then enable the corresponding flags.

Why separate installation?#

  • Licensing and distribution: Upstream tools have their own licenses and release models; we integrate them only as optional wrappers.

  • Heavy dependencies: SignalP 6.0 and DeepTMHMM typically need PyTorch and other large stacks; keeping them optional avoids forcing every user to install them.

  • Environment-specific failures: GPU/CPU, libstdc++, and PyTorch versions differ across clusters; local testing is essential.

SignalP 6.0#

Role in run_dbcan

When you pass --run_signalp to CAZyme_annotation, the pipeline invokes the signalp6 executable, writes results under the output directory, and fills the SignalP column in overview.tsv.

Install

Follow the official SignalP 6.0 installation instructions from the upstream project (e.g. installation instructions). Ensure the signalp6 command is on your PATH in the same environment you use to run run_dbcan.

Recommended checks before using run_dbcan

  1. Run signalp6 --help (or your install’s equivalent) and confirm the CLI starts without import errors.

  2. Run a small FASTA through SignalP 6.0 alone and confirm you get the expected output files.

Common environment issue (Linux)

If you see errors such as CXXABI_1.3.15 missing when importing matplotlib inside SignalP, the dynamic linker may be picking an older system libstdc++ instead of the one from your conda environment. Typical mitigations:

  • Activate the conda environment that contains SignalP and run run_dbcan from that same environment; the pipeline prepends that environment’s lib directory to LD_LIBRARY_PATH for the SignalP subprocess when possible.

  • If you still invoke signalp6 manually in a bare shell, try:

    export LD_LIBRARY_PATH="${CONDA_PREFIX}/lib:${LD_LIBRARY_PATH}"

    (adjust if you are not using conda).

run_dbcan options (SignalP)

  • --run_signalp / --no-run_signalp — enable or disable SignalP.

  • --signalp_org — other or euk (organism class passed to SignalP).

DeepTMHMM#

Role in run_dbcan

When you pass --run_deeptmhmm and --deeptmhmm_dir, the pipeline runs a local DeepTMHMM predict.py from that directory (with working directory set there), writes outputs to a deeptmhmm_out folder under your CAZyme_annotation output directory, and fills the DeepTMHMM column in overview.tsv.

Install

Obtain a DeepTMHMM (or compatible) checkout that contains predict.py and its model/assets, as required by the upstream tool. Point --deeptmhmm_dir at the directory that contains predict.py.

Recommended checks before using run_dbcan

  1. cd into that directory and run your chosen Python on predict.py --help (or the upstream documented test command).

  2. Run one short FASTA through predict.py with a fresh output directory and confirm topology files are produced.

PyTorch 2.6+ and saved checkpoints

Some DeepTMHMM checkpoints are pickled objects (not “weights only”). If you see weights_only / UnpicklingError when loading esm_model_args.pt or similar, use a DeepTMHMM tree that loads trusted checkpoints with weights_only=False (or follow PyTorch’s guidance for allowlisted globals). Only load checkpoints you trust.

run_dbcan options (DeepTMHMM)

  • --run_deeptmhmm / --no-run_deeptmhmm — enable or disable DeepTMHMM.

  • --deeptmhmm_dir — path to the DeepTMHMM install (directory containing predict.py).

  • --deeptmhmm_python — Python executable to use (default: python). Use this if DeepTMHMM must run in a specific conda env interpreter.

Column overwrite

Use --force_topology if you want to replace existing SignalP / DeepTMHMM values in overview.tsv instead of only filling empty cells.

Example (both tools enabled)#

After you have verified SignalP and DeepTMHMM independently, you can run (adjust paths):

run_dbcan CAZyme_annotation \
  --input_raw_data proteins.faa \
  --output_dir out \
  --db_dir db \
  --mode protein \
  --run_signalp \
  --run_deeptmhmm \
  --deeptmhmm_dir /path/to/DeepTMHMM

See also CAZyme Annotation for the full annotation workflow.

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