pyjess 0.10.1

Cython bindings and Python interface to JESS, a 3D template matching software.

🐍🔍 PyJess

Cython bindings and Python interface to Jess, a 3D template matching software.

🗺️ Overview

Jess is an algorithm for constraint-based structural template matching proposed by Jonathan Barker et al.[1]. It can be used to identify catalytic residues from a known template inside a protein structure. Jess is an evolution of TESS, a geometric hashing algorithm developed by Andrew Wallace et al.[2], removing some pre-computation and structural requirements from the original algorithm. Jess was further updated and maintained by Ioannis Riziotis during his PhD in the Thornton group.

PyJess is a Python module that provides bindings to Jess using Cython. It allows creating templates, querying them with protein structures, and retrieving the hits using a Python API without performing any external I/O. It's also more than 10x faster than Jess thanks to algorithmic optimizations added to improve the original Jess code while producing consistent results. PyJess was developed to support EnzyMM, the Enzyme Motif Miner[3].

🔧 Installing

PyJess is available for all modern Python versions (3.7+).

It can be installed directly from PyPI, which hosts some pre-built x86-64 wheels for Linux, MacOS, and Windows, as well as the code required to compile from source with Cython:

$ pip install pyjess

Otherwise, PyJess is also available as a Bioconda package:

$ conda install -c bioconda pyjess

Check the install page of the documentation for other ways to install PyJess on your machine.

🔖 Citation

PyJess is scientific software, published part of the EnzyMM[3] publication, and builds on top of Jess[2]. Please cite also Jess if you are using PyJess in an academic work, for instance as:

PyJess (Hackett et al., 2026), a Python library binding to Jess (Barker et al., 2003).

💡 Example

Prepare templates

Load Template objects to be used as references from different template files:

import pathlib
import pyjess

templates = []
for path in sorted(pathlib.Path("vendor/jess/examples").glob("template_*.qry")):
    templates.append(pyjess.Template.load(path, id=path.stem))

Prepare query structures

Load a Molecule (a PDB structure) from a PDB file, create one with the Python API, or convert it from a Bio.Model, gemmi.Model, or biotite.structure.AtomArray object:

# load from PDB file or mmCIF file
mol = pyjess.Molecule.load("vendor/jess/examples/test_pdbs/pdb1a0p.ent")

# load with BioPython
parser = Bio.PDB.PDBParser()
structure = parser.get_structure('pdb1a0p', "vendor/jess/examples/test_pdbs/pdb1a0p.ent")
mol = Molecule.from_biopython(structure, id="1a0p")

# load with Gemmi
structure = gemmi.read_pdb_string("vendor/jess/examples/test_pdbs/pdb1a0p.ent")
mol = Molecule.from_gemmi(structure[0], id="1a0p")

# load with Biotite
pdb_file = biotite.structure.io.pdb.PDBFile.read(f)
structure = pdb_file.get_structure(altloc="all", extra_fields=["atom_id", "b_factor", "occupancy", "charge"])
mol = Molecule.from_biotite(structure[0])

Match templates

Create a Jess instance and use it to query a against the stored templates:

jess = pyjess.Jess(templates)
query = jess.query(mol, rmsd_threshold=2.0, distance_cutoff=3.0, max_dynamic_distance=3.0)

Process hits

The hits are computed iteratively, and the different output statistics are computed on-the-fly when requested:

for hit in query:
    print(hit.molecule().id, hit.template().id, hit.rmsd, hit.log_evalue)
    for atom in hit.atoms():
        print(atom.name, atom.x, atom.y, atom.z)

Hits can also be rendered in PDB format like in the original Jess output, either by writing to a file directly, or to a Python string:

for hit in query:
    hit.dump(sys.stdout, format="pdb")

🧶 Thread-safety

Once a Jess instance has been created, the templates cannot be edited anymore, making the Jess.query method re-entrant and thread-safe. This allows querying several molecules against the same templates in parallel using e.g a ThreadPool:

molecules = []
for path in glob.glob("vendor/jess/examples/test_pdbs/*.ent"):
    molecules.append(Molecule.load(path))

with multiprocessing.ThreadPool() as pool:
    hits = pool.map(jess.query, molecules)

⚠️ Prior to PyJess v0.2.1, the Jess code was running some thread-unsafe operations which have now been patched. If running Jess in parallel, make sure to use v0.2.1 or later to use the code patched with re-entrant functions.

⏱️ Benchmarks

The following table reports the runtime of PyJess to match N=132 protein structures to the M=7607 templates of EnzyMM, using J=12 threads to parallelize.

Version	Runtime (s)	Match Speed (N * M / s * J)	Speedup
v0.4.2	618.1	135.4	N/A
v0.5.0	586.3	142.7	x1.05
v0.5.1	365.6	228.9	x1.69
v0.5.2	327.2	255.7	x1.88
v0.6.0	54.5	1535.4	x11.34
v0.7.0	52.4	1597.5	x11.80

Benchmarks were run on a quiet i7-1255U CPU running @4.70GHz with 10 physical cores / 12 logical cores.

💭 Feedback

⚠️ Issue Tracker

Found a bug ? Have an enhancement request ? Head over to the GitHub issue tracker if you need to report or ask something. If you are filing in on a bug, please include as much information as you can about the issue, and try to recreate the same bug in a simple, easily reproducible situation.

🏗️ Contributing

Contributions are more than welcome! See CONTRIBUTING.md for more details.

📋 Changelog

This project adheres to Semantic Versioning and provides a changelog in the Keep a Changelog format.

⚖️ License

This library is provided under the MIT License. The JESS code is distributed under the MIT License as well.

This project is in no way not affiliated, sponsored, or otherwise endorsed by the JESS authors. It was developed by Martin Larralde during his PhD project at the Leiden University Medical Center in the Zeller team.

📚 References

• [1] Barker, J. A., & Thornton, J. M. (2003). An algorithm for constraint-based structural template matching: application to 3D templates with statistical analysis. Bioinformatics (Oxford, England), 19(13), 1644–1649. doi:10.1093/bioinformatics/btg226.
• [2] Wallace, A. C., Borkakoti, N., & Thornton, J. M. (1997). TESS: a geometric hashing algorithm for deriving 3D coordinate templates for searching structural databases. Application to enzyme active sites. Protein science : a publication of the Protein Society, 6(11), 2308–2323. doi:10.1002/pro.5560061104.
• [3] Hackett, R. E., Riziotis, I. G., Larralde, M., Ribeiro, A. J. M., Zeller, G., Thornton, J. M. (2026). Investigating Enzyme Function by Geometric Matching of Catalytic Motifs. bioRxiv. 2026.02.10.705182; doi:10.64898/2026.02.10.705182.