pyconfind 0.1.0

Fast Python implementation of confind — protein side-chain contact-degree analysis.

pyconfind

A modern Python implementation of confind — the rotamer-based protein side-chain contact-degree analysis introduced in Zheng & Grigoryan's work on tertiary structural motifs.

The Python output is byte-for-byte identical to the upstream C++ binary on 248 of 253 real structures tested (100 single-chain PDB + 100 AlphaFold DB + 50 multi-chain + 3 high-resolution; see docs/stress_test_results.md) and on all 11 example structures shipped with the original codebase. The 5 exceptions are insertion-code structures where the C++ ordering relies on undefined behavior (documented).

pyconfind is also faster than the C++ binary, with two interchangeable contact-degree backends (both byte-identical to the reference):

• a pure NumPy/SciPy reference, which on its own already beats the C++ binary;
• an optional Numba JIT/multi-threaded backend (pip install pyconfind[fast]) that is ~2-3× faster again.

With the Numba backend and the rotamer library amortized across a batch, the per-structure analysis is ~8-18× faster than the C++ binary.

Runtime scales sub-quadratically with sequence length (the CA-distance cutoff bounds each residue's neighbor count). See docs/benchmark.md for details.

Install

pip install -e ".[dev]"      # includes the Numba fast backend
# or, runtime only:
pip install -e .             # pure-Python reference backend
pip install -e ".[fast]"     # + Numba backend

Example notebook

examples/pyconfind_demo.ipynb is a runnable walkthrough (install → fetch a PDB → analyze via the library API → visualize a contact map, per-residue scores, and a 3D structure colored by contact degree). Click the badge to run it on a free Colab CPU runtime.

Quick start

CLI (matches the original confind flag names, so existing pipelines drop in):

pyconfind --p input.pdb --rLib path/to/rotlibs --o out.cont
# Inputs may be PDB or mmCIF (format auto-detected via gemmi):
pyconfind --p input.cif --rLib path/to/rotlibs --o out.cont
# Modern structured output:
pyconfind --p input.pdb --rLib path/to/rotlibs --json --o out.json
# Only consider the native AA at each position (no AA substitution):
pyconfind --p input.pdb --rLib path/to/rotlibs --native-only --o out.cont
# Restrict the computed/output residues (MSL selection language):
pyconfind --p input.pdb --rLib path/to/rotlibs --sel "chain A AND resi 20-60" --o out.cont
# Pre-select part of the structure before anything runs:
pyconfind --p input.pdb --rLib path/to/rotlibs --psel "NAME CA WITHIN 25 OF CHAIN A" --o out.cont

Library API:

from pyconfind import analyze, format_confind_text

result = analyze("input.pdb", rotamer_library="path/to/rotlibs")
print(format_confind_text(result.positions, result.report))

# Inspect raw contacts:
for c in result.report.contacts:
    pi, pj = result.positions[c.pos_i], result.positions[c.pos_j]
    print(f"{pi.position.chain},{pi.position.resnum} <-> "
          f"{pj.position.chain},{pj.position.resnum}: degree={c.degree}")

Rotamer libraries

Out of the box, pyconfind supports the Dunbrack 2010 MSL-format library that ships with the upstream confind source (EBL.out + BEBL.out). Point --rLib at a directory containing both files (backbone-dependent) or at a single EBL.out-style file (backbone-independent).

Modern Dunbrack and Richardson-style libraries are next on the roadmap.

Native-only mode (extension over the C++ binary)

The original C++ confind substitutes in all 18 non-Gly/Pro amino acids at every position and computes contact degree across the full rotamer space. pyconfind adds --native-only: at each position, only place rotamers of the native amino acid (but still consider every rotamer of that AA). Useful when you want a contact-degree estimate that holds the sequence fixed.

Validation

The C++ reference binary is built from the upstream tarball by:

scripts/build-reference.sh

The byte-identity tests then compare pyconfind's output against the C++ output on every example PDB. To run them yourself:

pytest tests/

References

• "Sequence statistics of tertiary structural motifs reflect protein stability", F. Zheng, G. Grigoryan, PLoS ONE, 12(5): e0178272, 2017.

• "Tertiary Structural Propensities Reveal Fundamental Sequence/Structure Relationships", F. Zheng, J. Zhang, G. Grigoryan, Structure, 23(5): 961-971, 2015.