binderqc

Quality control and tag-site scoring for designed protein binders.

Install

pip install binderqc

Python 3.10+. Pulls in biotite, numpy, and pandas. For development from source: pip install -e ".[test]".

Usage

binderqc --binder-chains A --target-chains B --out out.csv complex.cif some_dir/

from binderqc import score_structure
rows = score_structure("complex.pdb", binder_chains=["A"], target_chains=["B"])

Inputs are PDB/CIF files, globs, or directories. Leave --binder-chains off to guess the binder as the shortest chain (20-250 aa, printed for each file); --target-chains defaults to the remaining chains.

Scoring is CPU-only (no folding, no GPU, no network). Files in a batch are independent, so -j scales near-linearly across cores for large directories.

Example output for the bundled LCB1 minibinder (a few of the columns):

Its warnings field reads "both termini ~equidistant from interface (ambiguous)", a tag-site advisory, so qc_pass stays True.

What it reports

Per binder chain:

• Interface: buried surface area, interface residue count, hydrogen-bond and salt-bridge counts, and a contact-packing density (a lightweight proxy for contact molecular surface).
• Pose: approach angle (end-on vs. lying across the surface).
• Grippability: epitope planarity, hydrophobic fraction, aromatic anchors, and a SASA-aware glyco-occlusion check (epitope_glyco_occluded, epitope_glyco_sites) — N-glycosylation sequons (N-X-[S/T]) that are exposed on the free target and sit at/near the epitope, so an installed glycan would mask an otherwise grippable patch. grippability_consensus(row, iara_score) optionally cross-checks this physical read against a learned target-side score (e.g. an IARA epitope mean, computed by the caller so binderqc stays dependency-clean), returning grippable / flat / disagree; pass --iara-score on the CLI to add it as a column.
• Tag site: recommended terminus (N/C) and the numbers behind it: relative SASA, CA-CA distance to the paratope, orientation, and a terminal cysteine's SG SASA.
• Developability: two complementary aggregation scores — an SAP-style spatial aggregation score (sap_score/sap_total) and an Aggrescan3D score (a3d_score/a3d_total_positive, a faithful pure-Python port of Aggrescan3D 1.0.2's a3v scale + algorithm; Pearson r≈0.92 vs the reference tool) — plus the ProtParam instability index (Guruprasad 1990), GRAVY, pI, MW, ε₂₈₀, and sequence liabilities. Liabilities cover deamidation (N-[G/S/T]), Asp isomerization (D-[G/S/T/D/H]), unpaired cysteines, N-glycosylation sequons, and — SASA-aware, so only surface-exposed residues count — Met/Trp oxidation hotspots.

A warnings column flags problems (small, flat, anchorless, or glyco-occluded interfaces; buried, ambiguous, or interface-facing tag sites; hydrophobic sequences). qc_pass is true when there are no quality warnings (tag-site advisories like an ambiguous terminus do not count), and --fasta writes those binders.

Tests

pip install -e ".[test]"
pytest

Runs against a bundled example, PDB 7JZU (the LCB1 minibinder on the SARS-CoV-2 RBD). LCB1 comes from Cao et al. (2020), which frames the problem binderqc targets: the bottleneck is selecting good binders, not designing them.

"…not in the de novo design of proteins with shape and chemical complementarity to the target surface, but in recognizing the best candidates."

In that work LCB1 buries ~1,000 Ų and forms "multiple hydrogen bonds and salt bridges … consistent with the subnanomolar affinities". binderqc on 7JZU agrees: 1021 Ų buried area (Cao reports ~1,000 Ų), plus 2 salt bridges and 17 interface polar contacts. See Cao et al., De novo design of picomolar SARS-CoV-2 miniprotein inhibitors, Science 370, 426-431 (2020), doi:10.1126/science.abd9909.

tests/pisa_correctness.py is a separate script (not part of the unit tests). It downloads 18 public complexes from RCSB and PDBePISA and checks the interface area against PISA (r ~ 1.0, about 1% median error):

pip install -e ".[validation]"
python tests/pisa_correctness.py

Figures

The complex in the banner is a real render, not a drawing: docs/render_structure.py draws a cartoon under a translucent molecular surface (binder blue, target grey) from actual coordinates, using 3Dmol.js in headless Chromium. It works on any PDB/CIF, so it is reusable for your own figures:

pip install -e ".[docs]" && python -m playwright install chromium
python docs/render_structure.py complex.pdb A B out.png   # structure, binder chain, target chain, output

License

MIT