Metadata-Version: 2.5
Name: binderqc
Version: 0.4.0
Summary: Quality-control and tag-site scoring for designed protein binders, from a predicted complex
Project-URL: Homepage, https://github.com/ssiddhantsharma/binderqc
Author: Siddhant Sharma
License: MIT License
        
        Copyright (c) 2026 Siddhant Sharma
        
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License-File: LICENSE
Keywords: binder,developability,protein-design,qc,sasa,structural-biology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: >=3.10
Requires-Dist: biotite>=1.0
Requires-Dist: numpy>=1.24
Requires-Dist: pandas>=2.0
Provides-Extra: docs
Requires-Dist: matplotlib; extra == 'docs'
Requires-Dist: playwright; extra == 'docs'
Provides-Extra: test
Requires-Dist: pytest; extra == 'test'
Provides-Extra: validation
Requires-Dist: matplotlib; extra == 'validation'
Requires-Dist: scipy; extra == 'validation'
Description-Content-Type: text/markdown

<h1 align="center">binderqc</h1>

<p align="center">
  Quality control and tag-site scoring for designed protein binders.
</p>

<p align="center">
  <a href="https://pypi.org/project/binderqc/"><img src="https://img.shields.io/pypi/v/binderqc.svg" alt="PyPI"></a>
  <a href="https://github.com/ssiddhantsharma/binderqc/actions/workflows/ci.yml"><img src="https://github.com/ssiddhantsharma/binderqc/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
  <a href="https://opensource.org/licenses/MIT"><img src="https://img.shields.io/badge/License-MIT-yellow.svg" alt="License: MIT"></a>
  <a href="https://www.python.org/"><img src="https://img.shields.io/badge/python-3.10%2B-blue.svg" alt="Python 3.10+"></a>
  <a href="https://doi.org/10.5281/zenodo.21529428"><img src="https://img.shields.io/badge/DOI-10.5281%2Fzenodo.21529428-blue.svg" alt="DOI"></a>
</p>

<p align="center">
  <img src="docs/schematic.png" width="860"
       alt="From a predicted binder-target complex, binderqc reports interface, pose, grippability, tag site, and developability">
</p>

## Install

```bash
pip install binderqc
```

Python 3.10+. Pulls in `biotite`, `numpy`, and `pandas`. To develop from source,
`pip install -e ".[test]"`.

## Usage

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

```python
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.

| flag | default | meaning |
|---|---|---|
| `--binder-chains` | auto-guess | comma-separated binder chain ids |
| `--target-chains` | all non-binder | comma-separated target chain ids |
| `--interface-cutoff` | `5.0` | heavy-atom contact distance (Å) |
| `--exposure-cutoff` | `0.25` | relSASA below which a terminus is buried |
| `--out` | `binderqc.csv` | output CSV path |
| `-j`, `--jobs` | `1` | worker processes to score a batch of files in parallel |
| `--fasta` | off | also write the QC-passing binders to this FASTA |

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

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

| recommended_tag | binder_bsa | epitope_planarity | epitope_aromatic_n | pi | qc_pass |
|---|---|---|---|---|---|
| C | 1021.4 | 3.21 | 11 | 4.17 | True |

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`). The check flags N-glycosylation sequons (N-X-[S/T]) that
  are exposed on the *free* target and sit at or near the epitope, where 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 (for example an IARA epitope mean,
  computed by the caller so binderqc stays dependency-clean) and returns
  `grippable`, `flat`, or `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 and algorithm, Pearson r≈0.92 vs the reference tool). Plus
  TAP-style surface charge patches (Raybould et al. 2019): `charge_patch_pos` and
  `charge_patch_neg` are the strongest exposed clusters of like charge, which net
  charge and pI miss and which drive viscosity and self-association. Following TAP's
  actual insight, the same idea is also computed over the binding region:
  `paratope_hydrophobicity` and `paratope_charge` say whether the interface itself is
  sticky or charge-clumped. These are reported numbers, not gates: TAP's thresholds
  are calibrated on antibody Fv surfaces and do not transfer to minibinders. It also
  reports the ProtParam instability index (Guruprasad 1990), GRAVY, pI, MW, and
  ε₂₈₀. Sequence liabilities cover deamidation (N-[G/S/T]), Asp isomerization
  (D-[G/S/T/D/H]), unpaired cysteines, N-glycosylation sequons, and Met/Trp
  oxidation hotspots. The oxidation check is SASA-aware, so only surface-exposed
  residues count.

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.

<details>
<summary>Full column list</summary>

`pdb, binder_chain, target_chains, n_interface_res, binder_bsa, n_hbonds,
n_salt_bridges, interface_packing, approach_angle, epitope_planarity,
epitope_hydrophobic_frac, epitope_aromatic_n, epitope_glyco_occluded,
epitope_glyco_sites, nterm_resnum, nterm_resname,
nterm_relsasa, nterm_dist_to_interface, nterm_orientation, nterm_sg_sasa,
cterm_resnum, cterm_resname, cterm_relsasa, cterm_dist_to_interface,
cterm_orientation, cterm_sg_sasa, recommended_tag, mw, gravy, pi,
instability_index, ext_coeff_280, sap_score, sap_total, a3d_score,
a3d_total_positive, charge_patch_pos, charge_patch_neg, paratope_hydrophobicity,
paratope_charge, sequence_liabilities, warnings, qc_pass, binder_sequence`
</details>

## Tests

```bash
pip install -e ".[test]"
pytest
```

Tests run 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 Å² and forms "multiple hydrogen bonds and salt
bridges … consistent with the subnanomolar affinities". binderqc on 7JZU agrees:
1021 Å² buried area (Cao reports ~1,000 Å²), 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](https://doi.org/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):

```bash
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 you can reuse it for your own figures:

```bash
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
