Metadata-Version: 2.1
Name: proabc-2
Version: 0.1.1
Summary: Predicts the antibody residues that will make contact with the antigen and the type of interaction using a Convolutional Neural Network.
License: Apache-2.0
Author: BonvinLab
Author-email: bonvinlab.support@uu.nl
Requires-Python: >=3.7,<4.0
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: Unix
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Dist: biopython (==1.74)
Requires-Dist: numpy (>=1.17,<2.0)
Requires-Dist: pandas (>=0.25,<0.26)
Requires-Dist: protobuf (<=3.20.1)
Requires-Dist: tensorflow (==1.14.0)
Description-Content-Type: text/markdown

# proABC-2
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![proabc2 logo](https://raw.githubusercontent.com/haddocking/proABC-2/main/logo/logo.png)

Predicts the antibody residues that will make contact with the antigen and the type of interaction using a Convolutional Neural Network (CNN).

## Installation

proABC-2 is available on PyPI and can be installed using pip:

```text
pip install proabc-2
```

It also depends on two third-party software, HMMER and IGBLAST, check the [third-party](THIRD_PARTY.md) section for more information.

## Example

Set up the data to run the example:

- Create a folder named `proabc2-prediction` in the root directory.

```bash
mkdir proabc2-prediction
```

- Create a heavy and light fasta file **inside** `proabc2-prediction` with the following content:

```text
echo ">APDB_H\nEVQLVESGGGLVQPGGSLRLSCAASGYTFTNYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPHYYGSSHWYFDVWGQGTLVTVSS" > proabc2-prediction/heavy.fasta
```

```text
echo ">APDB_L\nDIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIKRTV" > proabc2-prediction/light.fasta
```

- Execute `proabc-2`:

The input is in the following format:

```bash
proabc2 <input-folder> <heavy-fasta-filename> <light-fasta-filename>
```

So in this example:

```
proabc2 proabc2-prediction/ heavy.fasta light.fasta
```

The output will be in the same folder as the input files, named as `heavy-pred.csv` and `light-pred.csv`.

They consist of several columns:

- **Chothia**: position of the residue according to the Chothia numbering scheme
- **Sequence**: residue type for each position
- **pt**: probability of making a general interaction with the antigen
- **hb**: probability of making a hydrogen bonds with the antigen
- **hy**: probability of making a hydrophobic interaction with the antigen

| Chothia | Sequence |  pt  |  hb  |  hy  |
| :-----: | :------: | :--: | :--: | :--: |
|    1    |    E     | 0.23 | 0.17 | 0.24 |
|    2    |    V     | 0.23 | 0.15 | 0.23 |
|    3    |    Q     | 0.14 | 0.14 | 0.16 |
|   ...   |   ...    | ...  | ...  | ...  |

```bash
$ head proabc2-prediction/*pred.csv
==> proabc2-prediction/heavy-pred.csv <==
,Chothia,Sequence,pt,hb,hy
0,1,E,0.24,0.18,0.24
1,2,V,0.25,0.15,0.25
2,3,Q,0.16,0.16,0.17
3,4,L,0.14,0.14,0.17
4,5,V,0.14,0.15,0.15
5,6,E,0.16,0.16,0.16
6,7,S,0.14,0.16,0.13
7,8,G,0.17,0.13,0.16
8,9,G,0.14,0.14,0.15

==> proabc2-prediction/light-pred.csv <==
,Chothia,Sequence,pt,hb,hy
0,1,D,0.25,0.18,0.2
1,2,I,0.23,0.15,0.2
2,3,Q,0.15,0.16,0.17
3,4,M,0.15,0.14,0.15
4,5,T,0.16,0.15,0.16
5,6,Q,0.15,0.16,0.14
6,7,S,0.15,0.14,0.12
7,8,P,0.15,0.14,0.13
8,9,S,0.14,0.14,0.14
```

**proABC-2** also accepts the DNA sequences of the antibody chains and uses the [_Biopython Seq module_](https://biopython.org/DIST/docs/api/Bio.Seq-module.html) for the translation into protein sequences.

## Citation

- F. Ambrosetti, T.H. Olsen, P.P. Olimpieri, B. Jiménez-García, E. Milanetti, P. Marcatilli, A.M.J.J. Bonvin. ["proABC-2: PRediction Of AntiBody Contacts v2 and its application to information-driven docking"](https://doi.org/10.1093/bioinformatics/btaa644), _Bioinformatics_, , btaa644,

