| Name | proabc-2 JSON |
| Version |
0.1.1
JSON |
| download |
| home_page | None |
| Summary | Predicts the antibody residues that will make contact with the antigen and the type of interaction using a Convolutional Neural Network. |
| upload_time | 2024-05-02 09:21:34 |
| maintainer | None |
| docs_url | None |
| author | BonvinLab |
| requires_python | <4.0,>=3.7 |
| license | Apache-2.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
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| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# proABC-2
[](https://github.com/haddocking/proABC-2/actions/workflows/ci.yml)
[](https://app.codacy.com/gh/haddocking/proABC-2/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)
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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,
Raw data
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"description": "# proABC-2\n\n\n\n[](https://github.com/haddocking/proABC-2/actions/workflows/ci.yml)\n[](https://app.codacy.com/gh/haddocking/proABC-2/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_grade)\n[](https://app.codacy.com/gh/haddocking/proABC-2/dashboard?utm_source=gh&utm_medium=referral&utm_content=&utm_campaign=Badge_coverage)\n\n\n\nPredicts the antibody residues that will make contact with the antigen and the type of interaction using a Convolutional Neural Network (CNN).\n\n## Installation\n\nproABC-2 is available on PyPI and can be installed using pip:\n\n```text\npip install proabc-2\n```\n\nIt also depends on two third-party software, HMMER and IGBLAST, check the [third-party](THIRD_PARTY.md) section for more information.\n\n## Example\n\nSet up the data to run the example:\n\n- Create a folder named `proabc2-prediction` in the root directory.\n\n```bash\nmkdir proabc2-prediction\n```\n\n- Create a heavy and light fasta file **inside** `proabc2-prediction` with the following content:\n\n```text\necho \">APDB_H\\nEVQLVESGGGLVQPGGSLRLSCAASGYTFTNYGMNWVRQAPGKGLEWVGWINTYTGEPTYAADFKRRFTFSLDTSKSTAYLQMNSLRAEDTAVYYCAKYPHYYGSSHWYFDVWGQGTLVTVSS\" > proabc2-prediction/heavy.fasta\n```\n\n```text\necho \">APDB_L\\nDIQMTQSPSSLSASVGDRVTITCSASQDISNYLNWYQQKPGKAPKVLIYFTSSLHSGVPSRFSGSGSGTDFTLTISSLQPEDFATYYCQQYSTVPWTFGQGTKVEIKRTV\" > proabc2-prediction/light.fasta\n```\n\n- Execute `proabc-2`:\n\nThe input is in the following format:\n\n```bash\nproabc2 <input-folder> <heavy-fasta-filename> <light-fasta-filename>\n```\n\nSo in this example:\n\n```\nproabc2 proabc2-prediction/ heavy.fasta light.fasta\n```\n\nThe output will be in the same folder as the input files, named as `heavy-pred.csv` and `light-pred.csv`.\n\nThey consist of several columns:\n\n- **Chothia**: position of the residue according to the Chothia numbering scheme\n- **Sequence**: residue type for each position\n- **pt**: probability of making a general interaction with the antigen\n- **hb**: probability of making a hydrogen bonds with the antigen\n- **hy**: probability of making a hydrophobic interaction with the antigen\n\n| Chothia | Sequence | pt | hb | hy |\n| :-----: | :------: | :--: | :--: | :--: |\n| 1 | E | 0.23 | 0.17 | 0.24 |\n| 2 | V | 0.23 | 0.15 | 0.23 |\n| 3 | Q | 0.14 | 0.14 | 0.16 |\n| ... | ... | ... | ... | ... |\n\n```bash\n$ head proabc2-prediction/*pred.csv\n==> proabc2-prediction/heavy-pred.csv <==\n,Chothia,Sequence,pt,hb,hy\n0,1,E,0.24,0.18,0.24\n1,2,V,0.25,0.15,0.25\n2,3,Q,0.16,0.16,0.17\n3,4,L,0.14,0.14,0.17\n4,5,V,0.14,0.15,0.15\n5,6,E,0.16,0.16,0.16\n6,7,S,0.14,0.16,0.13\n7,8,G,0.17,0.13,0.16\n8,9,G,0.14,0.14,0.15\n\n==> proabc2-prediction/light-pred.csv <==\n,Chothia,Sequence,pt,hb,hy\n0,1,D,0.25,0.18,0.2\n1,2,I,0.23,0.15,0.2\n2,3,Q,0.15,0.16,0.17\n3,4,M,0.15,0.14,0.15\n4,5,T,0.16,0.15,0.16\n5,6,Q,0.15,0.16,0.14\n6,7,S,0.15,0.14,0.12\n7,8,P,0.15,0.14,0.13\n8,9,S,0.14,0.14,0.14\n```\n\n**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.\n\n## Citation\n\n- F. Ambrosetti, T.H. Olsen, P.P. Olimpieri, B. Jim\u00e9nez-Garc\u00eda, 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,\n",
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