ISOPEPtide bond detecTOR

Python package for the detection of intamolecular isopeptide bonds in protein structures. The method is described in "Isopeptor: a tool for detecting intramolecular isopeptide bonds in protein structures".

Isopeptor can be accessed via this google colab or installed and run locally.

Read the documentation for complete information on its usage and examples.

Installation

pip install isopeptor

Usage

From the command line:

isopeptor tests/data/test_structures/

Output:

protein_name        probability     chain   r1_bond r_cat   r2_bond r1_bond_name    r_cat_name      r2_bond_name    bond_type       rmsd    r_asa   template             
8beg                0.991           A       590     636     729     LYS             ASP             ASN             CnaA-like       0.0     0.1     8beg_A_590_636_729   
8beg                0.991           A       756     806     894     LYS             ASP             ASN             CnaA-like       0.0     0.1     8beg_A_756_806_894   
8beg                0.991           A       922     973     1049    LYS             ASP             ASN             CnaA-like       0.0     0.1     8beg_A_922_973_1049  
8beg                0.991           A       1076    1123    1211    LYS             ASP             ASN             CnaA-like       0.0     0.1     8beg_A_1076_1123_1211
5dz9                0.991           A       556     606     703     LYS             ASP             ASN             CnaA-like       0.0     0.1     4z1p_A_3_53_150      
5dz9                0.991           A       730     776     861     LYS             ASP             ASN             CnaA-like       0.0     0.1     4z1p_A_177_223_308   
4z1p                0.991           A       3       53      150     LYS             ASP             ASN             CnaA-like       0.0     0.1     4z1p_A_3_53_150      
4z1p                0.991           A       177     223     308     LYS             ASP             ASN             CnaA-like       0.0     0.1     4z1p_A_177_223_308   
7woi                0.909           B       57      158     195     LYS             GLU             ASN             CnaB-like       0.314   0.1     5j4m_A_47_139_172    
1amx                0.882           A       176     209     293     LYS             ASP             ASN             CnaA-like       0.353   0.1     2f68_X_176_209_293   
7woi                0.875           A       203     246     318     LYS             ASP             ASN             CnaA-like       0.363   0.1     4hss_A_187_224_299   
7woi                0.838           B       355     435     466     LYS             GLU             ASN             CnaB-like       0.403   0.1     8f70_A_299_386_437   
6to1_af             0.607           A       13      334     420     LYS             ASP             ASN             CnaA-like       0.565   0.1     5mkc_A_191_600_695   

To redirect the output to a .tsv file use:

isopeptor tests/data/test_structures/ > output.tsv

Full command line options:

usage: isopeptor [-h] [--distance DISTANCE] [--fixed_r_asa FIXED_R_ASA] [--eval_geometry] path_to_structure_files

Run isopeptide bond prediction from command line. Usage: isopeptor path/to pdb files/ > isopeptide_bonds.csv

positional arguments:
  path_to_structure_files
                        Path to directory containing .pdb/.cif files.

options:
  -h, --help            show this help message and exit
  --distance DISTANCE   Specifies permissivity of jess search. The higher, the more permissive.
  --fixed_r_asa FIXED_R_ASA
                        Fixes the relative solvent accessible area using a value between 0 and 1 to speed up the prediction.
  --eval_geometry       Run geometric evaluation of isopeptide bonds.

Test

python -m unittest discover -s tests -p "test_isopeptide.py"

Reference

If you use isopeptor please cite:

@article{10.1093/bioadv/vbaf049,
    author = {Costa, Francesco and Barringer, Rob and Riziotis, Ioannis and Andreeva, Antonina and Bateman, Alex},
    title = {Isopeptor: a tool for detecting intramolecular isopeptide bonds in protein structures},
    journal = {Bioinformatics Advances},
    volume = {5},
    number = {1},
    pages = {vbaf049},
    year = {2025},
    month = {03},
    issn = {2635-0041},
    doi = {10.1093/bioadv/vbaf049},
    url = {https://doi.org/10.1093/bioadv/vbaf049},
    eprint = {https://academic.oup.com/bioinformaticsadvances/article-pdf/5/1/vbaf049/62375500/vbaf049.pdf},
}