rossmann-toolbox 0.1.0

Prediction and re-engineering of the cofactor specificity of Rossmann-fold proteins

Prediction and re-engineering of the cofactor specificity of Rossmann-fold proteins

Installation

pip install rossmann-toolbox

Alternatively, to get the most recent changes, install directly from the repository:

pip install git+https://github.com/labstructbioinf/rossmann-toolbox.git

For some of the features additional dependencies are required:

Package	Sequence variant	Structure variant
FoldX4	-	required
DSSP3	-	required
HH-suite3	optional	optional

Getting started

Sequence-based approach

The input is a full-length sequence. The algorithm first detects Rossmann cores (i.e. the β-α-β motifs that interact with the cofactor) in the sequence and later evaluates their cofactor specificity:

from rossmann_toolbox import RossmannToolbox
rtb = RossmannToolbox(use_gpu=True)

data = {'3m6i_A': 'MASSASKTNIGVFTNPQHDLWISEASPSLESVQKGEELKEGEVTVAVRSTGICGSDVHFWKHGCIGPMIVECDHVLGHESAGEVIAVHPSVKSIKVGDRVAIEPQVICNACEPCLTGRYNGCERVDFLSTPPVPGLLRRYVNHPAVWCHKIGNMSYENGAMLEPLSVALAGLQRAGVRLGDPVLICGAGPIGLITMLCAKAAGACPLVITDIDEGRLKFAKEICPEVVTHKVERLSAEESAKKIVESFGGIEPAVALECTGVESSIAAAIWAVKFGGKVFVIGVGKNEIQIPFMRASVREVDLQFQYRYCNTWPRAIRLVENGLVDLTRLVTHRFPLEDALKAFETASDPKTGAIKVQIQSLE'}

preds = rtb.predict(data, mode='seq')
preds = {'3m6i_A': {'FAD': 0.0008955444,
                    'NAD': 0.998446,
                    'NADP': 0.00015508455,
                    'SAM': 0.0002544397, ...}}

Structure-based approach

The input is a protein structure. Preparation steps are the same as above, but additionally, structural features are calculated via FOLDX software, and secondary structure features via DSSP

# required binaries
PATH_FOLDX = ...
PATH_HHPRED = ...
PATH_DSSP = ...

path_to_structures = ...  # path to pdb files
chains_to_use = ... # chains to load from `path_to_structures`
rtb = RossmannToolbox(use_gpu=False, foldx_loc = PATH_FOLDX, 
                                     hhsearch_loc = PATH_HHPRED,
                                     dssp_loc = PATH_DSSP)

preds = rtb.predict_structure(path_to_structures, chains_to_use, mode='seq', core_detect_mode='dl')
preds = [{'NAD': 0.99977881,
  'NADP': 0.0018195,
  'SAM': 0.00341983,
  'FAD': 3.62e-05,
  'seq': 'AGVRLGDPVLICGAGPIGLITMLCAKAAGACPLVITDIDEGRL',
  'NAD_std': 0.0003879,
  'NADP_std': 0.00213571,
  'SAM_std': 0.00411747,
  'FAD_std': 3.95e-05}]

What next?

To learn about other features of the rossmann-toolbox, such as visualization of the results, please refer to the notebook examples/example_minimal.ipynb.

Contact

If you have any questions, problems or suggestions, please contact us. The rossmann-toolbox was developed by Kamil Kaminski, Jan Ludwiczak, Maciej Jasinski, Adriana Bukala, Rafal Madaj, Krzysztof Szczepaniak, and Stanislaw Dunin-Horkawicz.

This work was supported by the First TEAM program of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.