The SSB computational toolkit was developed to easily predict classical pharmacodynamic models of drug-GPCR (class A) interactions given just as input structural information of the receptor and the ligand. The toolkit doesn’t use any novel or untested methods. Instead, it brings together free and/or open source bioinformatic tools into a user-friendly pipeline to be used by experts and non-experts. The pipeline was built, as a first instance, in a jupyter notebook - an interactive computational environment for replication and exploration of scientific code and analysis. Nowadays, jupyter notebooks are being extensively used by the computational biology community, making them the preferred choice to share and rerun computational protocols.

• The SSB toolkit explained
  
  

pip install ssbtoolkit

1. Simulation of dose-response curves of agonists using affinity values
2. Simulation of dose-response curves of antagonists using affinity values
3. Simulation of dose-response curves of agonists using kenetic values
4. Simulation of dose-response curves of agonists using data acquired with tauRAMD
5. Exploring SSB pathways associated to disease variants
   
   

Documentation can be found online on ReadTheDocs.

If you use or adapt the SSBtoolkit for your own research projects please cite us.

@article{ribeiro_ssb_2022,
    title={{SSB} toolkit: from molecular structure to subcellular signaling pathways.},
    author={Ribeiro, Rui Pedro and Gossen, Jonas and Rossetti, Giulia and Giorgetti, Alejandro},
    publisher={bioRxiv},
    url={https://www.biorxiv.org/content/10.1101/2022.11.08.515595v1},
    doi={10.1101/2022.11.08.515595},
    year={2022}
}

EU Human Brain Project (SGA1 and SGA2): This open source software was developed in part in the Human Brain Project funded from the European Union's Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No 720270 and No. 78907 (Human Brain Project SGA1 and SGA2).