PredictSNP Onco

This is the landing page of the new tool PredictSNPOnco to be released in Q2 2021.

Cancer is a generic term for a large group of diseases that can affect any part of the body. One of the defining features of cancer is the rapid formation of abnormal cells that grow beyond their usual boundaries [1].

When a patient is diagnosed with cancer, treatment can be a race against time. A generic treatment, proven to work, is preferred to slower personalized treatment. Given the differences in the various types of cancer, the treatment approach “one size fits all” has clear limitations. Therefore, methods enabling personalized medicine have been in use, typically involving sequencing of patients’ DNA and analysis of identified mutations [2].

PredictSNPOnco [3] is the web tool for fully automated and fast analysis of the effect of mutations on stability and function in known cancer targets applying in silico methods of molecular modelling and bioinformatics (Scheme).

Our workflow employes calculations realized by in house as well as established third-party tools: MakeMultimer [4], I-Tasser [5], FoldX [6], Rosetta [7], PIC [8], HOPE [9], PredictSNP[10], P2Rank [11], PropKa [12], H++ [13]. The impact of mutations on the binding of all FDA-approved drugs is investigated using the tools AutoDock [14] and CaverDock [15]. The results of these calculations will be complemented by the data retrieved from the publicly accessible databases: M-CSA [16] and Swiss-Prot [17]. Calculations will be completed in a timeframe shorter than two weeks using a dataset from ZINC [18].



Scheme: Computational workflow of the software tool PredictSNPOnco analyzing mutations obtained by the sequencing DNA of oncology patients.

References

  1. Jones, P.A., Baylin, S.B., 2007: The Epigenomics of Cancer. Cell 128, 4: 683-692.
  2. Pinto, G.P., Hendrikse, N.M., Stourac, J., Damborsky, J., Bednar, D., 2021: Virtual Screening of Potential Anticancer Drugs based on Microbial Products. Seminars in Cancer Biology (submitted).
  3. Pinto, G.P., Stourac, J., Dobias, A., Slaby, O., Noskova, H., Sterba, J., Damborsky, J., Bednar, D., 2021: PredictSNPOnco: Strategy for Fast and Fully Automated Analysis of Protein Mutations Carried by Oncology Patients (under preparation).
  4. http://watcut.uwaterloo.ca/tools/makemultimer/
  5. Roy A., Kucukural, A., Zhang, Y., 2010: I-TASSER: a unified platform for automated protein structure and function prediction. Nature Protocols 5 : 725–738.
  6. Schymkowitz, J., Borg, J., Stricher, F., Nys, R., Rousseau, F., Serrano, L., 2005: The FoldX web server: an online force field. Nucleic Acids Research 33 : W382–W388.
  7. Song, Y., DiMaio, F., Wang, R.Y.R., Kim, D., Miles, C., Brunette, T.J. and Baker, D., 2013: High-resolution comparative modeling with RosettaCM. Structure 21 : 1735–1742.
  8. Tina, K.G., Bhadra, R., Srinivasan, N., 2007: PIC: Protein Interactions Calculator. Nucleic Acids Research 35: W473–W476.
  9. Venselaar, H., te Beek, T.A., Kuipers, R.K., Hekkelman, M.L., Vriend, G., 2010: Protein structure analysis of mutations causing inheritable diseases. An e-Science approach with life scientist Friendly interfaces. BMC Bioinformatics 11 : 548–558.
  10. Bendl, J., Stourac, J., Salanda, O., Pavelka, A., Wieben, E.D., Zendulka, J., Brezovsky, J., Damborsky, J., 2014: PredictSNP: Robust and Accurate Consensus Classifier for Prediction of Disease-Related Mutations. PLOS Computational Biology 10: e1003440.
  11. Krivak, R., Hoksza, 2018: D., P2Rank: machine learning based tool for rapid and accurate prediction of ligand binding sites from protein structure. Journal of Cheminformatics 10 : 39.
  12. Olsson, M.H.M., Søndergaard, C.R., Rostkowski, M., Jensen, J.H., 2011: PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. Journal of Chemical Theory and Computation 2: 525-537.
  13. Anandakrishnan, R., Aguilar, B., Onufriev A.V., 2012: H++ 3.0: automating pK prediction and the preparation of biomolecular structures for atomistic molecular modeling and simulations. Nucleic Acids Research 40 : W537–W541.
  14. Trott, O., Olson, A.J., 2010: AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization and Multithreading. Journal of Computational Chemistry 31: 455-461.
  15. Filipovic, J., Vavra, O., Plhak, J., Bednar, D., Marques, S. M., Brezovsky, J., Matyska, L., Damborsky, J., 2019: CaverDock: A Novel Method for the Fast Analysis of Ligand Transport. IEEE/ACM Transactions on Computational Biology and Bioinformatics 17: 1625-1638.
  16. Ribeiro, A.J.M., Holliday, G.L., Furnham, N., Tyzack, J.D., Ferris, K., Thornton, J.M., 2018: Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites. Nucleic Acids Research 46: D618-D623.
  17. Bienert, S., Waterhouse, A., de Beer, T.A.P., Tauriello, G., Studer, G., Bordoli, L., Schwede, T., 2017: The SWISS-MODEL Repository - new features and functionality. Nucleic Acids Research 45, D313-D319.
  18. Irwin, J.J., Tang, K.G., Young, J., Dandarchuluun, C., Wong, B.R., Khurelbaatar, M., Moroz, Y.S., Mayfield, J., Sayle, R.A., 2020: ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discovery. J. Chem. Inf. Model. 12 : 6065–6073.

Acknowledgements

The authors would like to express their thanks to the Czech Ministry of Education, the Grant Agency of the Czech Republic and the Technology Agency of the Czech Republic. This project has received funding from the European Union’s Horizon 2020 research and Innovation programme.