Parabase, a framework for curing different diseases cases by machine learning and big data analysis, consists of Peptide Prediction Model and Disease Occurrence Prediction Model.

     For the peptide prediction, we built a database involving SCM-based antifungal prediction system and connecting all the relative data of the target diseases. After the target has been chosen, the is built by CNN technology to predict the outbreak timing of diseases and connected to IoTtalk that is able to aggregate data in cloud computing.

     The system framework is a convenient tool product that offers users to search target peptides and do the application quickly and wisely. For fungal diseases as the first attempt, we formed an antifungal database that possesses the antifungal prediction system to search for potential antifungal peptides and the search system involving relationships of hosts, pathogens, and corresponding peptides.

     We have chosen several peptides from our database to both validate our system and realize the road to cure fungal diseases.

Predicted Peptides: By Our Antifungal Prediction System

七條

     To validate their antifungal function and also the system, we conducted several fungal experiments.

     (View the filtering process and the experiments conducted in Experiment Design.)

Validated Peptides: by our search system

五條

     1. To realize the mass production in agriculture, we tried to produce them from E.coli.

          (Click the Basic Parts or Composite Partsto veiw our biobricks.)

     2. We also put them into the antifungal prediction system to score their sequences and tried to interpret their structures.

     We took Rs-AFP2 3D structure as an example.

     The region of a peptide become redder when the dipeptide score there is higher. Otherwise, the region become bluer.

     It seemed that the N term of the peptide and the β2 sheet were the reddest. To our antifungal peptide prediction system based on the SCM, it indicated that these two regions were important regions that determined the hull peptide sequence as an antifungal peptides or not.

     To compare with papers, the paper showed that the active site are the β2−β3 loop, from Ala³¹ to Phe⁴⁹ and some activity was found in the N-terminal part of the protein.

     Comparing with the scoring card visualized picture and the real active site, we can find in the picture of score card the 3sheet and the N-termina were also labeled.

     In conclusion, we can say that SCM might possess the ability to show antifungal active sites.

Reference

W. M. M. Schaaper, Synthetic peptides derived from the β2−β3 loop of Raphanus sativus antifungal protein 2 that mimic the active site, http://onlinelibrary.wiley.com/doi/10.1034/j.1399-3011.2001.00842.x/full, 2001