Team:NCTU Formosa/Description
Overview
We established and optimized a powerful system that is simple and interpretable to predict peptides’ function with acceptable accuracy, which is based on the Scoring Card Method (SCM). Instead of heavy computation and complex operation, the SCM analyzes the functions of peptides with mere peptide sequences. Furthermore, we aggregated all the relative data to fulfill the integration of antifungal databases, which built the connection among the data of hosts, pathogens, and corresponding peptides. In addition, we not only constructed the system but also validated our prediction system with the wet lab. Moreover, IoTtalk realized the application to gather weather information in farmland and predicted the possibility of disease occurrence with cloud computing. A completion was done by NCTU_Formosa that carried out the solution of surviving in explosive information in the 21st century – Parabase, exact and fast!
(To check the operation of our whole system, please click Design page.)
Motivation
In the era of explosive information, how do we make the best use of vast data? To find peptides with the right functions from tons of unorganized data both effectively and accurately are such a time-consuming work, not to mention the human error one might commit to confuse the results. The consequence is to randomly select one antifungal peptide without comparison and comprehension. This is a waste of large data. We are getting the quantity while losing the quality.
Typically, a protein function analysis involves complicated calculation including template detection, alignment, or 3D modeling. This year, our blueprint is to design a system for quickly evaluating and analyzing peptide functions, and our first attempt is for fungal diseases in agriculture. Fungal diseases account for two-thirds of plant diseases in Taiwan, which cause huge economic losses. The method broadly used to eliminate fungal diseases is to apply chemical pesticides or to abandon entire farmlands. Our goal is to cure fungal diseases while leaving a system for iGEM and the world to select peptides and do further applications smartly.
