Research Projects

Plant pathogens can cause food security and economic losses due to their ability to multiply and spread within plant tissue. To combat this, plants have evolved disease resistance proteins (R proteins) to identify invading pathogens. Wheat, which is heavily treated with fungicides, may not always have access to chemical controls due to financial constraints or excessive regulation. To address this, wheat resistance genes (R genes) can be used to detect and control major diseases. Identifying R-genes in wild species and their relatives is challenging and time-consuming. Several methods have been developed for R protein prediction, but these are not suitable for finding R-genes in landraces and near relatives of crops. Therefore, there is a need to explore different Deep Learning (DL) and feature descriptors to identify and classify disease resistance proteins. Currently, there is no approach or technology for predicting different wheat-specific R proteins with a single platform. To address this, a wheat-specific Deep Learning based model is proposed, which will be applied to a web server for predicting R proteins. The number of experimentally discovered R proteins is increasing, and a comprehensive database of experimentally verified and computationally predicted sites for R proteins in wheat is also aimed at. This species-specific prediction model will help develop a comprehensive understanding of disease susceptibility and resistance in crop types to pests and pathogens.