DeepAProt: Deep learning based abiotic stress protein sequence classification and identification tool in cereals - Ahmed, Bulbul; Haque, Md Ashraful; Iquebal, Mir Asif; Jaiswal, Sarika; Angadi, U. B.; Kumar, Dinesh; Rai, Anil (Frontiers in Plant Science, 2023)
The impact of climate change has been alarming for the crop growth. The extreme weather conditions can stress the crops and reduce the yield of major crops belonging to Poaceae family too, that sustains 50% of the world’s food calorie and 20% of protein intake. Computational approaches, such as artificial intelligence-based techniques have become the forefront of prediction-based data interpretation and plant stress responses. In this study, we proposed a novel activation function, namely, Gaussian Error Linear Unit with Sigmoid (SIELU) which was implemented in the development of a Deep Learning (DL) model along with other hyper parameters for classification of unknown abiotic stress protein sequences from crops of Poaceae family. To develop DL models, we used public domain data pertaining to four different abiotic stress responsive proteins for classification of unknown proteins of the crops belonging to same family. It was observed that efficiency of the DL models with our proposed novel SIELU activation function outperformed the models with other activation function (GeLU) as well as models like SVM and RF with 95.11%, 80.78%, 94.97%, and 81.69% accuracy for cold, drought, heat and salinity, respectively. Also, a web-based tool, named DeepAProt (http://login1.cabgrid.res.in:5500/) has been developed using flask API. Further, a mobile app of the same was also been developed and can be downloaded by the users from the server. This server/App will provide researchers a tool for which will be useful in rapid and economical identification of proteins for abiotic stress management in crops Poaceae family, in endeavour of higher production for food security and combating hunger, ensuring UN SDG goal 2.0.