Prediction of Plant Lipocalin Genes based on Convolutional Neural Networks

Lipocalins play a key role in regulating biological functions such as modulation of cell growth and metabolism, binding of cell-surface receptors, nerve growth and regeneration, and regulating of immune responses. Identifying and analyzing plant lipocalins has become one of the important issues in the study of lipocalin family. Traditional methods such as protein structure analysis, cell localization and phylogenetic studies are complex and very expensive, which makes current exploration progress of plant lipocalins still slow compared with deep learning methods. In this paper, based on convolutional neural network, we constructed a deep learning model called 'LCNet', which has sensitivity and specificity for plant lipocalin genes of 0.953 and 0.941 respectively. In addition, we further verified the prediction performance of LCNet model by studying the similarities and differences of gene relative expression levels between lipocalin genes already identified biologically in Oryza and the genes predicted as Oryza lipocalin by LCNet model during the process of absorbing and transporting PCB18. This combination of deep learning and biological experiments has high precision, simple operation and low cost, which can reduce the workload of biologists and can be extended to other proteins to solve similar problems.