Deep-CABPred: Deep learning model for predicting functional chlorophyll a-b binding proteins in trait-based plant ecology using hybrid embedding with semi-normalized temporal convolutional networks

Chlorophyll a-b binding proteins (CABs) are crucial for photosynthesis, directly influencing plant efficiency and environmental adaptation. Identifying these proteins is vital for understanding ecological function and productivity, but traditional experimental methods are laborious. To overcome this, we developed Deep-CABPred, a novel deep learning model for efficient CAB prediction. Our approach leverages a combination of advanced feature embedding techniques. We extract multi-source protein information from primary sequences, employing FastText for discriminative subword-level patterns and ProtBERT (a protein large language model) for contextualized sequential features. These independently extracted features are then fused into a comprehensive representation. This fused feature set is subsequently integrated into a Semi-Normalized Temporal Convolutional Network (SN-TCN) for model training. Deep-CABPred's performance was rigorously validated using a five-fold cross-validation strategy, achieving impressive accuracies of 88.60 % on the training dataset and 83.68 % on the testing dataset. This model offers an effective computational solution for CAB prediction and holds significant potential for advancing our understanding of plant functional traits, ultimately supporting agricultural and conservation efforts in a changing climate. • Proposed a novel computational model for prediction of Chlorophyll a-b binding proteins • Features are extracted by ProtBERT and FastText • Model is trained by Semi-Normalized Temporal Convolutional Network • Deep-CABPred achieved the highest results compare with other deep learning models