Deep Learning based Recognition of Auto Covariance Signatures through Protein Interaction Model for Amino Acid Sequence

The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to improve the accuracy of protein interaction prediction. Auto-covariance signatures are employed to capture the positional relationships between amino acids and protein sequences, facilitating the extraction of structural and functional features. CNNs are used to automatically extract both short-and long-range dependencies, while RNNs handle temporal patterns. The model incorporates self-supervised learning to handle unlabeled data, improving generalization. With a benchmark dataset, the system achieved an impressive accuracy of 98.9%, outperforming existing methods in protein interaction prediction. The approach also highlights the importance of optimization techniques in managing protein sequence structures and functions effectively.