Identification of Protein Structure Using Hybrid Deep Learning Model

Proteins are the basic building blocks of a body that serve a variety of important roles, including those of structural and building materials, energy sources, and muscular contraction. Amino acids are the basic building blocks or molecules of proteins. Knowing the protein structure can help in understanding the functions of the compound which in turn helps in medical or bioinformatics field to create the specific type of drug or enzyme. The identification of protein secondary structure is important as it is an information bridge between the primary and tertiary structures and also helps in analysis of protein folding. To address this, we propose a novel hybrid deep learning model that combines Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The model leverages the feature extraction capabilities of CNNs and the temporal dependencies captured by BiLSTM networks to accurately predict protein secondary structure. We evaluate the model using a comprehensive dataset and showcase its superior performance compared to existing approaches. Obtained findings highlight the effectiveness of the hybrid CNN-BiLSTM model in protein structure prediction, further advancing our understanding of protein function and aiding in drug discovery and enzyme design applications.