Protein secondary structure prediction based on LSTM neural network approach

Proteins are crucial for maintaining cellular, organ, and tissue structure and function in the body, predicting protein structure and function from sequence remains challenging. Computational methods are essential for predicting protein properties. In Vietnam, protein technology and bioinformatics have gained attention, especially during the Covid-19 pandemic. This study presents a deep learning approach using a four-layer LSTM model and protein datasets to predict protein secondary structure. The proposed method incorporates RPCA to reduce dimensionality, eliminate errors and outliers, and enhance machine learning model effectiveness. The accuracy of the LSTM model is 88.73\%, surpassing modern methods. While limitations and challenges exist, this research contributes to organizing knowledge and building an experimental program for protein function prediction. The proposed method provides a valuable tool for accurately predicting protein function based on secondary structure. Future studies using deep learning, projected protein sequences and structures hold promise for further advancements in protein function prediction.