Deep learning architectures achieve state-of-the-art (SOTA) accuracy in protein secondary structure prediction.

Protein secondary structure prediction represents an important intermediate step between a protein's linear amino acid sequence and its three-dimensional structure, with broad implications for synthetic biology, drug development, and disease research. Although experimental techniques such as X-ray crystallography provide highly accurate structural information, they are labor-intensive, time-consuming, and costly, which has motivated the development of computational alternatives. Early machine-learning approaches to this problem were limited in their ability to capture complex sequence-structure relationships. The introduction of convolutional and recurrent neural networks improved hierarchical feature extraction, and predictive performance advanced further with transformer-based architectures such as AlphaFold2. This review outlines recent advances in hybrid model design, benchmark datasets, and evaluation metrics for protein secondary structure prediction. We also discuss current methodological limitations, including data dependency and dataset bias, and outline future directions such as cross-species validation, uncertainty-aware modeling, and the still-emerging potential of incorporating heterogeneous biological data into next-generation PSSP frameworks.