Deep Learning Ensemble Models for Enhanced Protein Secondary Structure Prediction: A Review

This narrative review examines the role of ensemble deep learning models in advancing protein secondary structure prediction (SSSP), a critical task in computational biology that informs protein function, drug design, and therapeutic development. The primary objective is to synthesize current knowledge on how ensemble methods, integrating architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, enhance prediction accuracy and robustness compared to single-model approaches. Key themes include the evolution of deep learning in SSSP, the application of ensemble techniques such as bagging and stacking, and the integration of multi-modal data like position-specific scoring matrices and contact maps. The review highlights the superior performance of models like DeepCNF and SPOT-۱D, which leverage diverse data and advanced techniques like transfer learning and attention mechanisms to achieve Q۳ accuracies up to ۸۹%. Critical reflections underscore challenges in generalizability to novel proteins, computational complexity, and limited interpretability, emphasizing the need for scalable and transparent frameworks. Emerging trends, including neural architecture search and self-supervised learning, signal a shift toward adaptable and interpretable models. The review concludes that ensemble deep learning significantly advances SSSP by addressing data complexity and overfitting, offering a foundation for future innovations in bioinformatics and structural biology applications.

Keywords:

Ensemble deep learning , Multi-modal data integration , Protein secondary structure prediction , Transfer learning , Transformer architectures