Enhancing Protein Structure Prediction With Hybrid Deep Learning Algorithms

The accurate prediction of protein structures is a cornerstone of computational biology, pivotal for understanding biological functions and designing therapeutics. Despite advances, current methods face challenges in balancing prediction accuracy and computational efficiency. This study introduces a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with attention mechanisms to enhance protein structure prediction. We trained our model on a dataset comprising over 20,000 protein structures from the Protein Data Bank (PDB). Our approach achieved a root-mean-square deviation (RMSD) improvement of 17.3% compared to existing models, demonstrating enhanced accuracy. Additionally, the model reduced computational time by 26.5%, addressing a critical efficiency bottleneck. Comprehensive validation performed on independent datasets confirmed the robustness of our framework, with statistical significance established at p < 0.01. The findings suggest that hybrid models combining CNNs and attention mechanisms offer a promising avenue for advancing protein structure prediction. Future work will explore integration with larger biological datasets and the application of transfer learning to extend model capabilities.