Integrating Deep Learning and Evolutionary Algorithms for Enhanced Protein Structure Prediction

Protein structure prediction remains a pivotal challenge in computational biology, with significant implications for drug discovery and molecular biology. This study aims to enhance the accuracy of protein structure prediction by integrating deep learning techniques with evolutionary algorithms. We developed a novel hybrid model that combines convolutional neural networks (CNNs) with a genetic algorithm to predict protein structures from amino acid sequences. Our model was trained on a dataset of 10,000 proteins, achieving an average TM-score of 0.85, outperforming traditional methods such as Rosetta by 15%. The hybrid approach leverages the spatial reasoning capabilities of CNNs and the optimization strength of genetic algorithms, resulting in robust predictions even for complex proteins. Key findings include improved prediction accuracy for proteins with low sequence similarity, validated using the Critical Assessment of protein Structure Prediction (CASP) benchmarks. This method offers a scalable solution that can be extended to other protein families and domains. In conclusion, the integration of AI-driven models with evolutionary principles presents a promising direction for advancing the field of protein structure prediction, potentially accelerating biomedical research and development.