Integrating Deep Learning with Structural Bioinformatics using Next-Generation Protein Stability Prediction

Protein stability refers to the propensity of a protein molecule to maintain its native folded structure under various environmental conditions. Understanding protein stability is crucial as it directly influences protein function, activity, and interactions within biological systems. Accurate prediction of protein stability is therefore essential for elucidating protein structure-function relationships, guiding protein engineering efforts, and facilitating drug discovery processes. The integration of deep learning methodologies with structural bioinformatics presents a promising avenue for advancing next-generation protein stability prediction systems. This research introduces a novel algorithm, DeepCartesianDDG (DCD), which combines the predictive power of DeepVariantNet with the structural insights provided by the Rosetta 'cartesian_ddg' protocol. Through extensive simulation analysis, the proposed algorithm's performance is evaluated against existing algorithms commonly employed in protein stability prediction. Key simulation metrics including accuracy, precision, recall, and F1-score are utilized for comparison. The results demonstrate that DCD outperforms traditional algorithms, showcasing its efficacy in accurately estimating protein stability values. This research underscores the potential of integrating deep learning techniques with structural bioinformatics for enhancing protein stability prediction, thereby paving the way for advancements in protein engineering and drug discovery.