Protein stability is crucial to the function of biological systems, as it determines the structural integrity and, ultimately, the functionality of proteins under physiological conditions. Point mutations, specifically missense variants, can significantly alter protein stability, potentially leading to loss of function, gain of harmful activities, or structural fragility. Precise prediction of the impact of mutations on protein stability is essential for understanding disease mechanisms and advancing therapeutic interventions.
In this study, we explore the application of quantum computing to enhance predictive accuracy for mutation-induced stability changes. We employ a quantum support vector machine (Quantum SVM) on missense variants annotated in UniProtKB/Swiss-Prot human entries. Quantum computing offers substantial advantages over classical approaches, including the ability to process complex, high-dimensional data more efficiently. Quantum algorithms, by exploiting superposition and entanglement, have the potential to outperform classical machine learning models in tasks requiring extensive computation, such as the analysis of molecular interactions and mutation effects on protein stability.
By benchmarking Quantum SVM against classical models, we aim to assess the viability of quantum-enhanced algorithms in bioinformatics applications, with a particular focus on the predictive analysis of missense mutations. This work represents an innovative approach to bridging quantum computing and molecular biology, with the potential to revolutionize the accuracy and scalability of computational tools in genomics and personalized medicine.