DeepAlloDriver: a deep learning-based strategy to predict cancer driver mutations

Driver mutations can contribute to the initial processes of cancer, and their identification is crucial for understanding tumorigenesis as well as for molecular drug discovery and development. Allostery regulates protein function away from the functional regions at an allosteric site. In addition to the known effects ofmutations around functional sites, mutations at allosteric sites have been associated with protein structure, dynamics, and energy communication. As a result, identifying driver mutations at allosteric sites will be beneficial for deciphering the mechanisms of cancer and developing allosteric drugs. In this study, we provided a platform called DeepAlloDriver to predict driver mutations using a deep learning method that exhibited >93% accuracy and precision. Using this server, we found that a missense mutation in RRAS2 (Gln72 to Leu) might serve as an allosteric driver of tumorigenesis, revealing the mechanism of the mutation in knock-in mice and cancer patients. Overall, DeepAlloDriver would facilitate the elucidation of the mechanisms underlying cancer progression and help prioritize cancer therapeutic targets. The web server is freely available at: https://mdl.shsmu.edu.cn/DeepAlloDriver. [GRAPHICS] .

Automated summary

Driver mutations play a crucial role in cancer initiation and their identification is important for understanding tumorigenesis and drug development. Mutations at allosteric sites have been associated with protein structure and dynamics. DeepAlloDriver, a deep learning platform, achieves >93% accuracy and precision in predicting driver mutations, and it identified a missense mutation in RRAS2 as a potential allosteric driver of tumorigenesis. This research provides insights into cancer mechanisms and aids in prioritizing therapeutic targets.