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

ABSTRACT: 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 of mutations 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 serve

SUBMITTER: Song Q 

PROVIDER: S-EPMC10320081 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature