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- W2007447370 abstract "Many cellular processes are impacted by signaling through receptor and non-receptor kinase proteins. These include such diverse cellular actions as proliferation, differentiation, and motility, as well as tissue level phenomena such as angiogenesis and development. This important role in the cell is reflected also in the relative overrepresentation of kinases among known cancer mutations to proteins. In order to better understand the functional effects of these mutations, we have developed computational methods that seek to predict the effect of point mutations on kinase activation. By predicting whether a given mutation causes a kinase to be more active, we can gain insight into the overall impact of the mutation on cell phenotype and give insight to clinicians on patient cohorting for efficacious treatment with targeted kinase inhibitors. We have developed two separate but complementary methods to predict kinase activation status. The first uses molecular dynamics (MD) simulations and scoring criteria to predict if a mutation preferentially stabilizes the protein's active state. As a complimentary approach to MD, we have developed machine learning techniques that utilize the method known as support vector machines to predict whether mutations in a large number of kinases (>450) are activating. This method has proven to be almost as effective at predicting activation mutations as the mechanistic picture gained from MD simulations. We think these methods are both broadly applicable and have the potential to greatly impact both our understanding of mechanisms of kinase activation as well as to guide best practices in the clinical setting of targeted therapy in cancer treatment." @default.
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- W2007447370 date "2015-01-01" @default.
- W2007447370 modified "2023-09-27" @default.
- W2007447370 title "Predicting the Effects of Clinically Observed Kinase Mutations using Molecular Modeling and Machine Learning Algorithms" @default.
- W2007447370 doi "https://doi.org/10.1016/j.bpj.2014.11.2063" @default.
- W2007447370 hasPublicationYear "2015" @default.
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