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Updated: Jun 28, 2026

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
Published on: December 7, 2014
MoKCa database--mutations of kinases in cancer
Christopher J Richardson1, Qiong Gao, Costas Mitsopoulous
1Section of Structural Biology, Institute of Cancer Research, Chester Beatty Laboratories, 237 Fulham Road, London SW3 6JB, UK.
Abstract:
Members of the protein kinase family are amongst the most commonly mutated genes in human cancer, and both mutated and activated protein kinases have proved to be tractable targets for the development of new anticancer therapies The MoKCa database (Mutations of Kinases in Cancer, http://strubiol.icr.ac.uk/extra/mokca) has been developed to structurally and functionally annotate, and where possible predict, the phenotypic consequences of mutations in protein kinases implicated in cancer. Somatic mutation data from tumours and tumour cell lines have been mapped onto the crystal structures of the affected protein domains. Positions of the mutated amino-acids are highlighted on a sequence-based domain pictogram, as well as a 3D-image of the protein structure, and in a molecular graphics package, integrated for interactive viewing. The data associated with each mutation is presented in the Web interface, along with expert annotation of the detailed molecular functional implications of the mutation. Proteins are linked to functional annotation resources and are annotated with structural and functional features such as domains and phosphorylation sites. MoKCa aims to provide assessments available from multiple sources and algorithms for each potential cancer-associated mutation, and present these together in a consistent and coherent fashion to facilitate authoritative annotation by cancer biologists and structural biologists, directly involved in the generation and analysis of new mutational data.
Insights
The MoKCa database structurally annotates cancer-associated protein kinase mutations. This resource aids researchers in understanding mutation consequences for developing targeted cancer therapies.
Area of Science:
- Biochemistry
- Genetics
- Oncology
Background:
- Protein kinases are frequently mutated in human cancers.
- Mutated and activated protein kinases are key targets for anticancer drug development.
Purpose of the Study:
- To develop the MoKCa database for structural and functional annotation of protein kinase mutations in cancer.
- To predict phenotypic consequences of these mutations.
Main Methods:
- Somatic mutation data from tumors and cell lines were mapped onto protein crystal structures.
- Mutated amino acid positions were visualized on sequence-based pictograms and 3D structures.
- Interactive molecular graphics were integrated for viewing mutation data.
Main Results:
- The MoKCa database provides expert annotations on the functional implications of mutations.
- Proteins are linked to functional resources and annotated with structural features like domains and phosphorylation sites.
- The database integrates assessments from multiple sources for cancer-associated mutations.
Conclusions:
- MoKCa facilitates authoritative annotation of cancer-related mutations for biologists.
- The database aids in understanding the molecular and functional impact of kinase mutations in cancer.
- It supports the generation and analysis of new mutational data for cancer research.
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