wKinMut-2: Identification and Interpretation of Pathogenic Variants in Human Protein Kinases
Abstract
Most genomic alterations are tolerated while only a minor fraction disrupts molecular function sufficiently to drive disease. Protein kinases play a central biological function and the functional consequences of their variants are abundantly characterized. However, this heterogeneous information is often scattered across different sources, which makes the integrative analysis complex and laborious. wKinMut-2 constitutes a solution to facilitate the interpretation of the consequences of human protein kinase variation. Nine methods predict their pathogenicity, including a kinase-specific random forest approach. To understand the biological mechanisms causative of human diseases and cancer, information from pertinent reference knowledgebases and the literature is automatically mined, digested and homogenized. Variants are visualized in their structural contexts and residues affecting catalytic and drug-binding are identified. Known protein-protein interactions are reported. Altogether, this information is intended to assist the generation of new working hypothesis to be corroborated with ulterior experimental work. The wKinMut-2 system, along with a user manual and examples is freely accessible at http://kinmut2.bioinfo.cnio.es, the code for local installations at https://github.com/Rbbt-Workflows/KinMut2.
Info
Journal Article, 2016
UN SDG Classification
DK Main Research Area
Science/Technology
