title: KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily.
abstract: The association between aberrant signal processing by protein kinases and human diseases such as cancer was established long time ago. However, understanding the link between sequence variants in the protein kinase superfamily and the mechanistic complex traits at the molecular level remains challenging: cells tolerate most genomic alterations and only a minor fraction disrupt molecular function sufficiently and drive disease.
  
  
  
  KinMutRF is a novel random-forest method to automatically identify pathogenic variants in human kinases. Twenty six decision trees implemented as a random forest ponder a battery of features that characterize the variants: a) at the gene level, including membership to a Kinbase group and Gene Ontology terms; b) at the PFAM domain level; and c) at the residue level, the types of amino acids involved, changes in biochemical properties, functional annotations from UniProt, Phospho.ELM and FireDB. KinMutRF identifies disease-associated variants satisfactorily (Acc: 0.88, Prec:0.82, Rec:0.75, F-score:0.78, MCC:0.68) when trained and cross-validated with the 3689 human kinase variants from UniProt that have been annotated as neutral or pathogenic. All unclassified variants were excluded from the training set. Furthermore, KinMutRF is discussed with respect to two independent kinase-specific sets of mutations no included in the training and testing, Kin-Driver (643 variants) and Pon-BTK (1495 variants). Moreover, we provide predictions for the 848 protein kinase variants in UniProt that remained unclassified. A public implementation of KinMutRF, including documentation and examples, is available online ( http://kinmut2.bioinfo.cnio.es ). The source code for local installation is released under a GPL version 3 license, and can be downloaded from https://github.com/Rbbt-Workflows/KinMut2 .
  
  
  
  KinMutRF is capable of classifying kinase variation with good performance. Predictions by KinMutRF compare favorably in a benchmark with other state-of-the-art methods (i.e. SIFT, Polyphen-2, MutationAssesor, MutationTaster, LRT, CADD, FATHMM, and VEST). Kinase-specific features rank as the most elucidatory in terms of information gain and are likely the improvement in prediction performance. This advocates for the development of family-specific classifiers able to exploit the discriminatory power of features unique to individual protein families.
authors.affiliation: Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
authors.forename: Tirso
authors.initials: T
authors.lastname: Pons
authors.affiliation: Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
authors.forename: Miguel
authors.initials: M
authors.lastname: Vazquez
authors.affiliation: Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark.
authors.forename: María Luisa
authors.initials: ML
authors.lastname: Matey-Hernandez
authors.affiliation: Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark.|Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3A, 2200, Copenhagen, Denmark.
authors.forename: Søren
authors.initials: S
authors.lastname: Brunak
authors.affiliation: Structural Biology and BioComputing Programme, Spanish National Cancer Research Centre (CNIO), Melchor Fernández Almagro, 3, 28029, Madrid, Spain.
authors.forename: Alfonso
authors.initials: A
authors.lastname: Valencia
authors.affiliation: Center for Biological Sequence Analysis (CBS), Systems Biology Department, Technical University of Denmark (DTU), Kemitorvet, Building 208, 2800 Kgs., Lyngby, Denmark. josemgizarzugaza@cbs.dtu.dk.
authors.forename: Jose Mg
authors.initials: JM
authors.lastname: Izarzugaza
mesh_terms: D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D003663:Decision Trees; D014644:Genetic Variation; D006801:Humans; D009154:Mutation; D011494:Protein Kinases / Q000235:genetics*; D012984:Software
chemical_list: D011494:Protein Kinases
keywords: Functional impact; Pathogenicity prediction; Protein kinases; Variant prioritization; X-linked agammaglobulinemia