wKinMut-2: Identification and Interpretation of Pathogenic Variants in Human Protein Kinases

Vázquez, Miguél; Pons, Tirso; Brunak, Søren; Valencia, Alfonso; Izarzugaza, Jose M. G.

doi:10.1002/humu.22914

Cited by 10 publications

(5 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several databases and annotation tools are specific for certain genes, protein domains, or regions, for example, primary immunodeficiency‐causing genes [Piirilä et al., ; Samarghitean et al., ; Ortutay and Vihinen, ], MMR genes [Thompson et al., ], protein kinase domain [Stenberg et al., ; Ortutay et al., ; Vazquez et al., ], phosphorylation sites [Safaei et al., ; Hornbeck et al., ], and many others. Specific predictors are available for variations in MMR genes [Chao et al., ; Ali et al., ; Thompson et al., ; Thompson et al., ; Niroula and Vihinen, ], the cystic fibrosis transmembrane conductance regulator protein [Masica et al., ], cytochrome P450 enzymes [Fechter and Porollo, ], hypertrophic cardiomyopathy‐related proteins [Jordan et al., ], protein kinase domains [Torkamani and Schork, ; Väliaho et al., ; Vazquez et al., ], phosphorylation sites [Wagih et al., ], signal peptides [Hon et al., ], and others (Supp. Table S1).…”

Section: Types Of Toolsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

View full text Add to dashboard Cite

Next-generation sequencing methods have revolutionized the speed of generating variation information. Sequence data have a plethora of applications and will increasingly be used for disease diagnosis. Interpretation of the identified variants is usually not possible with experimental methods. This has caused a bottleneck that many computational methods aim at addressing. Fast and efficient methods for explaining the significance and mechanisms of detected variants are required for efficient precision/personalized medicine. Computational prediction methods have been developed in three areas to address the issue. There are generic tolerance (pathogenicity) predictors for filtering harmful variants. Gene/protein/disease-specific tools are available for some applications. Mechanism and effect-specific computer programs aim at explaining the consequences of variations. Here, we discuss the different types of predictors and their applications. We review available variation databases and prediction methods useful for variation interpretation. We discuss how the performance of methods is assessed and summarize existing assessment studies. A brief introduction is provided to the principles of the methods developed for variation interpretation as well as guidelines for how to choose the optimal tools and where the field is heading in the future.

show abstract

Section: Types Of Toolsmentioning

confidence: 99%

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

2016

View full text Add to dashboard Cite

show abstract

“…KinMutRF is publicly implemented as a component of our pipeline for the identification, annotation and interpretation of the consequences of kinase variants, wKinMut-2 [ 61 ]. This resource is freely available at http://kinmut2.bioinfo.cnio.es .…”

Section: Discussionmentioning

confidence: 99%

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

et al. 2016

Self Cite

View full text Add to dashboard Cite

BackgroundThe 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.ResultsKinMutRF 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.ConclusionsKinMutRF 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2723-1) contains supplementary material, which is available to authorized users.

show abstract

“…Average and standard deviation are also shown and no bias was identified. Here we compare the performance of the best predictive model of Kinact with widely used tools to study the effects of mutations in proteins functions PolyPhen2 ( 42 ), SIFT ( 12 ) and wKinMut2 ( 49 ), a tool to identify and interpret pathogenic variants in human protein kinases.…”

Section: Validationmentioning

confidence: 99%

Kinact: a computational approach for predicting activating missense mutations in protein kinases

Rodrigues

Ascher

Pires

2018

Nucleic Acids Research

View full text Add to dashboard Cite

Protein phosphorylation is tightly regulated due to its vital role in many cellular processes. While gain of function mutations leading to constitutive activation of protein kinases are known to be driver events of many cancers, the identification of these mutations has proven challenging. Here we present Kinact, a novel machine learning approach for predicting kinase activating missense mutations using information from sequence and structure. By adapting our graph-based signatures, Kinact represents both structural and sequence information, which are used as evidence to train predictive models. We show the combination of structural and sequence features significantly improved the overall accuracy compared to considering either primary or tertiary structure alone, highlighting their complementarity. Kinact achieved a precision of 87% and 94% and Area Under ROC Curve of 0.89 and 0.92 on 10-fold cross-validation, and on blind tests, respectively, outperforming well established tools (P < 0.01). We further show that Kinact performs equally well on homology models built using templates with sequence identity as low as 33%. Kinact is freely available as a user-friendly web server at http://biosig.unimelb.edu.au/kinact/.

show abstract

wKinMut-2: Identification and Interpretation of Pathogenic Variants in Human Protein Kinases

Cited by 10 publications

References 42 publications

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

Variation Interpretation Predictors: Principles, Types, Performance, and Choice

KinMutRF: a random forest classifier of sequence variants in the human protein kinase superfamily

Kinact: a computational approach for predicting activating missense mutations in protein kinases

Contact Info

Product

Resources

About