Sequence and Structure-Based Analysis of Specificity Determinants in Eukaryotic Protein Kinases.


Journal

Cell reports
ISSN: 2211-1247
Titre abrégé: Cell Rep
Pays: United States
ID NLM: 101573691

Informations de publication

Date de publication:
12 01 2021
Historique:
received: 20 08 2018
revised: 03 11 2020
accepted: 14 12 2020
entrez: 13 1 2021
pubmed: 14 1 2021
medline: 5 1 2022
Statut: ppublish

Résumé

Protein kinases lie at the heart of cell-signaling processes and are often mutated in disease. Kinase target recognition at the active site is in part determined by a few amino acids around the phosphoacceptor residue. However, relatively little is known about how most preferences are encoded in the kinase sequence or how these preferences evolved. Here, we used alignment-based approaches to predict 30 specificity-determining residues (SDRs) for 16 preferences. These were studied with structural models and were validated by activity assays of mutant kinases. Cancer mutation data revealed that kinase SDRs are mutated more frequently than catalytic residues. We have observed that, throughout evolution, kinase specificity has been strongly conserved across orthologs but can diverge after gene duplication, as illustrated by the G protein-coupled receptor kinase family. The identified SDRs can be used to predict kinase specificity from sequence and aid in the interpretation of evolutionary or disease-related genomic variants.

Identifiants

pubmed: 33440154
pii: S2211-1247(20)31591-6
doi: 10.1016/j.celrep.2020.108602
pmc: PMC7809594
pii:
doi:

Substances chimiques

Protein Kinases EC 2.7.-

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

108602

Subventions

Organisme : Biotechnology and Biological Sciences Research Council
Pays : United Kingdom

Informations de copyright

Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Interests The authors declare no competing interests.

Auteurs

David Bradley (D)

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK.

Cristina Viéitez (C)

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK; European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany.

Vinothini Rajeeve (V)

Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.

Joel Selkrig (J)

European Molecular Biology Laboratory (EMBL), Genome Biology Unit, 69117 Heidelberg, Germany.

Pedro R Cutillas (PR)

Integrative Cell Signalling & Proteomics, Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK. Electronic address: p.cutillas@qmul.ac.uk.

Pedro Beltrao (P)

European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK. Electronic address: pbeltrao@ebi.ac.uk.

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Classifications MeSH