Dataset Information

Multiple consequences of a single amino acid pathogenic RTK mutation: the A391E mutation in FGFR3.

ABSTRACT: The A391E mutation in fibroblast growth factor receptor 3 (FGFR3) is the genetic cause for Crouzon syndrome with Acanthosis Nigricans. Here we investigate the effect of this mutation on FGFR3 activation in HEK 293 T cells over a wide range of fibroblast growth factor 1 concentrations using a physical-chemical approach that deconvolutes the effects of the mutation on dimerization, ligand binding, and efficiency of phosphorylation. It is believed that the mutation increases FGFR3 dimerization, and our results verify this. However, our results also demonstrate that the increase in dimerization is not the sole effect of the mutation, as the mutation also facilitates the phosphorylation of critical tyrosines in the activation loop of FGFR3. The activation of mutant FGFR3 is substantially increased due to a combination of these two effects. The low expression of the mutant, however, attenuates its signaling and may explain the mild phenotype in Crouzon syndrome with Acanthosis Nigricans. The results presented here provide new knowledge about the physical basis behind growth disorders and highlight the fact that a single RTK mutation may affect multiple steps in RTK activation.

SUBMITTER: Chen F

PROVIDER: S-EPMC3577887 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Multiple consequences of a single amino acid pathogenic RTK mutation: the A391E mutation in FGFR3.

Chen Fenghao F Sarabipour Sarvenaz S Hristova Kalina K

PloS one 20130220 2

The A391E mutation in fibroblast growth factor receptor 3 (FGFR3) is the genetic cause for Crouzon syndrome with Acanthosis Nigricans. Here we investigate the effect of this mutation on FGFR3 activation in HEK 293 T cells over a wide range of fibroblast growth factor 1 concentrations using a physical-chemical approach that deconvolutes the effects of the mutation on dimerization, ligand binding, and efficiency of phosphorylation. It is believed that the mutation increases FGFR3 dimerization, and ...[more]

PMID: 23437153

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Project description:MotivationNext Generation Sequencing technologies make it possible to detect rare genetic variants in individual patients. Currently, more than a dozen software and web services have been created to predict the pathogenicity of variants related with changing of amino acid residues. Despite considerable efforts in this area, at the moment there is no ideal method to classify pathogenic and harmless variants, and the assessment of the pathogenicity is often contradictory. In this article, we propose to use peptides structural formulas of proteins as an amino acid residues substitutions description, rather than a single-letter code. This allowed us to investigate the effectiveness of chemoinformatics approach to assess the pathogenicity of variants associated with amino acid substitutions.ResultsThe structure-activity relationships analysis relying on protein-specific data and atom centric substructural multilevel neighborhoods of atoms (MNA) descriptors of molecular fragments appeared to be suitable for predicting the pathogenic effect of single amino acid variants. MNA-based Naïve Bayes classifier algorithm, ClinVar and humsavar data were used for the creation of structure-activity relationships models for 10 proteins. The performance of the models was compared with 11 different predicting tools: 8 individual (SIFT 4G, Polyphen2 HDIV, MutationAssessor, PROVEAN, FATHMM, MVP, LIST-S2, MutPred) and 3 consensus (M-CAP, MetaSVM, MetaLR). The accuracy of MNA-based method varies for the proteins (AUC: 0.631-0.993; MCC: 0.191-0.891). It was similar for both the results of comparisons with the other individual predictors and third-party protein-specific predictors. For several proteins (BRCA1, BRCA2, COL1A2, and RYR1), the performance of the MNA-based method was outstanding, capable of capturing the pathogenic effect of structural changes in amino acid substitutions.Availability and implementationThe datasets are available as supplemental data at Bioinformatics online. A python script to convert amino acid and nucleotide sequences from single-letter codes to SD files is available at https://github.com/SmirnygaTotoshka/SequenceToSDF. The authors provide trial licenses for MultiPASS software to interested readers upon request.

Dataset Information

Multiple consequences of a single amino acid pathogenic RTK mutation: the A391E mutation in FGFR3.

Publications

Multiple consequences of a single amino acid pathogenic RTK mutation: the A391E mutation in FGFR3.

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