GTExome: Modeling commonly expressed missense mutations in the human genome.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2024
Historique:
received: 25 01 2024
accepted: 26 04 2024
medline: 30 5 2024
pubmed: 30 5 2024
entrez: 30 5 2024
Statut: epublish

Résumé

A web application, GTExome, is described that quickly identifies, classifies, and models missense mutations in commonly expressed human proteins. GTExome can be used to categorize genomic mutation data with tissue specific expression data from the Genotype-Tissue Expression (GTEx) project. Commonly expressed missense mutations in proteins from a wide range of tissue types can be selected and assessed for modeling suitability. Information about the consequences of each mutation is provided to the user including if disulfide bonds, hydrogen bonds, or salt bridges are broken, buried prolines introduced, buried charges are created or lost, charge is swapped, a buried glycine is replaced, or if the residue that would be removed is a proline in the cis configuration. Also, if the mutation site is in a binding pocket the number of pockets and their volumes are reported. The user can assess this information and then select from available experimental or computationally predicted structures of native proteins to create, visualize, and download a model of the mutated protein using Fast and Accurate Side-chain Protein Repacking (FASPR). For AlphaFold modeled proteins, confidence scores for native proteins are provided. Using this tool, we explored a set of 9,666 common missense mutations from a variety of tissues from GTEx and show that most mutations can be modeled using this tool to facilitate studies of protein-protein and protein-drug interactions. The open-source tool is freely available at https://pharmacogenomics.clas.ucdenver.edu/gtexome/.

Identifiants

pubmed: 38814966
doi: 10.1371/journal.pone.0303604
pii: PONE-D-24-03381
doi:

Substances chimiques

Proteins 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0303604

Informations de copyright

Copyright: © 2024 Hoffman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Auteurs

Jill Hoffman (J)

Computational Bioscience, University of Colorado Anschutz Medical Campus, Aurora, CO, United States of America.

Henry Tan (H)

Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America.

Clara Sandoval-Cooper (C)

Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America.

Kaelyn de Villiers (K)

Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America.

Scott M Reed (SM)

Department of Chemistry, University of Colorado Denver, Denver, CO, United States of America.

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Classifications MeSH