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An AI-generated proteome-scale dataset of predicted protein structures for the ctenophore Mnemiopsis leidyi.
Moreland, R Travis; Zhang, Suiyuan; Barreira, Sofia N; Ryan, Joseph F; Baxevanis, Andreas D.
Afiliación
  • Moreland RT; Center for Genomics and Data Science Research, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • Zhang S; Center for Genomics and Data Science Research, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • Barreira SN; Center for Genomics and Data Science Research, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA.
  • Ryan JF; Whitney Laboratory for Marine Bioscience, University of Florida, St. Augustine, Florida, USA.
  • Baxevanis AD; Center for Genomics and Data Science Research, Division of Intramural Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA.
Proteomics ; 24(15): e2300397, 2024 Aug.
Article en En | MEDLINE | ID: mdl-38329168
ABSTRACT
This Dataset Brief describes the computational prediction of protein structures for the ctenophore Mnemiopsis leidyi. Here, we report the proteome-scale generation of 15,333 protein structure predictions using AlphaFold, as well as an updated implementation of publicly available search, manipulation, and visualization tools for these protein structure predictions through the Mnemiopsis Genome Project Portal (https//research.nhgri.nih.gov/mnemiopsis). The utility of these predictions is demonstrated by highlighting comparisons to experimentally determined structures for the light-sensitive protein mnemiopsin 1 and the ionotropic glutamate receptor (iGluR). The application of these novel protein structure prediction methods will serve to further position non-bilaterian species such as Mnemiopsis as powerful model systems for the study of early animal evolution and human health.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteoma / Ctenóforos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Proteomics Asunto de la revista: BIOQUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Proteoma / Ctenóforos Tipo de estudio: Prognostic_studies / Risk_factors_studies Límite: Animals Idioma: En Revista: Proteomics Asunto de la revista: BIOQUIMICA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos