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Structural Analysis of Genomic and Proteomic Signatures Reveal Dynamic Expression of Intrinsically Disordered Regions in Breast Cancer and Tissue.
Zatorski, Nicole; Sun, Yifei; Elmas, Abdulkadir; Dallago, Christian; Karl, Timothy; Stein, David; Rost, Burkhard; Huang, Kuan-Lin; Walsh, Martin; Schlessinger, Avner.
Afiliación
  • Zatorski N; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
  • Sun Y; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
  • Elmas A; Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
  • Dallago C; NVIDIA DE GmbH, Einsteinstraße 172, 81677 München, Germany.
  • Karl T; Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany.
  • Stein D; Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany.
  • Rost B; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
  • Huang KL; Faculty of Informatics, Bioinformatics & Computational Biology, Technical University Munich (TUM), 85748 Garching, Germany.
  • Walsh M; Department of Genetic and Genomic Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
  • Schlessinger A; Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, One Gustave Levey Pl NY, NY 10029, USA.
bioRxiv ; 2023 Feb 24.
Article en En | MEDLINE | ID: mdl-36865220
Structural features of proteins capture underlying information about protein evolution and function, which enhances the analysis of proteomic and transcriptomic data. Here we develop Structural Analysis of Gene and protein Expression Signatures (SAGES), a method that describes expression data using features calculated from sequence-based prediction methods and 3D structural models. We used SAGES, along with machine learning, to characterize tissues from healthy individuals and those with breast cancer. We analyzed gene expression data from 23 breast cancer patients and genetic mutation data from the COSMIC database as well as 17 breast tumor protein expression profiles. We identified prominent expression of intrinsically disordered regions in breast cancer proteins as well as relationships between drug perturbation signatures and breast cancer disease signatures. Our results suggest that SAGES is generally applicable to describe diverse biological phenomena including disease states and drug effects.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: BioRxiv Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos