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Contextual AI models for single-cell protein biology.
Li, Michelle M; Huang, Yepeng; Sumathipala, Marissa; Liang, Man Qing; Valdeolivas, Alberto; Ananthakrishnan, Ashwin N; Liao, Katherine; Marbach, Daniel; Zitnik, Marinka.
Afiliación
  • Li MM; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Huang Y; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Sumathipala M; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Liang MQ; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Valdeolivas A; Roche Pharma Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd, Basel, Switzerland.
  • Ananthakrishnan AN; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Liao K; Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA.
  • Marbach D; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
  • Zitnik M; Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA, USA.
Nat Methods ; 21(8): 1546-1557, 2024 Aug.
Article en En | MEDLINE | ID: mdl-39039335
ABSTRACT
Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE's embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs' effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE's ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Análisis de la Célula Individual / Aprendizaje Profundo Límite: Humans Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 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 Asunto principal: Análisis de la Célula Individual / Aprendizaje Profundo Límite: Humans Idioma: En Revista: Nat Methods Asunto de la revista: TECNICAS E PROCEDIMENTOS DE LABORATORIO Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Estados Unidos