Self-supervised deep learning encodes high-resolution features of protein subcellular localization.

ABSTRACT: Explaining the diversity and complexity of protein localization is essential to fully understand cellular architecture. Here we present cytoself, a deep-learning approach for fully self-supervised protein localization profiling and clustering. Cytoself leverages a self-supervised training scheme that does not require preexisting knowledge, categories or annotations. Training cytoself on images of 1,311 endogenously labeled proteins from the OpenCell database reveals a highly resolved protein localization atlas that recapitulates major scales of cellular organization, from coarse classes, such as nuclear and cytoplasmic, to the subtle localization signatures of individual protein complexes. We quantitatively validate cytoself's ability to cluster proteins into organelles and protein complex

SUBMITTER: Kobayashi H 

PROVIDER: S-EPMC9349041 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature