CELL-E: A Text-to-Image Transformer for Protein Image Prediction

Accurately predicting cellular activities of proteins based on their primary amino acid sequences would greatly improve our understanding of the proteome. In this paper, we present CELL-E, a text-to-image transformer model that generates 2D probability density images describing the spatial distribution of proteins within cells. Given an amino acid sequence and a reference image for cell or nucleus morphology, CELL-E predicts a more refined representation of protein localization, as opposed to previous in silico methods that rely on pre-defined, discrete class annotations of protein localization to subcellular compartments.