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Protein Dynamics in Living Cells

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Heterokaryon Technique for Analysis of Cell Type-specific Localization

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Published on: March 11, 2011

CELL-E: A Text-To-Image Transformer for Protein Localization Prediction.

Emaad Khwaja^1,2, Yun S Song^2,3,4, Bo Huang^4,5,6

¹UC Berkeley - UCSF Joint Graduate Program in Bioengineering, CA, USA.

Research Square

|July 3, 2023

Summary

Predicting protein spatial distribution using amino acid sequences is now possible. CELL-E, a novel text-to-image model, generates detailed protein localization maps within cells, advancing proteome understanding.

Keywords:

generative models single-cell imaging text-to-image synthesis transformers

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Last Updated: Jul 24, 2025

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Published on: March 11, 2011

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Detecting Protein Subcellular Localization by Green Fluorescence Protein Tagging and 4',6-Diamidino-2-phenylindole Staining in Caenorhabditis elegans

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Published on: July 30, 2018

Area of Science:

Proteomics and Cell Biology
Bioinformatics and Computational Biology
Machine Learning in Biology

Background:

Understanding protein localization is crucial for deciphering cellular functions and the overall proteome.
Current computational methods for predicting protein localization often rely on discrete, predefined subcellular compartments, limiting predictive accuracy.
There is a need for more refined and accurate methods to predict the spatial distribution of proteins within cells.

Approach:

We introduce CELL-E, a novel text-to-image transformer model designed for predicting protein localization.
CELL-E takes a protein's amino acid sequence and a reference cell/nucleus morphology image as input.
The model generates 2D probability density images, offering a nuanced representation of protein spatial distribution.

Key Points:

CELL-E moves beyond discrete localization classes to predict continuous spatial probability density maps.
The model integrates information from amino acid sequences and cellular morphology for improved predictions.
This approach provides a more refined understanding of where proteins reside within the cellular environment.

Conclusions:

CELL-E offers a significant advancement in predicting protein spatial distribution from primary amino acid sequences.
The generated probability density images provide a more detailed and accurate representation of protein localization compared to existing in silico methods.
This work enhances our ability to understand the proteome and protein function through improved computational prediction.