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Deep generative model for protein subcellular localization prediction.

Guo-Hua Yuan1, Jinzhe Li2,3, Zejun Yang2

  • 1Center for Molecular Medicine, Children's Hospital of Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, 131 Dongan Road, Xuhui District, Shanghai 200032, China.

Briefings in Bioinformatics
|April 11, 2025
PubMed
Summary
This summary is machine-generated.

DeepGPS, a novel deep generative model, predicts protein subcellular localization using primary sequences and fluorescence images. It generates both textual labels and visual outputs, enhancing biological research accessibility.

Keywords:
deep learningimage generationprotein subcellular localization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Protein sequence dictates structure and subcellular localization.
  • Existing AI models for protein localization primarily offer textual predictions.
  • Accurate subcellular localization is crucial for understanding protein function.

Purpose of the Study:

  • To develop a deep generative model, deepGPS, for enhanced protein subcellular localization prediction.
  • To provide both textual and image-based outputs for improved biological insights.
  • To create a user-friendly platform for studying protein localization and function.

Main Methods:

  • Training a deep generative model (deepGPS) with protein primary sequences and fluorescence images.
  • Utilizing distinct image datasets from different cell lines to develop cell-type-specific models.
  • Extending the model for predicting localization in specific organelles like vesicles and endoplasmic reticulum.

Main Results:

  • DeepGPS accurately predicts cytoplasmic and nuclear protein localizations.
  • The model generates both textual labels and visual outputs.
  • Cell-type-specific models demonstrate potential for comparative analyses.
  • The model shows promise for predicting localization in other organelles with limited data.

Conclusions:

  • DeepGPS offers a novel approach to protein subcellular localization prediction with dual textual and image outputs.
  • The developed openGPS website provides a valuable public resource for biological research.
  • DeepGPS has the potential to advance the study of protein function and cellular mechanisms.