ArticleFrontiers in cell and developmental biology2021
Translation of Cellular Protein Localization Using Convolutional Networks.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 9 citations in OpenAlex.
- Virtual Fluorescence Translation for Biological Tissue by Conditional Generative Adversarial Network.Phenomics (Cham, Switzerland) · 2023Article
- Digitally predicting protein localization and manipulating protein activity in fluorescence images using 4D reslicing GAN.Bioinformatics (Oxford, England) · 2023Article
- Proteomic Analysis of the Effect ofMolecules (Basel, Switzerland) · 2022Article
- Emerging role of caldesmon in cancer: A potential biomarker for colorectal cancer and other cancers.World journal of gastrointestinal oncology · 2022Review
- Prot2Prot: a deep learning model for rapid, photorealistic macromolecular visualization.Journal of computer-aided molecular design · 2022Article
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8 authors at 1 institution in 1 country.
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Abstract
Protein localization in cells has been analyzed by fluorescent labeling using indirect immunofluorescence and fluorescent protein tagging. However, the relationships between the localization of different proteins had not been analyzed using artificial intelligence. Here, we applied convolutional networks for the prediction of localization of the cytoskeletal proteins from the localization of the other proteins. Lamellipodia are one of the actin-dependent subcellular structures involved in cell migration and are mainly generated by the Wiskott-Aldrich syndrome protein (WASP)-family verprolin homologous protein 2 (WAVE2) and the membrane remodeling I-BAR domain protein IRSp53. Focal adhesion is another actin-based structure that contains vinculin protein and promotes lamellipodia formation and cell migration. In contrast, microtubules are not directly related to actin filaments. The convolutional network was trained using images of actin filaments paired with WAVE2, IRSp53, vinculin, and microtubules. The generated images of WAVE2, IRSp53, and vinculin were highly similar to their real images. In contrast, the microtubule images generated from actin filament images were inferior without the generation of filamentous structures, suggesting that microscopic images of actin filaments provide more information about actin-related protein localization. Collectively, this study suggests that image translation by the convolutional network can predict the localization of functionally related proteins, and the convolutional network might be used to describe the relationships between the proteins by their localization.
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