Translation of Cellular Protein Localization Using Convolutional Networks

Kei Shigene; Yuta Hiasa; Yoshito Otake; Mazen Soufi; Suphamon Janewanthanakul; Tamako Nishimura; Yoshinobu Sato; Shiro Suetsugu

doi:10.3389/fcell.2021.635231

Translation of Cellular Protein Localization Using Convolutional Networks

Front Cell Dev Biol. 2021 Aug 5:9:635231. doi: 10.3389/fcell.2021.635231. eCollection 2021.

Authors

Kei Shigene¹, Yuta Hiasa², Yoshito Otake², Mazen Soufi², Suphamon Janewanthanakul¹, Tamako Nishimura¹, Yoshinobu Sato^{2

3}, Shiro Suetsugu^{1

3

4}

Affiliations

¹ Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.
² Division of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.
³ Data Science Center, Nara Institute of Science and Technology, Ikoma, Japan.
⁴ Center for Digital Green-Innovation, Nara Institute of Science and Technology, Ikoma, Japan.

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.

Keywords: Pix2pix; WAVE2; image conversion; lamellipodia; machine learning.