Identification of plant vacuole proteins by using graph neural network and contact maps

BMC Bioinformatics

Research

Jiazi Chen¹  Naoki Iwamori¹  Yuehui Chen²  Jin Sun³  Jianan Sui⁴ 

[1] Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan;School of Artificial Intelligence Institute and Information Science and Engineering, University of Jinan, Jinan, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, 611731, Chengdu, China;School of Information Science and Engineering, University of Jinan, Jinan, China;

关键词: Plant vacuole proteins;    Peroxisomal proteins;    SeqVec;    AlphaFold2;    Graph convolutional neural network;   

DOI  :  10.1186/s12859-023-05475-x

 received in 2023-05-13, accepted in 2023-09-12,  发布年份 2023

来源: Springer

【 摘 要 】

Plant vacuoles are essential organelles in the growth and development of plants, and accurate identification of their proteins is crucial for understanding their biological properties. In this study, we developed a novel model called GraphIdn for the identification of plant vacuole proteins. The model uses SeqVec, a deep representation learning model, to initialize the amino acid sequence. We utilized the AlphaFold2 algorithm to obtain the structural information of corresponding plant vacuole proteins, and then fed the calculated contact maps into a graph convolutional neural network. GraphIdn achieved accuracy values of 88.51% and 89.93% in independent testing and fivefold cross-validation, respectively, outperforming previous state-of-the-art predictors. As far as we know, this is the first model to use predicted protein topology structure graphs to identify plant vacuole proteins. Furthermore, we assessed the effectiveness and generalization capability of our GraphIdn model by applying it to identify and locate peroxisomal proteins, which yielded promising outcomes. The source code and datasets can be accessed at https://github.com/SJNNNN/GraphIdn.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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