School of Information Science and Engineering, University of Jinan, Jinan, China.
Laboratory of Zoology, Graduate School of Bioresource and Bioenvironmental Sciences, Kyushu University, Fukuoka-Shi, Fukuoka, Japan.
BMC Bioinformatics. 2023 Sep 22;24(1):357. doi: 10.1186/s12859-023-05475-x.
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 .
植物液泡是植物生长和发育过程中的重要细胞器，准确鉴定其蛋白质对于了解其生物学特性至关重要。在这项研究中，我们开发了一种名为 GraphIdn 的新型模型，用于鉴定植物液泡蛋白。该模型使用深度表示学习模型 SeqVec 来初始化氨基酸序列。我们利用 AlphaFold2 算法获取相应植物液泡蛋白的结构信息，然后将计算得到的接触图输入到图卷积神经网络中。GraphIdn 在独立测试和五重交叉验证中的准确率分别达到了 88.51%和 89.93%，优于之前的最先进预测器。据我们所知，这是第一个使用预测的蛋白质拓扑结构图来识别植物液泡蛋白的模型。此外，我们通过应用 GraphIdn 模型来识别和定位过氧化物酶体蛋白，评估了该模型的有效性和泛化能力，取得了有前景的结果。源代码和数据集可在 https://github.com/SJNNNN/GraphIdn 上获取。
