MULocDeep: An Interpretable Deep Learning Model for Protein Localization Prediction with Sub-organelle Resolution

Prediction of protein localization plays an important role in understanding protein function and mechanism. A deep learning-based localization prediction tool (“MULocDeep”) assessing each amino acid’s contribution to the localization process provides insights into the mechanism of protein sorting and localization motifs. A dataset with 45 sub-organellar localization annotations under 10 major sub-cellular compartments was produced and the tool was tested on an independent dataset of mitochondrial proteins that were extracted from Arabidopsis thaliana cell cultures, Solanum tuberosum tubers, and Vicia faba roots, and analyzed by shotgun mass spectrometry.

蛋白质定位预测对于解析蛋白质功能与作用机制具有重要意义。本研究开发了一款基于深度学习的蛋白质定位预测工具MULocDeep，该工具可评估每个氨基酸对蛋白质定位过程的贡献，能够为蛋白质分选与定位基序的作用机制提供研究视角。本研究构建了一套覆盖10个主要亚细胞区室、包含45项亚细胞器定位注释的数据集，并通过独立线粒体蛋白质数据集对该工具开展测试：该数据集的线粒体蛋白质提取自拟南芥（Arabidopsis thaliana）细胞培养物、马铃薯（Solanum tuberosum）块茎以及蚕豆（Vicia faba）根系，并经鸟枪法质谱分析法完成鉴定分析。