Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA.
Nucleic Acids Res. 2023 Jul 5;51(W1):W343-W349. doi: 10.1093/nar/gkad374.
Predicting protein localization and understanding its mechanisms are critical in biology and pathology. In this context, we propose a new web application of MULocDeep with improved performance, result interpretation, and visualization. By transferring the original model into species-specific models, MULocDeep achieved competitive prediction performance at the subcellular level against other state-of-the-art methods. It uniquely provides a comprehensive localization prediction at the suborganellar level. Besides prediction, our web service quantifies the contribution of single amino acids to localization for individual proteins; for a group of proteins, common motifs or potential targeting-related regions can be derived. Furthermore, the visualizations of targeting mechanism analyses can be downloaded for publication-ready figures. The MULocDeep web service is available at https://www.mu-loc.org/.
预测蛋白质的定位及其机制在生物学和病理学中至关重要。在这种背景下,我们提出了一个新的 MULocDeep 网络应用程序,该应用程序在性能、结果解释和可视化方面都有所改进。通过将原始模型转移到物种特异性模型中,MULocDeep 在亚细胞水平上的预测性能与其他最先进的方法相媲美。它还独特地提供了亚细胞器水平的全面定位预测。除了预测之外,我们的网络服务还可以量化单个氨基酸对单个蛋白质定位的贡献;对于一组蛋白质,可以得出常见的基序或潜在的靶向相关区域。此外,还可以下载靶向机制分析的可视化结果,以生成可发表的图形。MULocDeep 网络服务可在 https://www.mu-loc.org/ 上获得。