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MULocDeep 网络服务,用于在亚细胞和亚细胞器水平上进行蛋白质定位预测和可视化。

MULocDeep web service for protein localization prediction and visualization at subcellular and suborganellar levels.

机构信息

Department of Electrical Engineer and Computer Science, Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, USA.

出版信息

Nucleic Acids Res. 2023 Jul 5;51(W1):W343-W349. doi: 10.1093/nar/gkad374.

Abstract

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/.

摘要

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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0e0f/10320056/76770cdbb09a/gkad374figgra1.jpg

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