Department of Informatics, Bioinformatics & Computational Biology - i12, TUM (Technical University of Munich), Boltzmannstr. 3, 85748, Garching/Munich, Germany.
School of Chemistry and Molecular Biosciences, UQ (University of Queensland), Cooper Rd, Brisbane City, QLD, 4072, Australia.
BMC Bioinformatics. 2019 Apr 23;20(1):205. doi: 10.1186/s12859-019-2790-9.
Sub-nuclear structures or locations are associated with various nuclear processes. Proteins localized in these substructures are important to understand the interior nuclear mechanisms. Despite advances in high-throughput methods, experimental protein annotations remain limited. Predictions of cellular compartments have become very accurate, largely at the expense of leaving out substructures inside the nucleus making a fine-grained analysis impossible.
Here, we present a new method (LocNuclei) that predicts nuclear substructures from sequence alone. LocNuclei used a string-based Profile Kernel with Support Vector Machines (SVMs). It distinguishes sub-nuclear localization in 13 distinct substructures and distinguishes between nuclear proteins confined to the nucleus and those that are also native to other compartments (traveler proteins). High performance was achieved by implicitly leveraging a large biological knowledge-base in creating predictions by homology-based inference through BLAST. Using this approach, the performance reached AUC = 0.70-0.74 and Q13 = 59-65%. Travelling proteins (nucleus and other) were identified at Q2 = 70-74%. A Gene Ontology (GO) analysis of the enrichment of biological processes revealed that the predicted sub-nuclear compartments matched the expected functionality. Analysis of protein-protein interactions (PPI) show that formation of compartments and functionality of proteins in these compartments highly rely on interactions between proteins. This suggested that the LocNuclei predictions carry important information about function. The source code and data sets are available through GitHub: https://github.com/Rostlab/LocNuclei .
LocNuclei predicts subnuclear compartments and traveler proteins accurately. These predictions carry important information about functionality and PPIs.
亚核结构或位置与各种核过程相关。定位于这些亚结构中的蛋白质对于理解核内机制非常重要。尽管高通量方法取得了进展，但实验性蛋白质注释仍然有限。细胞区室的预测已经非常准确，主要是因为忽略了核内的亚结构，使得精细的分析成为不可能。
在这里，我们提出了一种新的方法（LocNuclei），该方法仅通过序列预测核亚结构。LocNuclei 使用基于字符串的 Profile Kernel 与支持向量机（SVMs）。它区分了 13 种不同亚结构中的核亚定位，并区分了局限于核内的核蛋白和那些也固有存在于其他区室（旅行者蛋白）的核蛋白。通过同源推断通过 BLAST 进行基于同源性的推断，隐含地利用了大量的生物学知识库来创建预测，从而实现了高性能。使用这种方法，性能达到 AUC=0.70-0.74 和 Q13=59-65%。旅行者蛋白（核内和其他）的识别率为 Q2=70-74%。对生物过程的富集进行基因本体论（GO）分析表明，预测的亚核区室与预期的功能相匹配。对蛋白质-蛋白质相互作用（PPI）的分析表明，区室的形成和这些区室中蛋白质的功能高度依赖于蛋白质之间的相互作用。这表明 LocNuclei 的预测携带了关于功能的重要信息。源代码和数据集可通过 GitHub 获得：https://github.com/Rostlab/LocNuclei。
LocNuclei 准确预测亚核区室和旅行者蛋白。这些预测携带了关于功能和 PPI 的重要信息。
