Going from where to why--interpretable prediction of protein subcellular localization.
Going from where to why--interpretable prediction of protein subcellular localization.
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DOI：
10.1093/bioinformatics/btq115
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发表时间：
2010-05-01
期刊：
影响因子：
--
通讯作者：
Kohlbacher O
中科院分区：
文献类型：
--
作者：
Briesemeister S;Rahnenführer J;Kohlbacher O
动机：蛋白质的亚细胞定位是理解蛋白质功能的关键。亚细胞定位的计算预测已经成为实验方法的一种可行的替代方法。虽然目前基于机器学习的方法具有良好的预测精度，但大多数方法都存在两个关键问题：缺乏可解释性和处理多个位置。结果：我们提出了YLoc，一种新的预测蛋白质亚细胞定位的方法，解决了这些问题。由于其简单的结构，YLoc可以识别有助于其亚细胞定位的蛋白质序列的相关特征，例如与蛋白质分选相关的定位信号或基序。我们提出了几个示例应用程序，其中YLoc识别负责蛋白质定位的序列特征，从而不仅揭示了蛋白质被运输到哪个位置，而且还揭示了为什么它被运输到那里。YLoc还提供了预测的置信度估计。因此，用户可以决定什么水平的误差是可接受的预测。由于概率方法和数千个双靶蛋白的使用，YLoc能够预测每个蛋白质的多个位置。使用几个独立的蛋白质亚细胞定位数据集对YLoc进行基准测试，并与其他最先进的预测因子进行比较。不考虑低置信度预测，YLoc可以实现超过90%的预测准确率。此外，我们表明，YLoc能够可靠地预测多个位置，并在这方面优于最好的预测。可用性：www.multiloc.org/YLoc联系：briese@informatik.uni-tuebingen.de补充信息：补充数据可在生物信息学在线。
Motivation: Protein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations. Results: We present YLoc, a novel method for predicting protein subcellular localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its subcellular localization, e.g. localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence features responsible for protein localization, and thus reveals not only to which location a protein is transported to, but also why it is transported there. YLoc also provides a confidence estimate for the prediction. Thus, the user can decide what level of error is acceptable for a prediction. Due to a probabilistic approach and the use of several thousands of dual-targeted proteins, YLoc is able to predict multiple locations per protein. YLoc was benchmarked using several independent datasets for protein subcellular localization and performs on par with other state-of-the-art predictors. Disregarding low-confidence predictions, YLoc can achieve prediction accuracies of over 90%. Moreover, we show that YLoc is able to reliably predict multiple locations and outperforms the best predictors in this area. Availability: www.multiloc.org/YLoc Contact: briese@informatik.uni-tuebingen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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