Structural bioinformatics prediction of membrane-binding proteins
Structural bioinformatics prediction of membrane-binding proteins
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DOI：
10.1016/j.jmb.2006.03.039
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发表时间：
2006-06-02
影响因子：
5.6
通讯作者：
Lu, Hui
中科院分区：
文献类型：
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作者：
Bhardwaj, Nitin;Stahelin, Robert V.;Lu, Hui
膜结合外周蛋白在许多生物过程中发挥重要作用，包括细胞信号传导和膜运输。与整体膜蛋白不同，这些蛋白以可逆的方式结合膜。由于外周蛋白不具有典型的跨膜片段，因此很难从它们的氨基酸序列中识别它们。作为基因组级鉴定膜结合外周蛋白的第一步，我们建立了一个基于核的机器学习协议。已知膜结合蛋白的关键特征，包括静电特性和氨基酸组成，从它们的氨基酸序列和三级结构中计算出来，然后将其纳入支持向量机进行分类。使用40个膜结合蛋白和230个非膜结合蛋白的数据集来构建和验证该方案。交叉验证和保留评估表明，该方案的预测准确率分别达到93.7%和91.6%。该方案被应用于预测来自新型蛋白激酶c的四个C2结构域的膜结合特性，尽管这些C2结构域具有50%的序列一致性，但只有一个被预测与膜结合，这是通过表面等离子体共振分析实验验证的。这些结果表明，我们的方案可用于预测各种模块化结构域的膜结合特性，并可能进一步扩展到膜结合外周蛋白的基因组水平鉴定。(c) 2006 Elsevier Ltd.版权所有。
Membrane-binding peripheral proteins play important roles in many biological processes, including cell signaling and membrane trafficking. Unlike integral membrane proteins, these proteins bind the membrane mostly in a reversible manner. Since peripheral proteins do not have canonical transmembrane segments, it is difficult to identify them from their amino acid sequences. As a first step toward genome-scale identification of membrane-binding peripheral proteins, we built a kernel-based machine learning protocol. Key features of known membrane-binding proteins, including electrostatic properties and amino acid composition, were calculated from their amino acid sequences and tertiary structures, which were then incorporated into the support vector machine to perform the classification. A data set of 40 membrane-binding proteins and 230 non-membrane-binding proteins was used to construct and validate the protocol. Cross-validation and holdout evaluation of the protocol showed that the accuracy of the prediction reached up to 93.7% and 91.6%, respectively. The protocol was applied to the prediction of membrane-binding properties of four C2 domains from novel protein kinases C. Although these C2 domains have 50% sequence identity only one of them was predicted to bind the membrane, which was verified experimentally with surface plasmon resonance analysis. These results suggest that our protocol can be used for predicting membrane-binding properties of a wide variety of modular domains and may be further extended to genome-scale identification of membrane-binding peripheral proteins. (c) 2006 Elsevier Ltd. All rights reserved.