Protein function prediction via graph kernels
Protein function prediction via graph kernels
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DOI：
10.1093/bioinformatics/bti1007
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发表时间：
2005-06-01
期刊：
影响因子：
5.8
通讯作者：
Kriegel, HP
中科院分区：
文献类型：
--
作者：
Borgwardt, KM;Ong, CS;Kriegel, HP
动机：蛋白质功能预测的计算方法通过寻找具有相似序列、结构、表面裂缝、化学性质、氨基酸基序、相互作用伙伴或系统发育特征的蛋白质来推断蛋白质功能。我们提出了一种新的方法，将序列、结构和化学信息结合到一个蛋白质图模型中。我们使用图核和支持向量机对这些蛋白质图进行分类，预测酶和非酶的功能类隶属度。结果：我们的图模型仅可从蛋白质序列和结构衍生，与需要额外蛋白质信息（如表面口袋的大小）的矢量模型竞争。如果我们将这些额外的信息包含到我们的图模型中，我们的分类器会产生比向量模型更高的精度水平。超核允许我们选择并以最佳方式组合蛋白质图中最相关的节点属性。为构建高效、有效地整合多种来源蛋白质信息的蛋白质功能预测系统奠定了基础。
Motivation: Computational approaches to protein function prediction infer protein function by finding proteins with similar sequence, structure, surface clefts, chemical properties, amino acid motifs, interaction partners or phylogenetic profiles. We present a new approach that combines sequential, structural and chemical information into one graph model of proteins. We predict functional class membership of enzymes and non-enzymes using graph kernels and support vector machine classification on these protein graphs.Results: Our graph model, derivable from protein sequence and structure only, is competitive with vector models that require additional protein information, such as the size of surface pockets. If we include this extra information into our graph model, our classifier yields significantly higher accuracy levels than the vector models. Hyperkernels allow us to select and to optimally combine the most relevant node attributes in our protein graphs. We have laid the foundation for a protein function prediction system that integrates protein information from various sources efficiently and effectively.