Batch-Learning Self-Organizing Map for Predicting Functions of Poorly-Characterized Proteins Massively Accumulated
Batch-Learning Self-Organizing Map for Predicting Functions of Poorly-Characterized Proteins Massively Accumulated
复制标题
用于预测大量积累的不良特征蛋白质功能的批量学习自组织图
DOI：
10.1007/978-3-642-02397-2_1
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发表时间：
2009
期刊：
影响因子：
--
通讯作者：
T.
中科院分区：
文献类型：
--
作者：
T. Abe;S. Kanaya;T.
由于大量基因组序列的解码，积累了许多无法通过氨基酸序列同源性搜索来鉴定其功能的蛋白质，但这些蛋白质对科学和工业仍然没有用处。迫切需要建立新的蛋白质功能预测方法。我们之前开发了用于基因组信息学的批量学习 SOM (BL-SOM)；在这里，我们开发了 BL-SOM，根据蛋白质寡肽组成的相似性来预测蛋白质的功能。寡肽是蛋白质的组成部分，参与其功能基序和结构部分的形成。对于分类为 2853 个功能已知的 COG（直系同源群）的 110,000 个蛋白质中的寡肽频率，BL-SOM 可以忠实地再现 COG 分类，因此，通过同源搜索未识别其功能的蛋白质可能与功能已知的蛋白质相关。 BL-SOM 用于预测从宏基因组分析中获得的大量蛋白质的蛋白质功能。
As the result of the decoding of large numbers of genome sequences, numerous proteins whose functions cannot be identified by the homology search of amino acid sequences have accumulated and remain of no use to science and industry. Establishment of novel prediction methods for protein function is urgently needed. We previously developed Batch-Learning SOM (BL-SOM) for genome informatics; here, we developed BL-SOM to predict functions of proteins on the basis of similarity in oligopeptide composition of proteins. Oligopeptides are component parts of a protein and involved in formation of its functional motifs and structural parts. Concerning oligopeptide frequencies in 110,000 proteins classified into 2853 function-known COGs (clusters of orthologous groups), BL-SOM could faithfully reproduce the COG classifications, and therefore, proteins whose functions have been unidentified with homology searches could be related to function-known proteins. BL-SOM was applied to predict protein functions of large numbers of proteins obtained from metagenome analyses.