Function prediction of uncharacterized proteins.
Function prediction of uncharacterized proteins.
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DOI：
10.1142/s0219720007002503
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发表时间：
2007-02-01
影响因子：
1
通讯作者：
Kihara, Daisuke
中科院分区：
文献类型：
--
作者：
Hawkins, Troy;Kihara, Daisuke
基因组计划产生的未知蛋白质序列的功能预测已经成为计算生物学的一个重要焦点。我们已经分类了几种超越传统序列相似性的方法，这些方法利用压倒性的大量可用数据进行计算功能预测，包括基于结构、关联（基因组背景）、相互作用（细胞背景）、过程（代谢背景）和蛋白质组学实验的方法。因为它们结合了基于序列的方法中不使用的结构和实验数据，所以它们可以为蛋白质功能预测提供额外的准确性和可靠性。在这里，我们首先回顾蛋白质功能的定义。然后介绍了这些方法的最新发展，特别关注可以做出的预测类型。需要进一步发展全面的系统生物学技术，可以利用不断增加的数据所提出的基因组学和蛋白质组学社区的强调。为了方便读者，每个类别的有用的在线资源表都包括在内。计算科学家在不久的将来的生物学研究和计算和实验生物学之间的相互作用的作用也得到了解决。
Function prediction of uncharacterized protein sequences generated by genome projects has emerged as an important focus for computational biology. We have categorized several approaches beyond traditional sequence similarity that utilize the overwhelmingly large amounts of available data for computational function prediction, including structure-, association (genomic context)-, interaction (cellular context)-, process (metabolic context)-, and proteomics-experiment-based methods. Because they incorporate structural and experimental data that is not used in sequence-based methods, they can provide additional accuracy and reliability to protein function prediction. Here, first we review the definition of protein function. Then the recent developments of these methods are introduced with special focus on the type of predictions that can be made. The need for further development of comprehensive systems biology techniques that can utilize the ever-increasing data presented by the genomics and proteomics communities is emphasized. For the readers' convenience, tables of useful online resources in each category are included. The role of computational scientists in the near future of biological research and the interplay between computational and experimental biology are also addressed.