Key Lab of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.
Rapid Commun Mass Spectrom. 2010 Mar;24(6):807-14. doi: 10.1002/rcm.4448.
Database searching is the technique of choice for shotgun proteomics, and to date much research effort has been spent on improving its effectiveness. However, database searching faces a serious challenge of efficiency, considering the large numbers of mass spectra and the ever fast increase in peptide databases resulting from genome translations, enzymatic digestions, and post-translational modifications. In this study, we conducted systematic research on speeding up database search engines for protein identification and illustrate the key points with the specific design of the pFind 2.1 search engine as a running example. Firstly, by constructing peptide indexes, pFind achieves a speedup of two to three compared with that without peptide indexes. Secondly, by constructing indexes for observed precursor and fragment ions, pFind achieves another speedup of two. As a result, pFind compares very favorably with predominant search engines such as Mascot, SEQUEST and X!Tandem.
数据库检索是鸟枪法蛋白质组学的首选技术，迄今为止，大量的研究工作都致力于提高其效率。然而，考虑到大量的质谱数据以及由于基因组翻译、酶切和翻译后修饰而导致的肽数据库的快速增长，数据库检索面临着严重的效率挑战。在本研究中，我们对加速蛋白质鉴定的数据库搜索引擎进行了系统的研究，并以 pFind 2.1 搜索引擎的具体设计为例来说明关键点。首先，通过构建肽索引，pFind 与不使用肽索引相比，速度提高了两到三倍。其次，通过构建观察到的前体离子和片段离子的索引，pFind 又提高了两倍的速度。因此，pFind 与 Mascot、SEQUEST 和 X!Tandem 等主要搜索引擎相比具有很大的优势。
