Yang Hao, Chi Hao, Zhou Wen-Jing, Zeng Wen-Feng, He Kun, Liu Chao, Sun Rui-Xiang, He Si-Min
Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, Chinese Academy of Sciences , Beijing 100190, China.
University of Chinese Academy of Sciences, Beijing 100049, China.
J Proteome Res. 2017 Feb 3;16(2):645-654. doi: 10.1021/acs.jproteome.6b00716. Epub 2017 Jan 12.
De novo peptide sequencing has improved remarkably, but sequencing full-length peptides with unexpected modifications is still a challenging problem. Here we present an open de novo sequencing tool, Open-pNovo, for de novo sequencing of peptides with arbitrary types of modifications. Although the search space increases by ∼300 times, Open-pNovo is close to or even ∼10-times faster than the other three proposed algorithms. Furthermore, considering top-1 candidates on three MS/MS data sets, Open-pNovo can recall over 90% of the results obtained by any one traditional algorithm and report 5-87% more peptides, including 14-250% more modified peptides. On a high-quality simulated data set, ∼85% peptides with arbitrary modifications can be recalled by Open-pNovo, while hardly any results can be recalled by others. In summary, Open-pNovo is an excellent tool for open de novo sequencing and has great potential for discovering unexpected modifications in the real biological applications.
从头肽测序已经有了显著改进，但对具有意外修饰的全长肽进行测序仍然是一个具有挑战性的问题。在此，我们展示了一种开放的从头测序工具Open-pNovo，用于对具有任意类型修饰的肽进行从头测序。尽管搜索空间增加了约300倍，但Open-pNovo比其他三种提出的算法快近10倍甚至更快。此外，考虑三个MS/MS数据集上的排名第一的候选序列，Open-pNovo能够召回任何一种传统算法获得的结果的90%以上，并报告多5-87%的肽段，包括多14-250%的修饰肽段。在一个高质量的模拟数据集上，Open-pNovo可以召回约85%具有任意修饰的肽段，而其他方法几乎无法召回任何结果。总之，Open-pNovo是一种用于开放从头测序的优秀工具，在实际生物学应用中发现意外修饰具有巨大潜力。
