MetaLP: An integrative linear programming method for protein inference in metaproteomics.
MetaLP: An integrative linear programming method for protein inference in metaproteomics.
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DOI：
10.1371/journal.pcbi.1010603
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发表时间：
2022-10
影响因子：
4.3
通讯作者：
Guo, Xuan
中科院分区：
文献类型：
--
作者：
Feng, Shichao;Ji, Hong-Long;Wang, Huan;Zhang, Bailu;Sterzenbach, Ryan;Pan, Chongle;Guo, Xuan
基于高通量串联质谱（MS/MS）的宏蛋白质组学在表征微生物组功能方面起着至关重要的作用。获取的MS/MS数据在蛋白质序列数据库中搜索以识别肽，然后用于推断元蛋白质组样本中存在的蛋白质列表。虽然蛋白质推断问题已经在单一生物的蛋白质组学中得到了很好的研究，但由于不同生物的同源蛋白之间共享大量的简并肽，因此对于复杂微生物群落来说，这仍然是一个主要的挑战。这一挑战需要改进对在宏蛋白质组学中鉴定的一组独特和退化肽的真蛋白鉴定和假蛋白鉴定的区分。MetaLP是在这里开发的蛋白质推断宏蛋白质组学使用综合线性规划方法。metagenomics shotgun测序或16s rRNA基因扩增子测序提取的分类丰度信息作为MetaLP的先验信息。对模拟、人类肠道、土壤和海洋微生物群落进行基准测试表明，MetaLP识别的蛋白质数量明显高于ProteinLP、PeptideProphet、DeepPep、PIPQ和Sipros Ensemble。综上所述，MetaLP通过将分类丰度信息整合到线性规划模型中，可以大大提高对复杂元蛋白质组的蛋白质推断。由于许多元蛋白质组数据库中普遍存在退化肽，因此从宏蛋白质组学中鉴定的肽推断出可靠的蛋白质列表是非常有意义的。简并肽在多种蛋白质中共享，因此不能唯一地归因于任何蛋白质。在这里，我们开发了一种蛋白质推断算法，MetaLP，用于微生物群落的鸟枪蛋白质组学分析，以更好地处理简并肽。MetaLP的两个关键创新是使用分类丰度作为先验信息和将蛋白质推理作为线性规划问题的公式。与许多现有的蛋白质推断算法相比，这些特征使MetaLP能够在复杂的元蛋白质组学数据集中产生更多的蛋白质鉴定。
Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model. Inferring a reliable list of proteins from identified peptides in metaproteomics is non-trivial because of the prevalence of degenerate peptides in many metaproteome databases. Degenerate peptides are shared among multiple proteins and, therefore, cannot be uniquely attributed to any protein. Here, we developed a protein inference algorithm, MetaLP, for shotgun proteomics analysis of microbial communities to better handle degenerate peptides. Two key innovations in MetaLP were the use of taxonomic abundances as prior information and the formulation of protein inference as a linear programming problem. These features enabled MetaLP to produce substantially more protein identifications in complex metaproteomic datasets than many existing protein inference algorithms.
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DOI：
10.1073/pnas.0907654107
发表时间：
2010-07-06
影响因子：
11.1
作者：
Gerster, Sarah;Qeli, Ermir;Buehlmann, Peter
通讯作者：
Buehlmann, Peter
影响因子：
9.9
作者：
Li, Jing;Zimmerman, Lisa J.;Park, Byung-Hoon;Tabb, David L.;Liebler, Daniel C.;Zhang, Bing
通讯作者：
Zhang, Bing
影响因子：
48
作者：
Langmead, Ben;Salzberg, Steven L.
通讯作者：
Salzberg, Steven L.
DOI：
10.1093/bioinformatics/btp461
发表时间：
2009-11-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Ramakrishnan SR;Vogel C;Kwon T;Penalva LO;Marcotte EM;Miranker DP
通讯作者：
Miranker DP
影响因子：
6.4
作者：
Bryson S;Li Z;Pett-Ridge J;Hettich RL;Mayali X;Pan C;Mueller RS
通讯作者：
Mueller RS