Method Development for Multiplexing CGE-LIF Based Glycoanalysis

Software Development for LC-MS/MS Based Metaproteomics


Metaproteomics represents the large scale characterization of the entire protein complement expressed by a microbial community at a given time point and under defined environmental conditions. This research area aimed at assessing the immediate catalytic potential of microbiota. However, the investigation of complex microbial communities by proteomic methods remains a challenging task as in contrast to pure-culture proteomics, metaproteome samples are heterogeneous and have a much higher complexity. Another problem constitutes the fact that only a small proportion of the microbial genome is sequenced, which leads to insufficient MS-based protein identification results. As a result a large quantity of fragmentation mass spectra remains unidentified due to the lack of appropriate protein sequence databases. Another important aspect is the functional analysis of complex microbial communities, as it constitutes an essential prerequisite for scientific endeavors such as improving the eco­nomic efficiency of biogas or waste water treatment plants, environ­ment­al remediation or investigating the complex microbial interactions in the human gut. 

Metaproteomics analysis software workflow.

Aim of the project

Mainly due to the lack of appropriate software solutions, the analysis and interpretation of data derived from LC-MS/MS experiments presented a major bottleneck in metaproteomics. In order to tackle the aforementioned challenges and overcome the limits of existing software solutions an improvement and adaptation of protein identification algorithms and database infrastructure was required. For that purpose, the software tool MetaProteomeAnalyzer (MPA) was developed, a client-server application tailored for the in-depth analysis of metaproteomics data that allows for the automated functional and taxonomic characterization of contained proteins in the samples.

MetaProteomeAnalyzer (MPA) main window.


Muth, T.; Kohrs, F.; Heyer, R.; Benndorf, D.; Rapp, E.; Reichl, U.; Martens, L.; Renard, B. Y.: MPA Portable: A Stand-Alone Software Package for Analyzing Metaproteome Samples on the Go. Analytical Chemistry 90, pp. 685 - 689 (2018)
Muth, T.; Behne, A.; Heyer, R.; Kohrs, F.; Benndorf, D.; Hoffmann, M.; Lehteva, M.; Reichl, U.; Martens, L.; Rapp, E.: The MetaProteomeAnalyzer: A Powerful Open-Source Software Suite for Metaproteomics Data Analysis and Interpretation. Journal of Proteome Research 14 (3), pp. 1557 - 1565 (2015)
Muth, T.; Kolmeder, C. A.; Salojärvi, J.; Keskitalo, S.; Varjosalo, M.; Verdam, F. J.; Rensen, S. S.; Reichl, U.; de Vos, W. M.; Rapp, E. et al.; Martens, L.: Navigating through metaproteomics data: A logbook of database searching. Proteomics 15 (20), pp. 3439 - 3453 (2015)
Muth, T.; Benndorf, D.; Reichl, U.; Rapp, E.; Martens, L.: Searching for a needle in a stack of needles: challenges in metaproteomics data analysis. Molecular BioSystems 9 (4), pp. 578 - 585 (2013)
Hanreich, A.; Schimpf, U.; Zakrzewski, M.; Schlüter, A.; Benndorf, D.; Heyer, R.; Rapp, E.; Puhler, A.; Reichl, U.; Klocke, M.: Metagenome and metaproteome analyses of microbial communities in mesophilic biogas-producing anaerobic batch fermentations indicate concerted plant carbohydrate degradation. Systematic and Applied Microbiology 36 (5), pp. 330 - 338 (2013)
Heyer, R.; Kohrs, F.; Benndorf, D.; Rapp, E.; Kausmann, R.; Heiermann, M.; Klocke, M.; Reichl, U.: Metaproteome analysis of the microbial communities in agricultural biogas plants. New biotechnology 30 (6), pp. 614 - 622 (2013)
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