Strategies for Metagenomic-Guided Whole-Community Proteomics of Complex Microbial Environments

• Published in: PloS one Vol. 6; no. 11; p. e27173
• Main Authors: Cantarel, Brandi L., Erickson, Alison R., VerBerkmoes, Nathan C., Erickson, Brian K., Carey, Patricia A., Pan, Chongle, Shah, Manesh, Mongodin, Emmanuel F., Jansson, Janet K., Fraser-Liggett, Claire M., Hettich, Robert L.
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 23.11.2011; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Amino Acid Sequence; Bacteria - metabolism; BASIC BIOLOGICAL SCIENCES; Biology; Bioremediation; Chromatography; Computer applications; database and informatics methods; database searching; Databases, Protein; Datasets; Deoxyribonucleic acid; Dictionaries; DNA; DNA sequencing; Ecosystem; Enzymes; Female; gene prediction; Gene sequencing; Genomes; Genomics; Geobacter; Humans; Laboratories; Mass spectrometry; Mass spectroscopy; Metabolism; metagenomics; Metagenomics - methods; Microorganisms; Nucleotide sequence; Open reading frames; Peptides; Peptides - metabolism; Physics; Physiology; protein sequencing; Proteins; proteomic databases; proteomic sequencing; Proteomics; Proteomics - methods; Residence Characteristics; Scientific imaging; Sequence Analysis, Protein; sequence databases; Sequence Homology, Amino Acid; Spectroscopy; Studies; United States > US; Maryland; Tennessee; Baltimore Maryland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0027173

Accurate protein identification in large-scale proteomics experiments relies upon a detailed, accurate protein catalogue, which is derived from predictions of open reading frames based on genome sequence data. Integration of mass spectrometry-based proteomics data with computational proteome predictions from environmental metagenomic sequences has been challenging because of the variable overlap between proteomic datasets and corresponding short-read nucleotide sequence data. In this study, we have benchmarked several strategies for increasing microbial peptide spectral matching in metaproteomic datasets using protein predictions generated from matched metagenomic sequences from the same human fecal samples. Additionally, we investigated the impact of mass spectrometry-based filters (high mass accuracy, delta correlation), and de novo peptide sequencing on the number and robustness of peptide-spectrum assignments in these complex datasets. In summary, we find that high mass accuracy peptide measurements searched against non-assembled reads from DNA sequencing of the same samples significantly increased identifiable proteins without sacrificing accuracy.