strategies for metagenomic-guided whole-community proteomics of complex microbial environments策略metagenomic-guided整个社区蛋白质组学复杂的微生物环境.pdf

Strategies for Metagenomic-Guided Whole-Community Proteomics of Complex Microbial Environments 1. 2,3. 2 2,3 Brandi L. Cantarel , Alison R. Erickson , Nathan C. VerBerkmoes , Brian K. Erickson , Patricia A. 2 2 2 1 4 Carey , Chongle Pan , Manesh Shah , Emmanuel F. Mongodin , Janet K. Jansson , Claire M. Fraser- 1 2 Liggett , Robert L. Hettich * 1 Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 2 Oak Ridge National Laboratory, Chemical Sciences Division, Oak Ridge, Tennessee, United States of America, 3 Graduate School of Genome Science Technology, University of Tennessee, Knoxville, Tennessee, United States of America, 4 Lawrence Berkeley National Laboratory, Earth Sciences Division, Department of Ecology, Berkeley, California, United States of America Abstract 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 predictio