An Alignment-Free “Metapeptide” Strategy for Metaproteomic Characterization of Microbiome Samples Using Shotgun Metagenomic Sequencing

Abstract

In principle, tandem mass spectrometry can be used to detect and quantify the peptides present in a microbiome sample, enabling functional and taxonomic insight into microbiome metabolic activity. However, the phylogenetic diversity constituting a particular microbiome is often unknown, and many of the organisms present may not have assembled genomes. In ocean microbiome samples, with particularly diverse and uncultured bacterial communities, it is difficult to construct protein databases that contain the bulk of the peptides in the sample without losing detection sensitivity due to the overwhelming number of candidate peptides for each tandem mass spectrum. We describe a method for deriving “metapeptides” (short amino acid sequences that may be represented in multiple organisms) from shotgun metagenomic sequencing of microbiome samples. In two ocean microbiome samples, we constructed site-specific metapeptide databases to detect more than one and a half times as many peptides as by searching against predicted genes from an assembled metagenome, and more than three times as many peptides as by searching against the NCBI environmental proteome database. The increased peptide yield can be used to enrich the taxonomic characterization of sample metaproteomes.

A robust, cost‐effective method for DNA, RNA and protein co‐extraction from soil, other complex microbiomes and pure cultures

“…This is most likely due to insufficient availability of soil‐relevant proteomic databases, but also to the well‐documented interference of humic substances with soil‐extracted proteins (Arenella et al, ; Bastida, Moreno, Nicols, Hernandez, & Garcia, ). The use of a matched metagenome, allowing for the “meta‐peptide” approach detailed by May et al (), would surely have improved the identification rate (Tang, Li, & Ye, ); however, this was beyond the scope of this work. To reduce data redundancy, protein hits were binned into metaproteins using MPA (with the peptide rule of at least on peptide in common), and a total of 2,212 metaproteins were identified across the nine metaproteomes (Supporting information Table S5).…”

“…The use of a matched metagenome, allowing for the "meta-peptide" approach detailed by May et al (2016), would surely have improved the identification rate (Tang, Li, & Ye, 2016); however, this was beyond the scope of this work. To reduce data redundancy, protein hits were binned into metaproteins using MPA (with the peptide rule of at least on peptide in common), and a total of 2,212 metaproteins were identified across the nine metaproteomes (Supporting information Table S5).…”

A sectioning and database enrichment approach for improved peptide spectrum matching in large, genome-guided protein sequence databases

“…For metaproteomics studies of complex communities of microorganisms, wherein a reference protein sequence database is either not available or is incomplete, data-processing methods need to be employed to generate a customized database. For example, metagenomic and/or metatranscriptomic sequencing data from same or related samples can be used to generate a customized database of proteins that may be expressed by the community [20][21][22][23][24] . For such an approach, methods have been described for protein sequence database generation such as SixGill 20 , MOCAT 21,22 and Graph2Pro 23,24 .…”

Combining high resolution and exact calibration to boost statistical power: A well-calibrated score function for high-resolution MS2 data

“…The database used in the database search consists of a metapeptide database that was derived from shotgun metagenomic sequencing of the same ocean sample (https://noble.gs.washington.edu/proj/metapeptide/metapeptidesCS.fasta). Briefly, a metapeptide database is a peptide database whose sequences are derived from raw read sequences that have been translated into peptides in all six reading frames (14).…”