Binary Classifier for Computing Posterior Error Probabilities in MetaMorpheus

MetaMorpheus is a free, open-source software program for the identification of peptides and proteoforms from data-dependent acquisition tandem MS experiments. There is inherent uncertainty in these assignments for several reasons, including the limited overlap between experimental and theoretical peaks, the m/z uncertainty, and noise peaks or peaks from coisolated peptides that produce false matches. False discovery rates provide only a set-wise approximation for incorrect spectrum matches. Here we implemented a binary decision tree calculation within MetaMorpheus to compute a posterior error probability, which provides a measure of uncertainty for each peptide-spectrum match. We demonstrate its utility for increasing identifications and resolving ambiguities in bottom-up, top-down, proteogenomic, and nonspecific digestion searches.

Colon tumour secretopeptidome: insights into endogenous proteolytic cleavage events in the colon tumour microenvironment

The secretopeptidome comprises endogenous peptides derived from proteins secreted into the tumour microenvironment through classical and non-classical secretion. This study characterised the low-Mr (<3kDa) component of the human colon tumour (LIM1215, LIM1863) secretopeptidome, as a first step towards gaining insights into extracellular proteolytic cleavage events in the tumour microenvironment. Based on two biological replicates, this secretopeptidome isolation strategy utilised differential centrifugal ultrafiltration in combination with analytical RP-HPLC and nanoLC-MS/MS. Secreted peptides were identified using a combination of Mascot and post-processing analyses including MSPro re-scoring, extended feature sets and Percolator, resulting in 474 protein identifications from 1228 peptides (≤1% q-value, ≤5% PEP) - a 36% increase in peptide identifications when compared with conventional Mascot (homology ionscore thresholding). In both colon tumour models, 122 identified peptides were derived from 41 cell surface protein ectodomains, 23 peptides (12 proteins) from regulated intramembrane proteolysis (RIP), and 12 peptides (9 proteins) generated from intracellular domain proteolysis. Further analyses using the protease/substrate database MEROPS, (http://merops.sanger.ac.uk/), revealed 335 (71%) proteins classified as originating from classical/non-classical secretion, or the cell membrane. Of these, peptides were identified from 42 substrates in MEROPS with defined protease cleavage sites, while peptides generated from a further 205 substrates were fragmented by hitherto unknown proteases. A salient finding was the identification of peptides from 88 classical/non-classical secreted substrates in MEROPS, implicated in tumour progression and angiogenesis (FGFBP1, PLXDC2), cell-cell recognition and signalling (DDR1, GPA33), and tumour invasiveness and metastasis (MACC1, SMAGP); the nature of the proteases responsible for these proteolytic events is unknown. To confirm reproducibility of peptide fragment abundance in this study, we report the identification of a specific cleaved peptide fragment in the secretopeptidome from the colon-specific GPA33 antigen in 4/14 human CRC models. This improved secretopeptidome isolation and characterisation strategy has extended our understanding of endogenous peptides generated through proteolysis of classical/non-classical secreted proteins, extracellular proteolytic processing of cell surface membrane proteins, and peptides generated through RIP. The novel peptide cleavage site information in this study provides a useful first step in detailing proteolytic cleavage associated with tumourigenesis and the extracellular environment. This article is part of a Special Issue entitled: An Updated Secretome.