Spectral clustering in peptidomics studies helps to unravel modification profile of biologically active peptides and enhances peptide identification rate

Abstract

When studying the set of biologically active peptides (the so-called peptidome) of a cell type, organ, or entire organism, the identification of peptides is mostly attempted by MS. However, identification rates are often dismally unsatisfactory. A great deal of failed or missed identifications may be attributable to the wealth of modifications on peptides, some of which may originate from in vivo post-translational processes to activate the molecule, whereas others could be introduced during the tissue preparation procedures. Preliminary knowledge of the modification profile of specific peptidome samples would greatly improve identification rates. To this end we developed an approach that performs clustering of mass spectra in a way that allows us to group spectra having similar peak patterns over significant segments. Comparing members of one spectral group enables us to assess the modifications (expressed as mass shifts in Dalton) present in a peptidome sample. The clustering algorithm in this study is called Bonanza, and it was applied to MALDI-TOF/TOF MS spectra from the mouse. Peptide identification rates went up from 17 to 36% for 278 spectra obtained from the pancreatic islets and from 21 to 43% for 163 pituitary spectra. Spectral clustering with subsequent advanced database search may result in the discovery of new biologically active peptides and modifications thereof, as shown by this report indeed.

Open MS/MS spectral library search to identify unanticipated post-translational modifications and increase spectral identification rate

“…For example, the Δ M of 12 Da detected in two datasets all occurred on peptide N-terms or basic amino acids. This modification is induced by formaldehyde (Toews et al , 2008), and has been recently detected in other datasets (Menschaert et al , 2009). Other detected PTMs include formylation (28 Da), acetylation (42 Da), methylation (14 Da), etc.…”

“…Ahrne et al (2009) proposed a workflow to combine open library search with sequence database search to increase spectral identification rate, but the library search engine they used was not deliberately designed for the open search mode. Besides, a spectral matching algorithm Bonanza is sometimes considered as an open library search tool (Falkner et al , 2008; Menschaert et al , 2009), but it was actually devised in a clustering framework and it is unknown whether the methods in it are directly applicable to general library search, such as the method for false discovery rate (FDR) control.…”

A survey of computational methods and error rate estimation procedures for peptide and protein identification in shotgun proteomics

“…One class of unrestrictive PTM search tools naturally builds on the concept of sequence tags [123, 137-139] reflecting the original (error-tolerant search) motivation behind that strategy [115]. Alternatively, the unrestrictive search can be conducted via spectrum to sequence alignment [140-143], spectral clustering [144, 145], peptide motif analysis [146, 147], or other methods [148, 149]. …”

The challenges of peptidomics in complementing proteomics in a clinical context

“…A very stringent approach, on the other hand, is to allow only bioactive peptides in a database that correspond to the study objective by only selecting those proteins that contain cleavage sites for known proteases and peptidases in the studied sample. Many fragment spectra will remain unidentified with this approach because the knowledge of processing patterns is very incomplete Spectra that originate from other types of peptides in the databases (e.g., by being modified or that have N or C‐terminal extensions) can be identified afterward by clustering methods (such as Bonanza Menschaert et al, ). In addition, the presence of a number of endogenous peptides encoded in unconventional coding regions such as short open‐reading frames are reported (Kondo et al, ; Ingolia et al, ; Hayakawa et al, ), making a comprehensive peptide database more difficult.…”

“…As such, these peptides carry post‐translational modifications (PTMs) to become biologically active or to improve stability. The most frequently observed PTMs of bioactive peptides are C‐terminal amidation, acetylation, pyroglutamate formation at the N‐terminus, and sulfatation (Boonen et al, ; Menschaert et al, ). Some of these PTMs hamper enzymatic degradation of peptides with peptidases and/or are required for biological activity.…”