Peptimetric: Quantifying and Visualizing Differences in Peptidomic Data

Abstract

Finding new sustainable means of diagnosing and treating diseases is one of the most pressing issues of our time. In recent years, several endogenous peptides have been found to be both excellent biomarkers for many diseases and to possess important physiological roles which may be utilized in treatments. The detection of peptides has been facilitated by the rapid development of biological mass spectrometry and now the combination of fast and sensitive high resolution MS instruments and stable nano HP-LC equipment sequences thousands of peptides in one single experiment. In most research conducted with these advanced systems, proteolytically cleaved proteins are analyzed and the specific peptides are identified by software dedicated for protein quantification using different proteomics workflows. Analysis of endogenous peptides with peptidomics workflows also benefit from the novel sensitive and advanced instrumentation, however, the generated peptidomic data is vast and subsequently laborious to visualize and examine, creating a bottleneck in the analysis. Therefore, we have created Peptimetric, an application designed to allow researchers to investigate and discover differences between peptidomic samples. Peptimetric allows the user to dynamically and interactively investigate the proteins, peptides, and some general characteristics of multiple samples, and is available as a web application at https://peptimetric.herokuapp.com. To illustrate the utility of Peptimetric, we've applied it to a peptidomic dataset of 15 urine samples from diabetic patients and corresponding data from healthy subjects.

Keywords: bioinformatics; biomarkers; mass spectrometry; peptidomics; proteomics; visualization.

FIGURE 1

An overview of the workflow performed in this study. The raw files, deposited by Van et al. (2020), were fetched from ProteomeXchange Consortium via the PRIDE partner repository, and searched against a human proteome database using PEAKS Xpro. In Peptimetric, the input files are matched against a local proteome database which was fetched from UniProt. Normalization and cutoffs are applied to the dataset. The data is visualized in a protein view, which in turn may be used to generate a peptide view. An overview of some general characteristics may also be generated for either the complete proteome or the selected protein.

FIGURE 2

Protein view. (A) Scatter plot containing all the proteins present in the samples. The size of the dots corresponds to the number of peptides derived from the precursor protein. The color corresponds to the distance from the line of equal abundance, i.e., the difference in abundance metric between the two groups. (B) Hovering over the insulin precursor protein results in the presented sample graph. Peptides are only present in non-diabetics. (C) Hovering over uromodulin generates the presented sample graphs. Uromodulin is varyingly present in both groups.

FIGURE 3

Peptide view of insulin precursor protein (INS_HUMAN) and uromodulin (UROM_HUMAN). (A) The C-peptide (57–87) from the insulin precursor protein is present in group 1 (non-diabetic). (B) There are two distinct regions present in uromodulin. The second region (430–460) contains the peptides UMOD1-UMOD7 and was documented by Van et al. [Van et al. (2020)].

FIGURE 4

General characteristics of the complete peptidomes. (A) The length distribution of peptides, weighted by the abundance metric. (B) Venn bars, showing the overlap of the peptidomes. (C) Amino acid profile, showing the amino acid prevalence for the complete sequence, as well as the first and last amino acid.