Peptidomics

Abstract

Peptides are biopolymers, typically consisting of 2-50 amino acids. They are biologically produced by the cellular ribosomal machinery or by non-ribosomal enzymes and, sometimes, other dedicated ligases. Peptides are arranged as linear chains or cycles, and include post-translational modifications, unusual amino acids and stabilizing motifs. Their structure and molecular size render them a unique chemical space, between small molecules and larger proteins. Peptides have important physiological functions as intrinsic signalling molecules, such as neuropeptides and peptide hormones, for cellular or interspecies communication, as toxins to catch prey or as defence molecules to fend off enemies and microorganisms. Clinically, they are gaining popularity as biomarkers or innovative therapeutics; to date there are more than 60 peptide drugs approved and more than 150 in clinical development. The emerging field of peptidomics comprises the comprehensive qualitative and quantitative analysis of the suite of peptides in a biological sample (endogenously produced, or exogenously administered as drugs). Peptidomics employs techniques of genomics, modern proteomics, state-of-the-art analytical chemistry and innovative computational biology, with a specialized set of tools. The complex biological matrices and often low abundance of analytes typically examined in peptidomics experiments require optimized sample preparation and isolation, including in silico analysis. This Primer covers the combination of techniques and workflows needed for peptide discovery and characterization and provides an overview of various biological and clinical applications of peptidomics.

Fig. 1. Peptides, from analysis to application.

The sources of peptides are vast, and many serve diverse biological roles. However, peptides are often difficult to detect without proper enrichment or clean-up strategies for use in downstream applications. Subsequent modification or derivatization of the peptides may be required to elucidate their sequence or structural features, which is essential for further design or use of discovered peptides in medical or industrial applications

Fig. 2. In silico peptide mining.

General workflow of how multi-omics approaches may be applied to peptidomics. a, Novel peptide candidates are often discovered in publicly available data sets, from genomics, transcriptomics or proteins. b, FASTA files are fed into the annotation platforms, where they utilize their own detection rule sets to predict the likelihood of true peptide candidates. c, Peptides derived from ribosomal transcription may undergo extensive modification, or non-ribosomal proteins may be responsible for peptide biosynthesis; as such, product prediction tools have been made to aid in novel peptide discovery. d, Finally, candidates validated by chemical analysis will be deposited in databases, increasing our collective knowledge and data amount to further improve algorithms and bioinformatics tools. sORF, short open reading frame.

Fig. 3. Overview of common peptidomics workflows.

a, De novo peptide sequencing is utilized in peptide discovery research. Purified peptides are analysed with matrix-assisted laser desorption/ionization time-of-flight/time-offlight mass spectrometry (MALDI-TOF/TOF-MS) or tandem mass spectrometry (MS/MS) systems. b, High-throughput peptidome analysis is often employed for automated sequence analysis, dereplication and/or spectral network analysis, quantitation and analysis for post-translational modifications. These analyses are usually paired with high-throughput analytical systems such as high-resolution liquid chromatography–tandem mass spectrometry (LC-MS/MS). Data acquisition generates MS/MS spectra that can be analysed with de novo sequencing algorithms or used for identification of peptides with a database search. Quantitation of peptides can be achieved at the level of the mass spectrometry peak as well as with multiple reaction monitoring acquisition. Post-translational modification analysis can be performed for tryptic peptides or for endogenous peptides using a peptide fragment spectrum for database search. c, Experimental protocol and data analysis procedure for mass spectrometry imaging (MSI). After sample preparation of tissue slices (for example, mouse brain tissue), the matrix is applied and co-registered with a tissue image to precisely define sample position in the mass spectrometer. The data acquisition over the entire raster generates a summed total ion chromatogram (TIC) spectrum. To visualize m/z maps, each m/z signal can be selected to show extracted ion chromatograms. The data can be processed (normalization, smoothing, data compression) for various biocomputational analysis. Scale bars, 200 μm. Part c data processing images reprinted from ref. , Springer Nature Limited.

Fig. 4. Quantitation methods for peptidomics.

Techniques include label-free, isotopic labelling and isobaric labelling quantitation. Label-free quantitation (left panel) is based on the mass spectrometry level and can be achieved through either spectral count or extracted ion chromatograms; labelled strategies can be achieved at both the mass spectrometry and tandem mass spectrometry (MS/MS) levels. Samples labelled with an isotopic labelling strategy (middle panel) can be quantified through the mass defect on the mass spectrometry level, whereas isobaric-labelled samples (right panel) are quantified on the MS/MS level. Bottom panel reprinted with permission from ref. , Wiley.

Fig. 5. From nature to medicine.

a, Generalized scheme of peptide sources and the peptidomics analysis pipeline. During structural elucidation, the complexity of spectral annotation will increase with the structural complexity of the peptides (for example, by post-translational modifications such as disulfide bond formation, cyclization). b, After peptide analysis, desired products are scaled up by chemical synthesis or synthetic biology (heterologous expression in various host organisms) for mode of action studies and pharmacological analysis. c, Optimization of the lead peptides (for example, insulin, bradykinin potentiating factor or vancomycin) by medicinal chemistry. Optimization is a multi-step process, including pharmacophore analysis, chemical synthesis, molecule library preparation and screening, structure–activity studies and pharmacology to generate optimized peptide or peptidomimetic drug candidates (for example, insulin detemir, captopril or telavancin) for applications in medicine. Structural alterations during lead to drug optimization are highlighted by pale yellow ovals (peptide backbones are coloured blue and red).