Ion Mobility Coupled to a Time-of-Flight Mass Analyzer Combined With Fragment Intensity Predictions Improves Identification of Classical Bioactive Peptides and Small Open Reading Frame-Encoded Peptides

Abstract

Bioactive peptides exhibit key roles in a wide variety of complex processes, such as regulation of body weight, learning, aging, and innate immune response. Next to the classical bioactive peptides, emerging from larger precursor proteins by specific proteolytic processing, a new class of peptides originating from small open reading frames (sORFs) have been recognized as important biological regulators. But their intrinsic properties, specific expression pattern and location on presumed non-coding regions have hindered the full characterization of the repertoire of bioactive peptides, despite their predominant role in various pathways. Although the development of peptidomics has offered the opportunity to study these peptides in vivo, it remains challenging to identify the full peptidome as the lack of cleavage enzyme specification and large search space complicates conventional database search approaches. In this study, we introduce a proteogenomics methodology using a new type of mass spectrometry instrument and the implementation of machine learning tools toward improved identification of potential bioactive peptides in the mouse brain. The application of trapped ion mobility spectrometry (tims) coupled to a time-of-flight mass analyzer (TOF) offers improved sensitivity, an enhanced peptide coverage, reduction in chemical noise and the reduced occurrence of chimeric spectra. Subsequent machine learning tools MS2PIP, predicting fragment ion intensities and DeepLC, predicting retention times, improve the database searching based on a large and comprehensive custom database containing both sORFs and alternative ORFs. Finally, the identification of peptides is further enhanced by applying the post-processing semi-supervised learning tool Percolator. Applying this workflow, the first peptidomics workflow combined with spectral intensity and retention time predictions, we identified a total of 167 predicted sORF-encoded peptides, of which 48 originating from presumed non-coding locations, next to 401 peptides from known neuropeptide precursors, linked to 66 annotated bioactive neuropeptides from within 22 different families. Additional PEAKS analysis expanded the pool of SEPs on presumed non-coding locations to 84, while an additional 204 peptides completed the list of peptides from neuropeptide precursors. Altogether, this study provides insights into a new robust pipeline that fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.

Proteomics‐driven identification of short open reading frame‐encoded peptides

“…With the development of high-throughput sequencing technology, growing evidence has shown that noncoding RNA (ncRNA) contains a small open reading frame (sORF), which can encode microproteins with less than 100 amino acids (AAs) [4]. Studies have shown that numerous species genomes, such as Drosophila (Drosophila melanogaster) [5][6][7], mouse (Mus musculus) [8,9], and human (Homo sapiens) [10][11][12][13][14], contain millions of sORFs. The discovery of these sORF-encoded peptides (SEPs) is an expansion of the proteome and genome.…”

Clinical Peptidomics: Advances in Instrumentation, Analyses, and Applications

“…The arrival time of an ion at the end of the IM separation path increases with the ion’s mass and collisional cross section (CCS), with the gas phase used in this procedure, so that the CCS value of each ion reflects its rotationally averaged cross-sectional area. Ions with smaller CCS values traverse the IM separation path faster than those with larger CCS values, adding another separation dimension beyond mass and charge to ion identifications [ 41 ]. For example, IM techniques (e.g., differential mobility spectrometry and trapped IM spectrometry) have been employed to identify and monitor isomeric peptides and improve the signal-to-noise ratio of targeted analytes without MS 2 or MS n analyses [ 42 , 43 ].…”

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0

“…4 In recent years, it has been repeatedly shown that extending this method by adding information from machine learning-based predictions of peptide LC-MS/MS behavior further improves peptide identification strategies. 5−7 This is especially true for more challenging proteomics workflows, such as immunopeptidomics, 8−11 proteogenomics, 12 biopeptidomics, 13 and metaproteomics. 7,14 Unfortunately, implementing these advanced methods into routine proteomics data analysis workflows has remained difficult.…”

“…By including additional information, such as peptide length, charge, missed cleavages, and mass accuracy, a dynamic scoring function is generated to better separate putatively correct and incorrect identifications with machine learning . In recent years, it has been repeatedly shown that extending this method by adding information from machine learning-based predictions of peptide LC-MS/MS behavior further improves peptide identification strategies. − This is especially true for more challenging proteomics workflows, such as immunopeptidomics, − proteogenomics, biopeptidomics, and metaproteomics. , Unfortunately, implementing these advanced methods into routine proteomics data analysis workflows has remained difficult. Indeed, data-driven rescoring tools are often difficult to use due to complex installation procedures, specialized hardware requirements, the use of custom file formats, or even poorly documented code.…”