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Merging full-spectrum and fragment ion intensity predictions from deep learning for high quality spectral libraries

ABSTRACT: In this work, we present a deep learning based full-spectrum prediction method and demonstrate the merits of using full-spectrum predicted approaches for library searching. Our proposed model provides flexibility to accommodate post-translational modifications and fills the current gap for long peptide predictions.

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Whole Body

SUBMITTER: Chak Ming Jerry Chan

LAB HEAD: Henry Lam

PROVIDER: PXD040721 | Pride | 2023-06-28

REPOSITORIES: Pride

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Project description: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 bio-active 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 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 towards improved identification of 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 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, we identified 48 sORF-encoded peptides originating from presumed non-coding locations, next to identifying 66 known neuropeptides from within 22 different families. Altogether, this robust pipeline fuses technological advancements from different fields ensuring an improved coverage of the neuropeptidome in the mouse brain.

Dataset Information

Merging full-spectrum and fragment ion intensity predictions from deep learning for high quality spectral libraries

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