Dataset Information

Analysis combined with fragment intensity predictions results in improved identification of classical bioactive peptides and sORF-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 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.

INSTRUMENT(S): timsTOF Pro

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Brain

SUBMITTER: Kurt Boonen

LAB HEAD: Kurt Boonen

PROVIDER: PXD026584 | Pride | 2021-10-11

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	sORFs_P1_brain_a_1_1_1104.d.zip	Other
	sORFs_P1_brain_a_1_1_1104.mgf	Mgf
	sORFs_P1_brain_a_1_1_1104.mzid.gz	Mzid
	sORFs_P1_brain_b_1_1_1105.d.zip	Other
	sORFs_P1_brain_b_1_1_1105.mgf	Mgf

Items per page:

1 - 5 of 45

Similar Datasets

Project description:Non-proteinogenic amino Q7 Q8 acids (npAAs) are valuable building blocks for the development of advanced pharmaceuticals and agrochemicals. The surge in interest in their synthesis is primarily due to the potential to enhance and diversify existing bioactive molecules. This can be achieved by altering these bioactive molecules to improve their effectiveness, reducing resistance compared to their natural counterparts or generating molecules with novel functions. Traditional production of npAAs in native hosts requires specialized conditions and complex cultivation media. Furthermore, these compounds are often found in organisms that challenge genetic manipulation. Thus, the recombinant production of these npAAs in a model organism like Escherichia coli paves the way for groundbreaking advancements in synthetic biology. For the first time, we report the synthesis of npAAs β-methylphenylalanine (BmePhe), β- hydroxyenduracididine (BhEnd), and enduracididine (End) from Streptomyces hygroscopicus in E. coli. Through proteomic and metabolomic analyses, we achieved the production of these compounds from two synthetic operons, comprising five heterologous proteins. Interestingly, we discovered that the exogenous addition of pathway precursors to the E. coli system enhanced the yield of BmePhe by 2.5 times, whereas it concurrently attenuated the production of BhEnd and End, signifying a selective precursor-dependent yield enhancement. The synthetic biology landscape is broadened in this study by expanding the repertoire of amino acids beyond the conventional set of 22 standard proteinogenic amino acids. The biosynthesized npAAs, End, BhEnd, and BmePhe hold promise for engineering proteins with modified functions, integrating into novel metabolites and/or enhancing biological stability and activity. Additionally, these amino acids’ biological production and subsequent purification present an alternative to traditional chemical synthesis methods, paving a direct pathway for pharmacological evaluation.

Dataset Information

Analysis combined with fragment intensity predictions results in improved identification of classical bioactive peptides and sORF-encoded peptides

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets