Combining in silico prediction and ribosome profiling in a genome-wide search for novel putatively coding sORFs

Background: It was long assumed that proteins are at least 100 amino acids (AAs) long. Moreover, the detection of short translation products (e. g. coded from small Open Reading Frames, sORFs) is very difficult as the short length makes it hard to distinguish true coding ORFs from ORFs occurring by chance. Nevertheless, over the past few years many such non-canonical genes (with ORFs < 100 AAs) have been discovered in different organisms like Arabidopsis thaliana, Saccharomyces cerevisiae, and Drosophila melanogaster. Thanks to advances in sequencing, bioinformatics and computing power, it is now possible to scan the genome in unprecedented scrutiny, for example in a search of this type of small ORFs. Results: Using bioinformatics methods, we performed a systematic search for putatively functional sORFs in the Mus musculus genome. A genome-wide scan detected all sORFs which were subsequently analyzed for their coding potential, based on evolutionary conservation at the AA level, and ranked using a Support Vector Machine (SVM) learning model. The ranked sORFs are finally overlapped with ribosome profiling data, hinting to sORF translation. All candidates are visually inspected using an in-house developed genome browser. In this way dozens of highly conserved sORFs, targeted by ribosomes were identified in the mouse genome, putatively encoding micropeptides. Conclusion: Our combined genome-wide approach leads to the prediction of a comprehensive but manageable set of putatively coding sORFs, a very important first step towards the identification of a new class of bioactive peptides, called micropeptides

A Pattern Search Method for Discovering Conserved Motifs in Bioactive Peptide Families

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Mass Spectrometry (Imaging) for Detection and Identification of Cyclic AMPs: Focus on Human Neutrophil Peptides (HNPs)

Antimicrobial peptides (AMPs) are known best for their role in innate immunity against bacteria, viruses, parasites and fungi. However, not only are they showing increasing promise as potential antimicrobial drug candidates, recently, it has been reported that certain AMPs also show a cytotoxic effect against cancer cells. Their possible antitumor effect could make AMPs interesting candidate cancer biomarkers and a possible lead for new anticancer therapy. Due to their cyclic structure, detection and identification of AMPs is challenging, however, mass spectrometry (imaging; MSI) has been shown as a powerful tool for visualization and identification of (unknown) cyclic AMPs. In this chapter, we will discuss how mass spectrometry (imaging), combined with the use of electron-transfer dissociation (ETD) as fragmentation technique, can be used as a reliable method to identify AMPs in their native cyclic state. Using this approach, we have previously detected and identified human neutrophil peptides (HNPs) as important AMPs in cancer, of which a detailed bacterial, viral and cancer-related overview will be presented

Ethylene Receptors, CTRs and EIN2 Target Protein Identification and Quantification Through Parallel Reaction Monitoring During Tomato Fruit Ripening.

Ethylene, the plant ripening hormone of climacteric fruit, is perceived by ethylene receptors which is the first step in the complex ethylene signal transduction pathway. Much progress has been made in elucidating the mechanism of this pathway, but there is still a lot to be done in the proteomic quantification of the main proteins involved, particularly during fruit ripening. This work focuses on the mass spectrometry based identification and quantification of the ethylene receptors (ETRs) and the downstream components of the pathway, CTR-like proteins (CTRs) and ETHYLENE INSENSITIVE 2 (EIN2). We used tomato as a model fruit to study changes in protein abundance involved in the ethylene signal transduction during fruit ripening. In order to detect and quantify these low abundant proteins located in the membrane of the endoplasmic reticulum, we developed a workflow comprising sample fractionation and MS analysis using parallel reaction monitoring. This work shows the feasibility of the identification and absolute quantification of all seven ethylene receptors, three out of four CTRs and EIN2 in four ripening stages of tomato. In parallel, gene expression was analyzed through real-time qPCR. Correlation between transcriptomic and proteomic profiles during ripening was only observed for three of the studied proteins, suggesting that the other signaling proteins are likely post-transcriptionally regulated. Based on our quantification results we were able to show that the protein levels of SlETR3 and SlETR4 increased during ripening, probably to control ethylene sensitivity. The other receptors and CTRs showed either stable levels that could sustain, or decreasing levels that could promote fruit ripening

MIND: A Double-Linear Model To Accurately Determine Monoisotopic Precursor Mass in High-Resolution Top-Down Proteomics

Top-down proteomics approaches are becoming ever more popular, due to the advantages offered by knowledge of the intact protein mass in correctly identifying the various proteoforms that potentially arise due to point mutation, alternative splicing, post-translational modifications, etc. Usually, the average mass is used in this context; however, it is known that this can fluctuate significantly due to both natural and technical causes. Ideally, one would prefer to use the monoisotopic precursor mass, but this falls below the detection limit for all but the smallest proteins. Methods that predict the monoisotopic mass based on the average mass are potentially affected by imprecisions associated with the average mass. To address this issue, we have developed a framework based on simple, linear models that allows prediction of the monoisotopic mass based on the exact mass of the most-abundant (aggregated) isotope peak, which is a robust measure of mass, insensitive to the aforementioned natural and technical causes. This linear model was tested experimentally, as well as in silico, and typically predicts monoisotopic masses with an accuracy of only a few parts per million. A confidence measure is associated with the predicted monoisotopic mass to handle the off-by-one-Da prediction error. Furthermore, we introduce a correction function to extract the “true” (i.e., theoretically) most-abundant isotope peak from a spectrum, even if the observed isotope distribution is distorted by noise or poor ion statistics. The method is available online as an R shiny app: https://valkenborg-lab.shinyapps.io/mind

Peptidomics in Drosophila melanogaster

In analogy with proteomics technology, where all proteins expressed in a cell or tissue are analysed, the peptidomic approach aims at the simultaneous visualisation and identification of the whole peptidome of a cell or tissue, ie all expressed peptides with their post-translational modifications. With nanoscale liquid chromatography (nanoLC), combined with mass spectrometry and subsequent database searching, the peptidome of the Drosophila larval brain has been identified at the amino acid sequence level. In a single experiment involving only 50 Drosophila larval brains, one can obtain a display of the expressed peptides. In this paper, current peptidomics technology will be explained, using Drosophila as an example. Compared with the 400,000 Drosophila whole bodies that were required as a starting material for traditional biochemical peptide purification rounds, the authors are convinced that peptidomics technology, which in the future will certainly be applied to the analysis of different physiological states, has the inherent potential to bring about a true revolution in the study of the molecular physiology of Drosophila.status: publishe

Uncovering conserved patterns in bioactive peptides in Metazoa

Bioactive (neuro)peptides play critical roles in regulating most biological processes in animals. Peptides belonging to the same family are characterized by a typical sequence pattern that is conserved among the family's peptide members. Such a conserved pattern or motif usually corresponds to the functionally important part of the biologically active peptide. In this paper, all known bioactive (neuro)peptides annotated in Swiss-Prot and TrEMBL protein databases are collected, and the pattern searching program Pratt is used to search these unaligned peptide sequences for conserved patterns. The obtained patterns are then refined by combining the information on amino acids at important functional sites collected from the literature. All the identified patterns are further tested by scanning them against Swiss-Prot and TrEMBL protein databases. The diagnostic power of each pattern is validated by the fact that any annotated protein from Swiss-Prot and TrEMBL that contains one of the established patterns, is indeed a known (neuro)peptide precursor. We discovered 155 novel peptide patterns in addition to the 56 established ones in the PROSITE database. All the patterns cover 110 peptide families. Fifty-five of these families are not characterized by the PROSITE signatures, and 12 are also not identified by other existing motif databases, such as Pfam and SMART. Using the newly identified peptide signatures as a search tool, we predicted 95 hypothetical proteins as putative peptide precursors. (c) 2006 Elsevier Inc. All rights reserved.status: publishe

The Construction of a Bioactive Peptide Database in Metazoa

Bioactive peptides play critical roles in regulating most biological processes in animals, and have considerable biological, medical and industrial importance. A number of peptides have been discovered usually based on their biological activities in vitro or based on their sequence similarities in silico. Through searches in Swiss-Prot and Trembl protein databases using BLAST alignment tools and other in silico methods, all currently known bioactive peptides and their precursor proteins are extracted. In addition, 132 recently discovered putative peptide genes in Drosophila as well as their orthologs in other species are collected. In total, 20 027 bioactive peptides from 19 438 precursor proteins covering 2820 metazoan species are retained, and they, respectively, make up a peptide and a peptide precursor database. The peptides and peptide precursor proteins are further classified into 373 families, 178 of which are represented by Prosite Pfam or Smart motifs, or by typical peptide motifs that have been constructed recently. The remaining 195 families are novel peptide families. The motifs characterizing the 178 peptide families are saved into a peptide motif database. The peptide, peptide precursor and peptide motif databases (version 1.0) are the most complete peptide, precursor and peptide motif collection in Metazoa so far. They are available on the WWW at http://www.peptides.be/.status: publishe