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The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography–mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction models, accurate retention times cannot be predicted for modified peptides. This is especially problematic for fledgling open searches, which will benefit from accurate retention time prediction for modified peptides to reduce identification ambiguity. We present DeepLC, a deep learning peptide retention time predictor using peptide encoding based on atomic composition that allows the retention time of (previously unseen) modified peptides to be predicted accurately. We show that DeepLC performs similarly to current state-of-the-art approaches for unmodified peptides and, more importantly, accurately predicts retention times for modifications not seen during training. Moreover, we show that DeepLC’s ability to predict retention times for any modification enables potentially incorrect identifications to be flagged in an open search of a wide variety of proteome data.DeepLC, a deep learning-based peptide retention time predictor, can predict retention times for unmodified peptides as well as peptides with previously unseen modifications.

The placenta is the interface between mother and fetus and inadequate function contributes to short and long-term ill-health. The placenta is absent from most large-scale RNA-Seq datasets. We therefore analyze long and small RNAs (~101 and 20 million reads per sample respectively) from 302 human placentas, including 94 cases of preeclampsia (PE) and 56 cases of fetal growth restriction (FGR). The placental transcriptome has the seventh lowest complexity of 50 human tissues: 271 genes account for 50% of all reads. We identify multiple circular RNAs and validate 6 of these by Sanger sequencing across the back-splice junction. Using large-scale mass spectrometry datasets, we find strong evidence of peptides produced by translation of two circular RNAs. We also identify novel piRNAs which are clustered on Chr1 and Chr14. PE and FGR are associated with multiple and overlapping differences in mRNA, lincRNA and circRNA but fewer consistent differences in small RNAs. Of the three protein coding genes differentially expressed in both PE and FGR, one encodes a secreted protein FSTL3 (follistatin-like 3). Elevated serum levels of FSTL3 in pregnant women are predictive of subsequent PE and FGR. To aid visualization of our placenta transcriptome data, we develop a web application ( https://www.obgyn.cam.ac.uk/placentome/ ). Placental dysfunction can have catastrophic or barely discernible effects ranging from miscarriage to apparently normal birth. Here the authors present a comprehensive analysis of the human placental transcriptome and identify circular RNAs and piRNAs.

Public proteomics data often lack essential metadata, limiting its potential. To address this, we present lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond its initial publication. Public proteomics data often lack essential metadata, limiting their potential. To address this, the authors developed lesSDRF, a tool to simplify the process of metadata annotation, thereby ensuring that data leave a lasting, impactful legacy well beyond their initial publication.

A platform for isolating low-abundance protein complexes from Arabidopsis seedlings and cell cultures is described. Its power resides in an improved TAP tag combined with ultrasensitive MS and filtering against a list of nonspecific proteins. Tandem affinity purification coupled to mass spectrometry (TAP-MS) is one of the most advanced methods to characterize protein complexes in plants, giving a comprehensive view on the protein-protein interactions (PPIs) of a certain protein of interest (bait). The bait protein is fused to a double affinity tag, which consists of a protein G tag and a streptavidin-binding peptide separated by a very specific protease cleavage site, allowing highly specific protein complex isolation under near-physiological conditions. Implementation of this optimized TAP tag, combined with ultrasensitive MS, means that these experiments can be performed on small amounts (25 mg of total protein) of protein extracts from Arabidopsis cell suspension cultures. It is also possible to use this approach to isolate low abundant protein complexes from Arabidopsis seedlings, thus opening perspectives for the exploration of protein complexes in a plant developmental context. Next to protocols for efficient biomass generation of seedlings (∼7.5 months), we provide detailed protocols for TAP (1 d), and for sample preparation and liquid chromatography-tandem MS (LC-MS/MS; ∼5 d), either from Arabidopsis seedlings or from cell cultures. For the identification of specific co-purifying proteins, we use an extended protein database and filter against a list of nonspecific proteins on the basis of the occurrence of a co-purified protein among 543 TAP experiments. The value of the provided protocols is illustrated through numerous applications described in recent literature.

Listeria monocytogenes is a foodborne intracellular bacterial pathogen leading to human listeriosis. Despite a high mortality rate and increasing antibiotic resistance no clinically approved vaccine against Listeria is available. Attenuated Listeria strains offer protection and are tested as antitumor vaccine vectors, but would benefit from a better knowledge on immunodominant vector antigens. To identify novel antigens, we screen for Listeria peptides presented on the surface of infected human cell lines by mass spectrometry-based immunopeptidomics. In between more than 15,000 human self-peptides, we detect 68 Listeria immunopeptides from 42 different bacterial proteins, including several known antigens. Peptides presented on different cell lines are often derived from the same bacterial surface proteins, classifying these antigens as potential vaccine candidates. Encoding these highly presented antigens in lipid nanoparticle mRNA vaccine formulations results in specific CD8 +  T-cell responses and induces protection in vaccination challenge experiments in mice. Our results can serve as a starting point for the development of a clinical mRNA vaccine against Listeria and aid to improve attenuated Listeria vaccines and vectors, demonstrating the power of immunopeptidomics for next-generation bacterial vaccine development. Currently, no approved vaccines for Listeria monocytogenes are available. Here, the authors use immunopeptidomics to map bacterial peptides presented on infected cells and identify antigens that, as mRNA vaccine, provide protection in mice.

  A Brief History and Outline of the Group Start of the Lab: October 1, 2009 Size of the Lab: 15 Research Field: proteomics informatics The Computational Omics and Systems Biology (CompOmics) group was started in October 2009, when I left my position as PRIDE Group Coordinator at EMBL-EBI in Cambridge, UK for a tenured professor position at Ghent University and a group leader position at VIB, both in Ghent, Belgium. [...]newcomers are immediately considered part of the team, since the team decided to hire them.

Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are key targets for developing vaccines and immunotherapies against infectious pathogens or cancer cells. Identifying HLAIps is challenging due to their high diversity, low abundance, and patient individuality. Here, we develop a highly sensitive method for identifying HLAIps using liquid chromatography-ion mobility-tandem mass spectrometry (LC-IMS-MS/MS). In addition, we train a timsTOF-specific peak intensity MS 2 PIP model for tryptic and non-tryptic peptides and implement it in MS 2 Rescore (v3) together with the CCS predictor from ionmob. The optimized method, Thunder-DDA-PASEF, semi-selectively fragments singly and multiply charged HLAIps based on their IMS and m/z. Moreover, the method employs the high sensitivity mode and extended IMS resolution with fewer MS/MS frames (300 ms TIMS ramp, 3 MS/MS frames), doubling the coverage of immunopeptidomics analyses, compared to the proteomics-tailored DDA-PASEF (100 ms TIMS ramp, 10 MS/MS frames). Additionally, rescoring boosts the HLAIps identification by 41.7% to 33%, resulting in 5738 HLAIps from as little as one million JY cell equivalents, and 14,516 HLAIps from 20 million. This enables in-depth profiling of HLAIps from diverse human cell lines and human plasma. Finally, profiling JY and Raji cells transfected to express the SARS-CoV-2 spike protein results in 16 spike HLAIps, thirteen of which have been reported to elicit immune responses in human patients. Human leukocyte antigen (HLA) class I peptide ligands (HLAIps) are targets for developing vaccines and immunotherapies. Here the authors report Thunder-DDA-PASEF, an immunopeptidomics method which enhances the identification of vital HLAIps crucial for vaccine and immunotherapy development.

Accumulating evidence highlights the role of long non-coding RNAs (lncRNAs) in cellular homeostasis, and their dysregulation in disease settings. Most lncRNAs function by interacting with proteins or protein complexes. While several orthogonal methods have been developed to identify these proteins, each method has its inherent strengths and limitations. Here, we combine two RNA-centric methods ChIRP-MS and RNA-BioID to obtain a comprehensive list of proteins that interact with the well-known lncRNA HOTAIR. Overexpression of HOTAIR has been associated with a metastasis-promoting phenotype in various cancers. Although HOTAIR is known to bind with PRC2 and LSD1 protein complexes, only very limited unbiased comprehensive approaches to map its interactome have been performed. Both ChIRP-MS and RNA-BioID data sets show an association of HOTAIR with mitoribosomes, suggesting that HOTAIR has functions independent of its (post-)transcriptional mode-of-action.