Biology subjects

Bergant, V.

Publications and source records attributed to Bergant, V..

4 recordsLinked to original sources

bioRxiv2025

Modanovo: A Unified Model for Post-Translational Modification-Aware de Novo Sequencing Using Experimental Spectra from In Vivo and Synthetic Peptides

Post-translational modifications (PTMs) play a central role in cellular regulation and are implicated in numerous diseases. Database searching remains the standard for identifying modified peptides from tandem mass spectra, but is hindered by the combinatorial expansion of modification types and sites. De novo peptide sequencing offers an attractive alternative, yet existing methods remain limited to unmodified peptides or a narrow set of PTMs. Here, we curated a large dataset of spectra from endogenous and synthetic peptides from ProteomeTools spanning 19 biologically relevant amino acid-PTM combinations, covering phosphorylation, acetylation, and ubiquitination. We used this dataset to develop Modanovo, an extension of the Casanovo transformer architecture for de novo peptide sequencing. Modanovo achieved robust performance across these amino acid-PTM combinations (median area under the precision-coverage curve 0.92), while maintaining performance on unmodified peptides (0.93), nearly identical to Casanovo (0.94). The model outperformed {pi}-PrimeNovo-PTM and showed increased precision and complementarity to the database search tool MSFragger. Robustness was confirmed across independent datasets, particularly at peptide lengths frequently represented in the curated dataset. Applied to a phosphoproteomics dataset from monkeypox virus-infected cells, Modanovo recovered numerous confident peptides not reported by database search, including new viral phosphosites supported by spectral evidence, thereby demonstrating its complementarity to database-driven identification approaches. These results establish Modanovo as a broadly applicable model for comprehensive de novo sequencing of both modified and unmodified peptides.

bioinformatics↗

bioRxiv2025

HCR-Proxy resolves site-specific proximal RNA proteomes at subcompartmental nanoscale resolution

The spatial organization of RNA condensates is fundamental for understanding of basic cellular functions, but may also provide pivotal insights into diseases. One of the major challenges to understanding the role of condensates is the lack of technologies to map condensate-scale protein architecture at subcompartmental or nanoscale resolution. To address this, we introduce HCR-Proxy, a proximity labelling technique that couples Hybridization Chain Reaction (HCR)-based signal amplification with in situ proximity biotinylation (Proxy), enabling proteomic profiling of RNA-proximal proteomes at subcompartmental resolution. We benchmarked HCR-Proxy using nascent pre-rRNA targets to investigate the distinct proteomic signatures of the nucleolar subcompartments and to uncover a spatial logic of protein partitioning shaped by RNA sequence. Our results demonstrate HCR-Proxys ability to provide spatially-resolved maps of RNA interactomes within the nucleolus, offering new insights into the molecular organisation and compartmentalisation of condensates. This subcompartment-specific nucleolar proteome profiling enabled integration with deep learning frameworks, which effectively confirmed a sequence-encoded basis for protein partitioning across nested condensate subcompartments, characterised by antagonistic gradients in charge, length, and RNA-binding domains. HCR-Proxy thus provides a scalable platform for spatially resolved RNA interactome discovery, bridging transcript localisation with proteomic context in native cellular environments. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=196 SRC="FIGDIR/small/656369v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@116015forg.highwire.dtl.DTLVardef@1b414f1org.highwire.dtl.DTLVardef@1caf0a1org.highwire.dtl.DTLVardef@7bf8b2_HPS_FORMAT_FIGEXP M_FIG C_FIG Bullet points- HCR-Proxy enables the first nanoscale-resolution mapping of RNA-proximal proteomes in situ. - HCR-Proxy establishes a broadly applicable modular platform for spatially resolved RNA- interactomics. - Subcompartmental proteomes are resolved across nucleolar subdomains by targeting specific pre-rRNA regions. - Deep learning confirms a sequence-encoded logic of protein partitioning within condensate subcompartments.

molecular biology↗

bioRxiv2021

Epiblast morphogenesis is controlled by selective mRNA decay triggered by LIN28A relocation

The embryonic progression from naive to primed pluripotency is accompanied by the rapid decay of pluripotency-associated mRNAs and a concomitant radical morphogenetic sequence of epiblast polarization, rosette formation and lumenogenesis. The mechanisms triggering and linking these events remain poorly understood. Guided by machine learning and metabolic RNA sequencing, we identified RNA binding proteins (RBPs), especially LIN28A, as primary mRNA decay factors. Using mRNA-RBP interactome capture, we revealed a dramatic increase in LIN28A mRNA binding during the naive-rosette-primed pluripotency transition, driven by its nucleolar-to-cytoplasmic translocation. Cytoplasmic LIN28A binds to 3UTRs of pluripotency-associated mRNAs to directly stimulate their decay and drive lumenogenesis. Accordingly, forced nuclear retention of LIN28A impeded lumenogenesis, impaired gastrulation, and caused an unforeseen embryonic multiplication. Selective mRNA decay, driven by nucleo-cytoplasmic RBP translocation, therefore acts as an intrinsic mechanism linking cell identity switches to the control of embryonic growth and morphogenesis.

developmental biology↗

bioRxiv2020

Multi-level proteomics reveals host-perturbation strategies of SARS-CoV-2 and SARS-CoV

The global emergence of SARS-CoV-2 urgently requires an in-depth understanding of molecular functions of viral proteins and their interactions with the host proteome. Several individual omics studies have extended our knowledge of COVID-19 pathophysiology1-10. Integration of such datasets to obtain a holistic view of virus-host interactions and to define the pathogenic properties of SARS-CoV-2 is limited by the heterogeneity of the experimental systems. We therefore conducted a concurrent multi-omics study of SARS-CoV-2 and SARS-CoV. Using state-of-the-art proteomics, we profiled the interactome of both viruses, as well as their influence on transcriptome, proteome, ubiquitinome and phosphoproteome in a lung-derived human cell line. Projecting these data onto the global network of cellular interactions revealed crosstalk between the perturbations taking place upon SARS-CoV-2 and SARS-CoV infections at different layers and identified unique and common molecular mechanisms of these closely related coronaviruses. The TGF-{beta} pathway, known for its involvement in tissue fibrosis, was specifically dysregulated by SARS-CoV-2 ORF8 and autophagy by SARS-CoV-2 ORF3. The extensive dataset (available at https://covinet.innatelab.org) highlights many hotspots that can be targeted by existing drugs and it can guide rational design of virus- and host-directed therapies, which we exemplify by identifying kinase and MMPs inhibitors with potent antiviral effects against SARS-CoV-2.

systems biology↗