Biology subjects

Zuallaert, J.

Publications and source records attributed to Zuallaert, J..

4 recordsLinked to original sources

bioRxiv2025

The E. coli PeptideAtlas Build: Characterizing the observed Escherichia coli pan-proteome and its post-translational modifications

Escherichia coli is a widely used model organism in molecular biology. Despite its pivotal role, a comprehensive proteome resource covering the E. coli pan-proteome and its post-translational modifications (PTMs) has been lacking. Here we present the E. coli PeptideAtlas build, the first comprehensive pan-proteome analysis of E. coli, generated from 40 high-quality public and in-house datasets spanning a broad diversity of strains, sample types, and experimental conditions, and comprising over 73 million MS/MS spectra. All datasets were reprocessed using both a closed search (Trans-Proteomic Pipeline using MSFragger) and an open search (ionbot). The E. coli PeptideAtlas build provides evidence for 4,755 proteins, including 1,410 previously lacking protein-level support in UniProt. The resource offers protein coverage, modification sites, raw spectra with matched peptides, and manually annotated metadata for the E. coli pan proteome. PTM profiling identified over 10,000 modification sites, including phosphorylation (3,806), acetylation (754), methylation (730), glutathionylation (352) and phosphoribosylation (226). Analysis of the glutathionylation sites revealed potential links to metal binding regulation. We also detected proteins likely associated with phages, underscoring the value of pan-proteomic approaches for studying host-phage interactions. All identifications are publicly accessible and traceable through the PeptideAtlas interface. We expect that the E. coli PeptideAtlas build will provide a useful resource for the community, which supports, for example, targeted MS experiment design, PTM enrichment method development, and strain typing. It allows straightforward lookups of protein and peptide identifications and facilitates comparative proteomic analyses by enabling the assessment of protein presence and variability across different E. coli strains. The build is available at https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/buildDetails?atlas_build_id=585.

bioinformatics↗

bioRxiv2025

Unraveling the cis-regulatory code controlling abscisic acid-dependent gene expression in Arabidopsis using deep learning

Abscisic acid (ABA) is a key regulator of abiotic stress responses in plants. Understanding the regulation of ABA-dependent gene expression is key to uncovering how plants adapt to environmental stress and how their resilience can be improved. We explored gene expression regulation by ABA in Arabidopsis thaliana through the training of an interpretable deep learning model predicting ABA responsiveness in the root from proximal promoter sequences. Implementing state-of-the-art augmentation strategies to boost performance, our convolutional neural network-based model was able to confidently predict ABA responsiveness. We demonstrate that it learned actual motifs in the promoter sequences and confirm that ABRE-binding factor (ABF) binding sites play a key role in regulating ABA-dependent gene expression. However, also other motifs contributing to ABA-mediated gene expression regulation were identified. Our model outperforms a model trained to predict ABF binding, indicating it successfully learned the cis-regulatory code beyond the canonical ABF binding sites. Furthermore, the importance of motif clustering for regulating gene expression levels in response to ABA was unveiled by our model. Lastly, we identified genomic regions-beyond the proximal promoter-both with and without ABF binding sites, predicted by our model to drive ABA responsiveness. These genomic regions were used to generate reporter lines for experimental validation and were shown to drive the response to ABA in planta. This confirms that our model successfully inferred the regulatory code controlling ABA-dependent gene expression.

plant biology↗

bioRxiv2025

Assessing the relation between protein phosphorylation, AlphaFold3 models and conformational variability

Proteins perform diverse functions critical to cellular processes. Transitions between functional states are often regulated by post-translational modifications (PTMs) such as phosphorylation, which dynamically influence protein structure, function, folding, and interactions. Dysregulation of PTMs can therefore contribute to diseases such as cancer and Alzheimers. However, the structure-function relationship between proteins and their modifications remains poorly understood due to a lack of experimental structural data, the inherent diversity of PTMs, and the dynamic nature of proteins. Recent advances in deep learning, particularly AlphaFold, have transformed protein structure prediction with near-experimental accuracy. However, it remains unclear whether these models can effectively capture PTM-driven conformational changes, such as those induced by phosphorylation. Here, we systematically evaluated AlphaFold models (AF2, AF3-non phospho, and AF3-phospho) to assess their ability to predict phosphorylation-induced structural diversity. By analysing experimentally derived conformational ensembles, we found that all models predominantly aligned with dominant structural states, often failing to capture phosphorylation-specific conformations. Despite its phosphorylation-aware design, AF3-phospho predictions provided only modest improvement over AF2 and AF3-non phospho predictions. Our findings highlight key challenges in modelling PTM-driven structural landscapes and underscore the need for more adaptable structure prediction frameworks capable of capturing modification-induced conformational variability.

bioinformatics↗

bioRxiv2022

PhosphoLingo: protein language models for phosphorylation site prediction

Protein post-translational modifications (PTMs) play an important role in numerous biological processes by significantly affecting protein structure and dynamics. Effective computational methods that provide a sequence-based prediction of PTM sites are desirable to guide functional experiments. Whereas these methods typically train neural networks on one-hot encoded amino acid sequences, protein language models carry higher-level pattern information that may improve sequence based prediction performance and hence constitute the current edge of the field. In this study, we first evaluate the training of convolutional neural networks on top of various protein language models for sequence based PTM prediction. Our results show substantial prediction accuracy improvements for various PTMs with current procedures of dataset compilation and model performance evaluation. We then used model interpretation methods to study what these advanced models actually base their learning on. Importantly for the entire field of PTM site predictors trained on proteomics-derived data, our model interpretation and transferability experiments reveal that the current approach to compile training datasets based on proteomics data leads to an artefactual protease-specific training bias that is exploited by the prediction models. This results in an overly optimistic estimation of prediction accuracy, an important caveat in the application of advanced machine learning approaches to PTM prediction based on proteomics data. We suggest a partial solution to reduce this data bias by implementing negative sample filtering, only allowing candidate PTM sites in matched peptides that are present in the experimental metadata. Availability and implementationThe prediction tool, with training and evaluation code, trained models, datasets, and predictions for various PTMs are available at https://github.com/jasperzuallaert/PhosphoLingo. Contactsven.degroeve@vib-ugent.be and nico.callewaert@vib-ugent.be Supplementary informationSupplementary materials are available at bioRxiv.

bioinformatics↗