Biology subjects

Thomes, L.

Publications and source records attributed to Thomes, L..

6 recordsLinked to original sources

bioRxiv2025

GlyContact reveals structure-function relationships in glycans

Glycans are branched, structurally diverse, and highly flexible biomolecules. These characteristics make glycoanalytics and glycan structural characterization challenging, resulting in often unclear structure-to-function relationships. The creation of GlycoShape, currently the largest open-access (OA) database of glycan 3D structures from molecular dynamics (MD) simulations, provides an opportunity to fill this information gap. Here, we present GlyContact, an open-source Python package designed and developed to retrieve, process, and analyze glycan 3D structures, from MD, NMR, or X-ray crystallography. We demonstrate that this platform can (i) unveil the impact of sequence context on glycan motif structure, (ii) yield a predictive understanding of motif flexibility and surface accessibility on lectin-glycan binding, which improves state-of-the-art AI models for this task, and (iii) accurately predict torsion angles of multiple conformers via new AI models. We envision that GlyContact will allow researchers to explore glycan structures within their 3D space, obtaining new insights into their biological functions. GlyContact is available OA at https://github.com/lthomes/glycontact.

bioinformatics↗

bioRxiv2024

Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis

Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.

bioinformatics↗

bioRxiv2023

GlycoDraw: A Python Implementation for Generating High-Quality Glycan Figures

Glycans are essential to all scales of biology, with their intricate structures being crucial for their biological functions. The structural complexity of glycans is communicated through simplified and unified visual representations according to the Symbol Nomenclature for Glycans (SNFG) guidelines adopted by the community. Here, we introduce GlycoDraw, a Python-native implementation for high-throughput generation of high-quality, SNFG-compliant glycan figures with flexible display options. GlycoDraw is released as part of our glycan analysis ecosystem, glycowork, facilitating integration into existing workflows by enabling fully automated annotation of glycan-related figures and thus assisting the analysis of e.g., differential abundance data or glycomics mass spectra.

bioinformatics↗

bioRxiv2023

Mammalian Milk Glycomes: Connecting the Dots between Evolutionary Conservation and Biosynthetic Pathways

Milk oligosaccharides (MOs) are among the most abundant constituents of breast milk and are essential for health and development. Biosynthesized from monosaccharides into complex sequences, MOs differ considerably between taxonomic groups. Even human MO biosynthesis is insufficiently understood, hampering evolutionary and functional analyses. Using a comprehensive resource of all published MOs from >100 mammals, we develop a nonparametric pipeline for generating and analyzing MO biosynthetic networks, which readily generalizes to other glycan classes. We then use evolutionary relationships and inferred intermediates of these networks to discover (i) distributional glycome biases, (ii) biosynthetic restrictions, such as reaction path dependence, and (iii) conserved biosynthetic modules. This allows us to prune and pinpoint biosynthetic pathways despite missing information. Machine learning and network analysis cluster species by their milk glycome, identifying characteristic sequence relationships and evolutionary gains/losses of motifs, MOs, and biosynthetic modules. These resources and analyses will advance our understanding of glycan biosynthesis and the evolution of breast milk.

bioinformatics↗

bioRxiv2021

The role of fucose-containing glycan motifs across taxonomic kingdoms

The extraordinary diversity of glycans leads to large differences in the glycomes of different kingdoms of life. Yet, while most monosaccharides are solely found in certain taxonomic groups, there is a small set of monosaccharides with widespread distribution across nearly all domains of life. These general monosaccharides are particularly relevant for glycan motifs, as they can readily be used by commensals and pathogens to mimic host glycans or hijack existing glycan recognition systems. Among these, the monosaccharide fucose is especially interesting, as it frequently presents itself as a terminal monosaccharide, primed for interaction with proteins. Here, we analyze fucose-containing glycan motifs across all taxonomic kingdoms. Using a hereby presented large species-specific glycan dataset and a plethora of methods for glycan-focused bioinformatics and machine learning, we identify characteristic as well as shared fucose-containing glycan motifs for various taxonomic groups, demonstrating clear differences in fucose usage. Even within domains, fucose is used differentially based on an organisms physiology and habitat. We particularly highlight differences in fucose-containing motifs between vertebrates and invertebrates. With the example of pathogenic and non-pathogenic Escherichia coli strains, we also demonstrate the importance of fucose-containing motifs in molecular mimicry and thereby pathogenic potential. Finally, we also confirm and extend the role of fucosyltransferase-coding genes (FUT) in several important biological processes that include development, immunity, and diseases, especially cancer. We envision that this study will shed light on an important class of glycan motifs, with potential new insights into the role of fucosylated glycans in symbiosis, pathogenicity, and immunity.

bioinformatics↗

bioRxiv2021

Glycowork: A Python package for glycan data science and machine learning

As a biological sequence, glycans occur in every domain of life and comprise monosaccharides that are chained together to form oligo- or polysaccharides. While glycans are crucial for most biological processes, existing analysis modalities make it difficult for researchers with limited computational background to include information from these diverse and nonlinear sequences into standard workflows. Here, we present glycowork, an open-source Python package that was designed for the processing and analysis of glycan data by end users, with a strong focus on glycan-related data science and machine learning. Glycowork includes numerous functions to, for instance, automatically annotate glycan motifs and analyze their distributions via heatmaps and statistical enrichment. We also provide visualization methods, routines to interact with stored databases, trained machine learning models, and learned glycan representations. We envision that glycowork can extract further insights from any glycan dataset and demonstrate this with several workflows that analyze glycan motifs in various biological contexts. Glycowork can be freely accessed at https://github.com/BojarLab/glycowork/.

bioinformatics↗