New Machine Learning Model Delivers 90% Accuracy in Glycan Annotation
A machine learning algorithm that enables accurate, de novo annotation of glycan structures from tandem mass spectrometry (MS/MS) data – without relying on existing glycan libraries – has been developed by researchers at the University of Oxford and collaborators. The method, called GlycoDeNovo-RT-GNN, integrates a graph neural network (GNN) with retention time predictions to generate biosynthetically plausible glycan topologies from raw spectra.
Tested on over 200 permethylated N- and O-glycan MS/MS spectra from Thermo Orbitrap Fusion Lumos and Agilent Q-TOF 6550 instruments, the model achieved over 90% accuracy in correctly identifying glycan topologies. This approach significantly improves upon previous de novo methods, particularly in ranking candidate structures and resolving isomeric ambiguity.
According to the authors, the method “will accelerate the annotation of MS/MS glycomics data and aid in identifying biosynthetically feasible glycans.” The output is compatible with existing glycomics software such as GlycoWorkbench and Glycoforest, supporting integration into current analytical workflows.
New Cross-Linker Enables Protein Interaction Mapping in 10,000 Cells
Researchers at the Dalian Institute of Chemical Physics have developed a cross-linker – dubbed DPST (2,6-dimethylpiperidine disuccinimidyl tridecanoate) – that simplifies in vivo cross-linking mass spectrometry (XL-MS), allowing both enrichment and quantification of protein complexes from as few as 10,000 cells.
Conventional XL-MS techniques often suffer from poor reproducibility and significant sample loss during multi-step enrichment. DPST sidesteps these issues through a one-step enrichment method using tandem mass tag (TMT) antibodies. This streamlines workflows, boosts signal-to-noise ratio, and supports light/heavy isotope labeling directly at the cellular level – without increasing spectral complexity.
The cross-linker’s high sensitivity enabled researchers to map protein interactions in primary neurons from single-embryo mice and to capture weak, transient interactions in dynamic liquid–liquid phase separation environments. “DPST provides a powerful solution for both qualitative and quantitative XL-MS analysis,” said lead investigator Zhang Lihua, noting the tool’s “strong potential to drive advances in biomedical research and drug discovery.”
Deciphering Polymers, Block by Block
A new algorithm developed by researchers at the University of Amsterdam delivers unprecedented insight into the internal structure of block copolymers – key materials used in everything from automotive parts to medical devices. Described in Analytica Chimica Acta and Macromolecules, the method combines tandem mass spectrometry (MS/MS) data with a novel computational strategy that interprets polymer fragmentation behavior.
The team used electrospray ionization quadrupole time-of-flight (ESI-QTOF) MS/MS to acquire detailed fragmentation patterns of polyamide and polyurethane copolymers. These data were then processed by their algorithm to reconstruct block-length distributions – a feat not possible with traditional methods like NMR, which average out critical details.
“Block-length distribution data are essential to assess the quality of complex copolymers and for the design of new sustainable polymer materials,” the authors write. The results revealed that polymers with the same chemical composition can have markedly different microstructures depending on their synthesis routes, directly affecting material performance and recyclability.
By enabling fine-grained, reproducible analysis of polymer architecture, the approach marks a “significant step forward” in structural elucidation and sets the stage for precision design of next-generation, sustainable materials.
Invasive Mosquito Species Spreads to French Caribbean Territory
The mosquito Aedes albopictus – a known vector for dengue, chikungunya, and Zika viruses – has been detected for the first time in Saint Barthélemy, raising concerns about the potential spread to other French Caribbean territories and beyond. Its arrival marks a significant shift in local vector ecology, as this species had never been observed in the French Territories of the Americas despite its global spread since the 1990s.
The researchers confirmed the identity of the mosquitoes using three independent methods: traditional morphology, cytochrome c oxidase 1 gene barcoding, and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). The mosquitoes were first collected from a floor drain in Lorient during routine surveillance in September 2024. Follow-up fieldwork in October revealed breeding sites not only near the original location but also 1.6 km away in Saint Jean, near the island's airport, indicating a wider distribution than initially suspected.
Owing to its ecological flexibility, the authors warn that the species' presence suggests Ae. albopictus is poised to spread quickly, especially in artificial breeding sites in urban areas. Given the island’s daily air and sea connections to other parts of the Caribbean, the introduction of Ae. albopictus could have broader regional implications, prompting calls for urgent reinforcement of vector control measures.
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