Attachment of human noroviruses to histo blood group antigens (HBGAs) is essential for infection, but how this binding event promotes the infection of host cells is unknown. Here, we employ protein NMR experiments supported by mass spectrometry and crystallography to study HBGA binding to the P-domain of a prevalent virus strain (GII.4). We report a highly selective transformation of asparagine 373, located in an antigenic loop adjoining the HBGA binding site, into an iso-aspartate residue. This spontaneous post-translational modification (PTM) proceeds with an estimated half-life of a few days at physiological temperatures, independent of the presence of HBGAs but dramatically affecting HBGA recognition. Sequence conservation and the surface-exposed position of this PTM suggest an important role in infection and immune recognition for many norovirus strains.

The number of publications in the field of chemical cross-linking combined with mass spectrometry (XL-MS) to derive constraints for protein three-dimensional structure modeling and to probe protein–protein interactions has increased during the last years. As the technique is now becoming routine for in vitro and in vivo applications in proteomics and structural biology there is a pressing need to define protocols as well as data analysis and reporting formats. Such consensus formats should become accepted in the field and be shown to lead to reproducible results. This first, community-based harmonization study on XL-MS is based on the results of 32 groups participating worldwide. The aim of this paper is to summarize the status quo of XL-MS and to compare and evaluate existing cross-linking strategies. Our study therefore builds the framework for establishing best practice guidelines to conduct cross-linking experiments, perform data analysis, and define reporting formats with the ultimate goal of assisting scientists to generate accurate and reproducible XL-MS results.

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With the advance of experimental procedures obtaining chemical crosslinking information is becoming a fast and routine practice. Information on crosslinks can greatly enhance the accuracy of protein structure modeling. Here, we review the current state of the art in modeling protein structures with the assistance of experimentally determined chemical crosslinks within the framework of the 13th meeting of Critical Assessment of Structure Prediction approaches. This largest‐to‐date blind assessment reveals benefits of using data assistance in difficult to model protein structure prediction cases. However, in a broader context, it also suggests that with the unprecedented advance in accuracy to predict contacts in recent years, experimental crosslinks will be useful only if their specificity and accuracy further improved and they are better integrated into computational workflows.