Accessibility statement

Dr Jon Agirre
(He/Him)

Royal Society University Research Fellow (assistant professor)

Tel: 01904 32 8252  Office: B/K/065 (Biology, Campus West)
Email: jon.agirre@york.ac.uk

Integrative structural glycobiology

AlphaFold_glycans_long

In our group, we create computer programs that help model and understand atomic structures of biomolecules. We are particularly interested in carbohydrates and their interactions with proteins. We favour a combination of Python for prototyping, C++ for number crunching, HTML and Javascript for representation. We are a friendly, diverse and very international bunch, and our ambition is to put YSBL at the forefront of crystallographic and cryoEM method development.

In the past six years, our team has produced new methodologies for building meaningful atomic models of glycans. A timely development, our Privateer software has been routinely used in the determination of SARS-CoV-2 structures that have informed the mRNA vaccines, antibody and inhibitor designs. But as the studies of the SARS-CoV-2 spike glycoprotein dynamics have evidenced, there is a big structural gap between what we can manage to resolve – even with the best methods – in an atomic structure and what is really there as determined by glycomics techniques – eg ~65 monosaccharides may be modelled into a Cryo-EM reconstruction of the SARS-CoV-2 spike glycoprotein, while 476 are detected by mass spectrometry. This is due to intractable problems such as microheterogeneity and flexibility – intrinsic to protein glycosylation – which hamper the process of structure determination.

However, for an atomic model of a glycoprotein to be mechanistically useful, it must contain the full-length glycans the glycoprotein has in vivo. This problem has now been taken to the extreme with the release of AlphaFold: a software method capable of predicting the structure of proteins with experimental-grade accuracy, but which leaves the crucial glycan components out altogether. New methods are urgently required to bridge the gap between the incomplete structures produced by the structural biology community, including AlphaFold, and the expectations of the mechanistic glycobiology field. In my team, we build on machine learning approaches to predict, graft and extend protein glycosylation onto either incomplete (X-ray crystallography and Cryo-EM) models, or AlphaFold protein structure predictions – all based on technology developed as part of my Royal Society University Research fellowship.

Portrait

York Research Database link

glycojones

Funding and support

My team's activities are supported (financially and/or logistically) by:

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