Background
The recent release of the AlphaFold Protein Structure Database by DeepMind and EMBL-EBI has made available to the scientific community highly accurate structural predictions for 20,000 human proteins and those from 20 other biologically relevant organisms.
However, the protein-only predictions AlphaFold makes means that cofactors and, most importantly, co- and post-translational modifications, are understandably (due to the scope of the technique) not modelled. Among the most relevant co- and post-translational modifications is protein glycosylation. Indeed, between 50% and 70% of those 20,000 predicted human proteins are believed to be glycosylated, but none of this is yet visibly highlighted on the database. However, the algorithms implemented by DeepMind have digested inter-residue distances from the Protein Data Bank5, where glycosylated proteins often exhibit either full or partial glycan structures; therefore, the space where unmodeled modifications, such as protein glycosylation, is somehow preserved in AlphaFold models, allowing for these structural features to be directly grafted onto a model, post-prediction.
Experimental Approach and Objectives
We have developed proof-of-concept software that grafts protein glycosylation from a library of structurally equilibrated glycan blocks, obtained from molecular dynamics (MD), into an AlphaFold model. This task has been automated and integrated into the new Python interface of the carbohydrate-specific Privateer software.
In this project, you will be harnessing all available prior knowledge ('big data' coming from glycomics, crystallographic and Cryo-EM structures in the Protein Data Bank…) to find the best strategies for glycosylating AlphaFold models in silico. Your work will include evaluating and finding ways of improving the decisions made by our machine learning algorithm for the estimation of glycan compositions, currently under development in our group. Basic knowledge of Python essential.
Novelty
This project will be the first one to produce a conformation-dependent analysis of glycan geometry, which will serve to enhance and validate structures of glycans in glycoproteins. 3D structures are routinely used to inform vaccine design, where the properties of the constructs to be encapsulated are based on, among others, structural information.
Training
Depending on the successful candidate’s qualifications, taking an ‘Introduction to Python programming’ course might be desirable. This runs regularly in the Department of Chemistry at the University of York.
The project will also provide externally-funded opportunities for teaching and training in specialised structural biology workshops in the UK and overseas through the York-exclusive Hartshorn-Jones fund (which may cover both training and purchases of hardware and software), Collaborative Computational Projects for macromolecular crystallography (CCP4) and electron cryo-microscopy (CCP-EM).
All research students follow our innovative Doctoral Training in Chemistry (iDTC): cohort-based training to support the development of scientific, transferable and employability skills.
Equality, Diversity and Inclusion
The Department of Chemistry holds an Athena SWAN Gold Award and is committed to supporting equality and diversity for all staff and students. Chemistry at York was the first academic department in the UK to receive the Athena SWAN Gold award in 2007, renewed 2010, 2015 and 2019.
All Chemistry research students have access to our innovative Doctoral Training in Chemistry (iDTC): cohort-based training to support the development of scientific, transferable and employability skills: https://www.york.ac.uk/chemistry/postgraduate/cdts/
The Department of Chemistry holds an Athena SWAN Gold Award and is committed to supporting equality and diversity for all staff and students. The Department strives to provide a working environment which allows all staff and students to contribute fully, to flourish, and to excel: https://www.york.ac.uk/chemistry/ed/ .
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