Study employs AI to comprehend sugar surrounding proteins in the human body

Novel studies on artificial intelligence (AI) algorithms facilitated researchers to develop more complete models of the protein structures in human bodies. This opened the door to the rapid design of vaccines and therapeutics.

Study employs AI to comprehend sugar surrounding proteins in the human body
A model of sugars involved in the research. Image Credit: Dr Jon Agirre.

The research headed by the University of York employed AI to help scientists comprehend more about the sugar that encloses most proteins in human bodies.

Around 70% of human proteins are scaffolded or enclosed with sugar that performs a vital role in how they look and act. Certain viruses like those causing Flu, AIDS, Ebola, and COVID-19 are also protected behind sugars (glycans). The addition of the sugars is called modification.

To analyze the proteins, scientists developed software that inserts missing sugar components to models generated with AlphaFold—an AI program created by Google’s DeepMind, which executes predictions of protein structures.

The proteins of the human body are tiny machines that in their billions, make up our flesh and bones, transport our oxygen, allow us to function, and defend us from pathogens. And just like a hammer relies on a metal head to strike pointy objects including nails, proteins have specialized shapes and compositions to get their jobs done.”

Dr Jon Agirre, Study Senior author, Department of Chemistry, University of York

Dr. Agirre adds, “The AlphaFold method for protein structure prediction has the potential to revolutionize workflows in biology, allowing scientists to understand a protein and the impact of mutations faster than ever.”

However, the algorithm does not account for essential modifications that affect protein structure and function, which gives us only part of the picture. Our research has shown that this can be addressed in a relatively straightforward manner, leading to a more complete structural prediction.”

Dr Jon Agirre, Study Senior author, Department of Chemistry, University of York

The current introduction of AlphaFold and the associated database of protein structures has enabled scientists to have accurate structure predictions for all known human proteins.

It is always great to watch an international collaboration grow to bear fruit, but this is just the beginning for us. Our software was used in the glycan structural work that underpinned the mRNA vaccines against SARS-CoV-2, but now there is so much more we can do thanks to the AlphaFold technological leap. It is still early stages, but the objective is to move on from reacting to changes in a glycan shield to anticipating them.”

Dr Jon Agirre, Study Senior author, Department of Chemistry, University of York

Source:
Journal reference:

Bagdonas, H., et al. (2021) The case for post-predictional modifications in the AlphaFold Protein Structure Database. Nature Structural & Molecular Biology. doi.org/10.1038/s41594-021-00680-9.

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