Scientists build on AI modeling to understand more about protein-sugar structures

Date:
November 2, 2021
Source:
University of York
Summary:
New research building on AI algorithms has enabled scientists to
create more complete models of the protein structures in our bodies
- paving the way for faster design of therapeutics and vaccines.



FULL STORY ==========================================================================
New research building on AI algorithms has enabled scientists to create
more complete models of the protein structures in our bodies -- paving
the way for faster design of therapeutics and vaccines.


==========================================================================
The study -- led by the University of York -- used artificial intelligence
(AI) to help researchers understand more about the sugar that surrounds
most proteins in our bodies.

Up to 70 per cent of human proteins are surrounded or scaffolded with
sugar, which plays an important part in how they look and act. Moreover,
some viruses like those behind AIDS, Flu, Ebola and COVID-19 are also
shielded behind sugars (glycans). The addition of these sugars is known
as modification.

To study the proteins, researchers created software that adds missing
sugar components to models created with AlphaFold, which is an artificial intelligence program developed by Google's DeepMind which performs
predictions of protein structures.

Senior author, Dr Jon Agirre from the Department of Chemistry said:
"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 specialised
shapes and compositions to get their jobs done." "The AlphaFold method
for protein structure prediction has the potential to revolutionise
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."
The recent introduction of AlphaFold and the accompanying database of
protein structures has enabled scientists to have accurate structure predictions for all known human proteins.

Dr Agirre added: "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." The research was conducted with Dr Elisa
Fadda and Carl A. Fogarty from Maynooth University. Haroldas Bagdonas,
PhD student at the York Structural Biology Laboratory, which is part of
the Department of Chemistry, also worked on the study with Dr Agirre.

========================================================================== Story Source: Materials provided by University_of_York. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Haroldas Bagdonas, Carl A. Fogarty, Elisa Fadda, Jon Agirre. The
case for
post-predictional modifications in the AlphaFold Protein Structure
Database. Nature Structural & Molecular Biology, 2021; DOI:
10.1038/ s41594-021-00680-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/11/211102111151.htm

--- up 8 weeks, 5 days, 8 hours, 25 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1:317/3)