New tool predicts changes that may make COVID variants more infectious


Date:
September 29, 2021
Source:
Penn State
Summary:
Researchers have created a novel framework that can predict
with reasonable accuracy the amino-acid changes in the virus'
spike protein that may improve its binding to human cells and
confer increased infectivity to the virus. The tool could enable
the computational surveillance of SARS-CoV-2 and provide advance
warning of potentially dangerous variants with an even higher
binding affinity potential. This can aid in the early implementation
of public health measures to prevent the virus's spread and perhaps
even may inform vaccine booster formulations.



FULL STORY ==========================================================================
As SARS-CoV-2 continues to evolve, new variants are expected to arise that
may have an increased ability to infect their hosts and evade the hosts'
immune systems. The first key step in infection is when the virus' spike protein binds to the ACE2 receptor on human cells. Researchers at Penn
State have created a novel framework that can predict with reasonable
accuracy the amino-acid changes in the virus' spike protein that may
improve its binding to human cells and confer increased infectivity to
the virus.


==========================================================================
The tool could enable the computational surveillance of SARS-CoV-2
and provide advance warning of potentially dangerous variants with
an even higher binding affinity potential. This can aid in the early implementation of public health measures to prevent the virus's spread
and perhaps even may inform vaccine booster formulations.

"Emerging variants could potentially be highly contagious in humans and
other animals," said Suresh Kuchipudi, clinical professor of veterinary
and biomedical sciences and associate director of the Animal Diagnostic
Lab, Penn State. "Therefore, it is critical to proactively assess
what amino acid changes may likely increase the infectiousness of the
virus. Our framework is a powerful tool for determining the impact of
amino acid changes in the SARS-CoV- 2 spike protein that affect the
ability of the virus to bind to ACE2 receptors in humans and multiple
animal species." The team used a novel, two-step computational procedure
to create a model for predicting which changes in amino acids -- molecules linked together to form proteins -- may occur in the receptor binding
domain (RBD) of SARS-CoV-2's spike protein that could affect its ability
to bind to the ACE2 receptors of human and other animal cells.

According to Kuchipudi, the currently circulating variants include one
or more mutations that have led to amino-acid changes in the RBD of the
spike protein.

"These amino-acid changes may have conferred fitness advantages
and increased infectivity through a variety of mechanisms," he
said. "Increased binding affinity of the RBD of the spike protein with
the human ACE2 receptor is one such mechanism." Kuchipudi explained
that the spike protein binding to the ACE2 receptor is the first and
crucial step in viral entry to the cell.



==========================================================================
"The binding strength between RBD and ACE2 directly affects infection
dynamics and potentially disease progression," he said. "The ability to reliably predict the effects of virus amino-acid changes in the ability
of its RBD to interact more strongly with the ACE2 receptor can help in assessing public health implications and the potential for spillover and adaptation into humans and other animals." Costas D. Maranas, Donald
B. Broughton Professor in the Department of Chemical Engineering at Penn
State, led the development of the team's new two-step procedure. First,
the researchers tested the predictive power of a technique, called
Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) analysis, to quantify the binding affinity of the RBD for ACE2. MM-GBSA analysis sums
up multiple types of energy contributions associated with the virus's RBD "sticking" to the human ACE2 receptor. Using data from already existing variants, the team found that this technique was only partially able to
predict the binding affinity of SARS-CoV-2's RBD for ACE2.

Therefore, Maranas and the team explored the use of the energy terms
from the MM-GBSA analysis as features in a neural network regression
model -- a type of deep-learning algorithm -- and trained the model
using experimentally available data on binding in variants with single
amino acid changes. They found that they could predict with more than 80% accuracy whether certain amino acid changes improved or worsened binding affinity for the dataset explored.

"This combined MM-GBSA with a neural network model approach appears to
be quite effective at predicting the effect of amino acid changes not
used during model training," said Maranas.

The model also allowed for the prediction of the binding strength
of various already observed SARS-CoV-2 amino acid changes seen in
the Alpha, Beta, Gamma and Delta variants. This may provide the
computational means for predicting such affinities in yet-to-be
discovered variants. Nevertheless, even though our computational tool
can find amino acid changes that boost binding affinity even further,
they have not yet been observed in circulating variants. This may mean
that such changes could interfere with other requirements of productive
virus infection. It is a reminder that binding with the ACE2 receptor
is not the complete story.



==========================================================================
The findings published today (Sept. 29) in the journal Proceedings of
the National Academy of Sciences.

"Our method sets up a framework for screening for binding affinity
changes resulting from unknown single and multiple amino acid changes; therefore, offering a valuable tool to assess currently circulating and prospectively future viral variants in terms of their affinity for ACE2
and greater infectiousness," said Maranas.

Kuchipudi added, "SARS-CoV-2 can switch hosts as a result of increased
contact between the virus and potential new hosts. This tool can help
make sense of the enormous virus sequence data generated by genomic surveillance. In particular, it may help determine if the virus can
adapt and spread among agricultural animals, thereby informing targeted mitigation measures." Co-first authors on the paper are Chen Chen, postdoctoral researcher, and Veda Sheersh Boorla, graduate student in
chemical engineering, both at Penn State.

Other authors include Deepro Banerjee, graduate research assistant, Penn
State; Ratul Chowdhury, postodoctoral associate, Harvard University;
Victoria S.

Cavener, researcher, Huck Institutes of the Life Sciences, Penn State;
Ruth H.

Nissly, research technologist in veterinary and biomedical sciences,
Penn State; Abhinay Gontu, graduate student in veterinary and biomedical sciences, Penn State; Nina R. Boyle, graduate student in integrative and biomedical physiology, Penn State; Kurt Vandegrift, associate research professor of biology, Penn State; and Meera Surendran Nair, assistant
clinical professor of veterinary and biomedical sciences, Penn State.

The United States Department of Agriculture, the United States Department
of Energy and the Huck Institutes of the Life Sciences at Penn State
supported this research.

========================================================================== Story Source: Materials provided by Penn_State. Original written by Sara LaJeunesse. Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Chen Chen, Veda Sheersh Boorla, Deepro Banerjee, Ratul Chowdhury,
Victoria S. Cavener, Ruth H. Nissly, Abhinay Gontu, Nina R. Boyle,
Kurt Vandegrift, Meera Surendran Nair, Suresh V. Kuchipudi, Costas
D. Maranas.

Computational prediction of the effect of amino acid changes
on the binding affinity between SARS-CoV-2 spike RBD and human
ACE2. Proceedings of the National Academy of Sciences, 2021; 118
(42): e2106480118 DOI: 10.1073/pnas.2106480118 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/09/210929163800.htm

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