OCT 07, 2021 8:30 AM PDT

Computational Crystal Ball Predicts Which COVID Variants Are More Dangerous

WRITTEN BY: Tara Fernandez

Viruses are nature’s shapeshifters—their genomes are in a constant state of flux as they divide in their hosts. Over time, they gradually acquire a library of errors that eventually change the way the virus behaves.

This evolutionary phenomenon has led to the emergence of SARS-CoV-2 variants such as Delta; a COVID variant thought to be more than twice as contagious as previous iterations of the coronavirus.

Given that the virus always seems to be one step ahead of efforts to control the pandemic, researchers have been seeking ways of predicting exactly how SARS-CoV-2 is changing over time and what that means from an epidemiological standpoint. Such platforms would buy us time, signaling the potential emergence of a potentially more dangerous variant before it sweeps through and devastates communities.

To this end, scientists at Penn State have made a giant leap forward, creating the first computational framework for predicting genetic changes in the coronavirus’ spike protein. The virus uses this protein as a molecular key to gain entry into human cells via their ACE2 receptors.

The vast majority of circulating variants possess mutations in the spike protein’s receptor binding domain (RBD). These amino acid changes boost the infectivity of the virus, allowing SARS-CoV-2 to enter and infect human cells faster and more easily, making the virus more transmissible. These accumulated changes in the RBD may also help variants evade the immune systems of vaccinated individuals or those who have recovered from COVID-19.

In their study, the researchers developed a two-step computational framework that models changes occurring in the RBD and links these mutations to the ability of the virus to bind to human ACE2 receptors.

“The binding strength between RBD and ACE2 directly affects infection dynamics and potentially disease progression,” explained the lead investigator of the study, Suresh Kuchipudi. 

“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.”

Kuchipudi and colleagues leveraged a technique known as Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) analysis, which measures how strongly the virus RBD binds to ACE2. Powered by binding affinity data of existing variants, their new framework uses a deep-learning algorithm to help predict the effects of amino acid changes in the RBD.

The team found that they could predict with over 80 percent accuracy whether or not RBD amino acid mutations improved or worsened the virus’ ability to latch on to ACE-2.

This new framework would empower public health officials with a tool to assess the infectivity of currently circulating and emerging SARS-CoV-2 variants in terms of their infectivity. If a new strain is predicted to have a greater binding affinity to ACE2, countermeasures to protect the public can rapidly be deployed to save lives. 



 

About the Author
  • Tara Fernandez has a PhD in Cell Biology and has spent over a decade uncovering the molecular basis of diseases ranging from skin cancer to obesity and diabetes. She currently works on developing and marketing disruptive new technologies in the biotechnology industry. Her areas of interest include innovation in molecular diagnostics, cell therapies, and immunology. She actively participates in various science communication and public engagement initiatives to promote STEM in the community.
You May Also Like
JUL 20, 2021
Clinical & Molecular DX
Wearable Health Monitors Powered by Sweaty Fingertips
JUL 20, 2021
Wearable Health Monitors Powered by Sweaty Fingertips
Fingertips have thousands of sweat-producing glands, churning out anywhere from 100 to 1,000 times more sweat than other ...
JUL 29, 2021
Genetics & Genomics
Obesity May Not Always Lead to Disease
JUL 29, 2021
Obesity May Not Always Lead to Disease
Some gene variants might be protecting people from the negative health effects of obesity.
AUG 28, 2021
Cannabis Sciences
Could Eye-tracking Data Detect THC Levels?
AUG 28, 2021
Could Eye-tracking Data Detect THC Levels?
Eye-tracking data shows promise for detecting levels of tetrahydrocannabinol (THC). The corresponding study was publishe ...
OCT 04, 2021
Genetics & Genomics
Zaki syndrome - Pediatric Disorder & Potential Treatment ID'ed
OCT 04, 2021
Zaki syndrome - Pediatric Disorder & Potential Treatment ID'ed
Since it's become quick and relatively inexpensive to sequence a human genome, researchers have gained unprecedented and ...
OCT 11, 2021
Genetics & Genomics
A Genetic Risk Factor is Shared by Alzheimer's and Severe COVID-19
OCT 11, 2021
A Genetic Risk Factor is Shared by Alzheimer's and Severe COVID-19
While amyloid plaques are a hallmark of Alzheimer's disease, the neurological disorder has also been linked to inflammat ...
OCT 25, 2021
Clinical & Molecular DX
Illuminating Blips in Blood Flow to the Brain
OCT 25, 2021
Illuminating Blips in Blood Flow to the Brain
  Just like an athlete needs to consume a huge number of calories to support their rigorous training regimes, neuro ...
Loading Comments...