Home Framework New framework can predict changes that may increase infectivity of SARS-CoV-2

New framework can predict changes that may increase infectivity of SARS-CoV-2


As SARS-CoV-2 continues to evolve, new variants are expected to emerge 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. Penn State researchers have created a new 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 computer surveillance of SARS-CoV-2 and provide early warning of potentially dangerous variants with even higher binding affinity potential. This can help with the early implementation of public health measures to prevent the spread of the virus and possibly even inform vaccine booster formulations.

Emerging variants could potentially be highly contagious in humans and other animals. Therefore, it is essential to proactively assess changes in amino acids that may increase the infectivity 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 several animal species. “

Suresh Kuchipudi, Clinical Professor of Veterinary and Biomedical Sciences and Associate Director of the Animal Diagnostic Lab, Penn State

The team used a novel two-step computational procedure to create a model to predict which changes in amino acids -; molecules linked together to form proteins -; may occur in the receptor binding domain (RBD) of the SARS-CoV-2 spike protein which could affect its ability to bind to ACE2 receptors in human cells and other animal cells.

According to Kuchipudi, the variants currently in circulation include one or more mutations that resulted in amino acid changes in the RBD of the spike protein.

“These amino acid changes may have conferred fitness benefits and increased infectivity through a variety of mechanisms,” he said. “The increased binding affinity of the spike protein RBD to the human ACE2 receptor is one such mechanism.”

Kuchipudi explained that the spike protein binding to the ACE2 receptor is the crucial first step for viral entry into the cell.

“The binding strength between RBD and ACE2 directly affects the dynamics of the infection and potentially the progression of the disease,” he said. “The ability to reliably predict the effects of the virus’s amino acid changes on the ability of its RBD to interact more strongly with the ACE2 receptor may help assess the public health implications and the potential for spillover and spillover. adaptation in humans and other animals. “

Costas D. Maranas, Professor Donald B. Broughton 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 Generalized Molecular Mechanical Birth Surface Analysis (MM-GBSA), to quantify the binding affinity of RBD for ACE2. The MM-GBSA analysis summarizes several types of energetic contributions associated with the “sticking” of virus RBD to the human ACE2 receptor. Using data from already existing variants, the team found that this technique was only partially able to predict the RBD binding affinity of SARS-CoV-2 for ACE2.

Therefore, Maranas and the team explored the use of 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 greater than 80% accuracy whether certain amino acid changes improved or worsened the binding affinity for the data set being explored.

“This combined approach of MM-GBSA and a neural network model appears to be quite effective in predicting the effect of unused amino acid changes during model training,” said Maranas.

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

The results published today (September 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, providing a valuable tool to assess currently circulating and prospectively future viral variants in terms of of their affinity for ACE2 and greater infectivity, ”says Maranas.

Kuchipudi added, “SARS-CoV-2 can change hosts due to increased contact between the virus and potential new hosts. This tool can help make sense of the huge viral sequence data generated by genomic monitoring. In particular, it can help determine whether the virus can adapt and spread among farm animals, thus informing targeted mitigation measures. “


Journal reference:

Chen, C., et al. (2021) Computer prediction of the effect of amino acid changes on the binding affinity between the SARS-CoV-2 RBD peak and human ACE2. PNAS. doi.org/10.1073/pnas.2106480118.


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