New tool predicts changes that may make COVID variants more infectious


Researchers have developed a new method that can predict with reasonable accuracy the changes in SARS-CoV-2 that may confer increased infectivity to the virus that causes COVID-19. The tool, described in the journal Proceedings of the National Academy of Sciences, 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 spread of the virus and perhaps even may inform vaccine booster formulations, the researchers said

The framework can predict the changes in amino acid — molecules linked together to form proteins — in the virus’s spike protein that may improve its binding to human cells and confer increased infectivity to the virus, they said. SARS-CoV-2 uses the spike protein to enter and infect the human cells.

“Emerging variants could potentially be highly contagious in humans and other animals,” said Suresh Kuchipudi, a clinical professor at Penn State in the US. “Therefore, it is critical to proactively assess what amino acid changes may likely increase the infectiousness of the virus,” Kuchipudi said.

The team used a novel, two-step computational procedure to create a model for predicting which changes in amino acids 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. Increased binding affinity of the RBD of the spike protein with the human ACE2 receptor is one such mechanism,” he said.

The researchers first 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 led by Costas D Maranas, Professor at Penn State, found that this technique was only partially able to predict the binding affinity of SARS-CoV-2’s RBD for ACE2.

The team used a neural network model — a type of deep-learning algorithm — and trained it using experimentally available data on binding in variants with single amino acid changes. They found that they could predict with more than 80 per cent 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, the researchers said. The researchers noted that SARS-CoV-2 can switch hosts as a result of increased contact between the virus and potential new hosts. “The new tool can help make sense of the enormous virus sequence data generated by genomic surveillance,” Kuchipudi said. “In particular, it may help determine if the virus can adapt and spread among agricultural animals, thereby informing targeted mitigation measures,” he added.

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