Researchers develop computational model to predict changes in SARS-CoV-2’s viral fitness

Through advantageous mutations that can occur at the virus’s spike protein, tests have shown that the emergence of several new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can help the virus to infect human host cells more efficiently.

Despite various mitigation measures, the coronavirus disease 2019 (COVID-19) pandemic continues, with more than 138.7 million people infected and over 3 million deaths.

Research to understand viral pathogenesis, large-scale vaccine administration, and the development of effective therapeutic agents have helped in coping with the global crisis. However, all of these measures have been accompanied by the emergence of new variants, which may tip the scale towards graver conditions for many.

The increase in the viral immunity at the population level due to SARS-CoV-2 infection, vaccination or passive immunization via nAbs (neutralizing antibodies) results in stronger selection pressure on the SARS-CoV-2 virus. This is the cause of the emergence of new variants which have exhibited the capacity to evade immune responses.

Computational and experimental studies may thus focus on understanding these escape mechanisms in the SARS-CoV-2 viral infection and on setting up SARS-CoV-2 immune monitoring of the world’s population to track and eventually limit the spreading of potentially escaping variants.

Two researchers from the Université Libre de Bruxelles in Belgium, Fabrizio Pucci and Marianne Rooman, recently built a simplified computational model, called SpikePro, to predict the SARS-CoV-2 fitness from the amino acid sequence and structure of the spike protein. This study focuses on the spike protein’s stability and the binding affinity to the host receptor. A preprint outlining the details of this model is available to read in full on the bioRvix* server.

SpikePro is an easy-to-use program that identifies with good accuracy and within a few seconds. It is freely available in the GitHub repository www.github.com/3BioCompBio/SpikeProSARS-CoV-2.

Using this model, the researchers reported three contributions: 1) the viral transmissibility predicted from the stability of the spike protein, 2) the infectivity computed in terms of the affinity of the spike protein for the ACE2 host cell receptor, and 3) the ability of the virus to escape from the human immune response based on the binding affinity of the spike protein for a set of neutralizing antibodies.

Indeed, even though SARS-CoV-2 has a moderate mutation rate compared to other RNA viruses due to its more accurate replication, tracking viral dynamics in the huge space of possible variant combinations (including also deletions and insertions) under the influence of human immunity makes predictions highly challenging.”

The researchers demonstrated that the SpikePro model reproduces well the available experimental, epidemiological and clinical data on the impact of variants on the biophysical characteristics of the virus.

Whether the validation is performed on large-scale mutagenesis data, nAb cocktails or polyclonal human sera, 367 whether the comparison involves the fitness of the spike protein, of the spike protein/ACE2 complex, or of a series 368 of spike protein/nAb complexes, the results are very good with correlation coefficients in the 0.3 to 0.5 range.”

To elaborate, they showed that it could identify circulating viral strains that recently became dominant at the population level. These viral strains have increased their fitness. Because the SpikePro identifies the new SARS-CoV-2 variants with high fitness, which need to be closely monitored by health agencies, this model has a central role to play in genomic surveillance programs of the new SARS-CoV-2 strains. Especially in the future, with the growing number of people vaccinated and thus creating a larger selective pressure on the virus, SpikePro would be highly useful.

Also, the researchers emphasized that the SpikePro model (besides being able to reproduce known results) truly predicts, and describes and interprets the effect of new spike protein variants that become fixed in the future SARS-CoV-2 evolution. Using the well-known structure-based PoPMuSiC and BeAtMuSiC 376 predictors, the researchers achieve this by the physical description of the fitness in terms of free energy contributions.

In the paper, the researchers also discussed the limitations of the study: some approximations were made in the construction process, not accounting for possible amino acid deletions or insertions in the spike protein. Epistatic effects were also left out and not extended to other proteins of the SARS-CoV-2 virus, such as the non-structural protein 1 (Nsp1), which also contributes to immune evasion.

Considering a weighted combination of 386 the effects of RBD variants on all nAbs depending on different factors such as time and vaccination status would further improve our method in mimicking the immune response and its temporal evolution.”

In this study, the researchers developed and validated SpikePro: a simple computational model that predicts the impact of spike protein variants on the SARS-CoV-2 fitness and, more specifically, on viral transmissibility, infectivity and ability to escape from the host’s immune system.

The research duo claimed that SpikePro is a useful instrument for the genomic surveillance of the SARS-CoV-2 virus. It predicts in a fast and accurate way the emergence of new viral strains and their dangerousness, they write.

*Important Notice

bioRxiv publishes preliminary scientific reports that are not peer-reviewed and, therefore, should not be regarded as conclusive, guide clinical practice/health-related behavior, or treated as established information.

Journal reference:
Dr. Ramya Dwivedi

Written by

Dr. Ramya Dwivedi

Ramya has a Ph.D. in Biotechnology from the National Chemical Laboratories (CSIR-NCL), in Pune. Her work consisted of functionalizing nanoparticles with different molecules of biological interest, studying the reaction system and establishing useful applications.

Citations

Please use one of the following formats to cite this article in your essay, paper or report:

  • APA

    Dwivedi, Ramya. (2021, April 19). Researchers develop computational model to predict changes in SARS-CoV-2’s viral fitness. News-Medical. Retrieved on May 18, 2021 from https://www.news-medical.net/news/20210419/Researchers-develop-computational-model-to-predict-changes-in-SARS-CoV-2e28099s-viral-fitness.aspx.

  • MLA

    Dwivedi, Ramya. "Researchers develop computational model to predict changes in SARS-CoV-2’s viral fitness". News-Medical. 18 May 2021. <https://www.news-medical.net/news/20210419/Researchers-develop-computational-model-to-predict-changes-in-SARS-CoV-2e28099s-viral-fitness.aspx>.

  • Chicago

    Dwivedi, Ramya. "Researchers develop computational model to predict changes in SARS-CoV-2’s viral fitness". News-Medical. https://www.news-medical.net/news/20210419/Researchers-develop-computational-model-to-predict-changes-in-SARS-CoV-2e28099s-viral-fitness.aspx. (accessed May 18, 2021).

  • Harvard

    Dwivedi, Ramya. 2021. Researchers develop computational model to predict changes in SARS-CoV-2’s viral fitness. News-Medical, viewed 18 May 2021, https://www.news-medical.net/news/20210419/Researchers-develop-computational-model-to-predict-changes-in-SARS-CoV-2e28099s-viral-fitness.aspx.

Comments

The opinions expressed here are the views of the writer and do not necessarily reflect the views and opinions of News Medical.
You might also like... ×
Highly efficient immune response in asymptomatic SARS-CoV-2 patients