Using computer modeling to predict the evolution of new COVID variants

Using computer modeling to predict the evolution of new COVID variants
Fig 1. Domains of the Spike protein. N-Terminal Domain (NTD), Receptor Binding Domain (RBD), Subunit 1/Subunit 2 junction (S1/S2), Fusion Peptide (FP), Heptad Repeat 1 (HR1), Heptad Repeat 2 (HR2), Transmembrane Domain (TM), and the Cytoplasmic Tail (CT). Crystallography structure in the conformational state of all 3 RBD domains closed (PDB 6VXX) and of 1 RBD open (PDB 6VYB), binding to ACE2 (PDB 6M17). Credit: DOI: 10.1371/journal.pcbi.1009286

A group of researchers at Université de Montréal has simulated the effects of more than 17,000 possible mutations of the spike protein of SARS-CoV-2, the virus responsible for COVID-19.

These spike proteins are found on the surface of the virus and are responsible for its toothed appearance. When these proteins are in the "open" state, the virus is able to bind to and trigger an infection.

As Najmanovich, project lead and professor in UdeM's Department of Pharmacology and Physiology, explains, "our computer modeling has shown that in the original virus the spike proteins are in the closed state 75% of the time and in the open state 25% of the time. In D614G, the very first spike mutation, the open state is more rigid and the closed state more flexible. This causes the spike proteins to stay in the open state longer, giving the virus more opportunity to bind to human receptors and making it more infectious."

This modeling technique has enabled the team to predict future even before they appear in nature and to anticipate their virulence for humans.

A proven technique, but one with limitations

Prof. Najmanovich, who is a specialist in molecular design and computational structural pharmacology, points out that this model correctly foresaw the emergence of specific mutations that appeared in several variants of concern, including B117 (the "British" variant), B1351 (the "South African" variant), and P1 (the "Brazilian" variant).

"Proteins are linear chains of 20 different types of amino acids," explains Najmanovich. "The spike contains more than 1,000 amino acids. In our , we replaced each amino with each of the other 19 possible , and then repeated the process for each site on the original virus. This gave us not only all existing mutations, but also ones that are not yet present in nature."

But these predictions are not always completely accurate. For example, the Delta variant as predicted by the model is no more infectious than other variants. The reason, says Prof. Najmanovich, is that "we had to simplify our model because of the vast number of calculations involved. This meant we weren't able to capture other biological and molecular processes that could potentially help the become more infectious."

More information: Natália Teruel et al, Modelling conformational state dynamics and its role on infection for SARS-CoV-2 Spike protein variants, PLOS Computational Biology (2021). DOI: 10.1371/journal.pcbi.1009286

Journal information: PLoS Computational Biology
Citation: Using computer modeling to predict the evolution of new COVID variants (2021, September 17) retrieved 23 February 2024 from
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