Emerging dominant SARS-CoV-2 variants Jiahui Chen1 Rui Wang1 Yuta Hozumi1 Gengzhuo Liu1 Yuchi Qiu1 Xiaoqi Wei1and Guo-Wei Wei134

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Emerging dominant SARS-CoV-2 variants

Jiahui Chen1, Rui Wang1, Yuta Hozumi1, Gengzhuo Liu1, Yuchi Qiu1, Xiaoqi Wei1and

Guo-Wei Wei1,3,4∗

1Department of Mathematics,

Michigan State University, MI 48824, USA.

2Department of Electrical and Computer Engineering,

Michigan State University, MI 48824, USA.

3Department of Biochemistry and Molecular Biology,

Michigan State University, MI 48824, USA.

October 19, 2022

Abstract

Accurate and reliable forecasting of emerging dominant severe acute respiratory syndrome coronavirus

2 (SARS-CoV-2) variants enables policymakers and vaccine makers to get prepared for future waves

of infections. The last three waves of SARS-CoV-2 infections caused by dominant variants Omicron

(BA.1), BA.2, and BA.4/BA.5 were accurately foretold by our artiﬁcial intelligence (AI) models built

with biophysics, genotyping of viral genomes, experimental data, algebraic topology, and deep learning.

Based on newly available experimental data, we analyzed the impacts of all possible viral spike (S) protein

receptor-binding domain (RBD) mutations on the SARS-CoV-2 infectivity. Our analysis sheds light on

viral evolutionary mechanisms, i.e., natural selection through infectivity strengthening and antibody

resistance. We forecast that BA.2.10.4, BA.2.75, BQ.1.1, and particularly, BA.2.75+R346T, have high

potential to become new dominant variants to drive the next surge.

Keywords: COVID-19, SARS-CoV-2, Omicron, infectivity, subvariants, deep learning, algebraic topology.

∗Corresponding author. Email: weig@msu.edu

arXiv:2210.09485v1 [q-bio.PE] 18 Oct 2022

1 Introduction

In the past two years, the coronavirus disease-2019 (COVID-19) pandemic was fueled by the spread of a few

dominant variants of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), as shown in Figure

1. Speciﬁcally, the Alpha and Beta variants contributed to a peak of infections and deaths from October

2020 to January 2021. The Gamma variant caused another peak of infections and deaths in April and May

2021. The Delta variant led to the third wave of COVID-19 infections and deaths around August 2021.

The Omicron (B.1.1.529), which was extraordinary in its infectivity, vaccine breakthrough, and antibody

resistance, created a huge spike in the world’s daily infection record in December 2021 and January 2022.

Omicron BA.2 subvariant rapidly replaced the original Omicron (i.e., BA.1) in March 2022. Around July

2022, Omicron subvariants BA.4 and BA.5 took over BA.2 and became the new dominant SARS-CoV-2

variant. These variant-driven waves of infections are also associated with spikes in deaths and have given

rise to tremendous economic loss. A life-and-death question is: what will be future dominant variants?

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Omicron

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Figure 1: Illustration of daily COVID-19 cases (light blue) and deaths (red) since 2020 [1]. The curves are smoothed by ﬁve-day

averages.

Forecasting and surveillance of emerging SARS-CoV-2 variants are some of the most challenging tasks

of our time. Among about half a million SARS-CoV-2/COVID-19 related publications recorded in Google

Scholar, few accurately foretold the emerging SARS-CoV-2 variants. Accurate and reliable forecasting of

emerging SARS-CoV-2 variants enables policymakers and vaccine makers to plan, leading to enormous

social, economic, and health beneﬁts. To foretell future variants, one must have the full understanding of

the mechanisms of viral evolution, the mechanisms of viral mutations, and the relationship between viral

evolution and viral mutation.

Future variants are created through the SARS-CoV-2 evolution, in which is a SARS-CoV-2 evolves

through changes in its RNA at molecular scale to gain ﬁtness over its counterparts at the host population

scale. At the molecular scale, most mutations occur randomly. Indeed, random genetic drift is a major

mechanism of mutations, resulting in errors in various biological processes, such as replication, transcription,

and translation. Additionally, virus-virus intra-organismic recombination can alter SASR-CoV-2 genes,

which has a stochastic nature too. However, SARS-CoV-2 has a genetic proofreading mechanism facilitated

by the synergistic interactions between RNA-dependent RNA polymerase and non-structure proteins 14

(NSP14) [2, 3]. At the organismic scale, inter-organismic recombination happens but the resulting variants

may not be clinically signiﬁcant. In contrast, host editing of virus genes is known to be a signiﬁcant

mechanism for SARS-CoV-2 mutations [4]. At the population scale, mutations occurring at molecular and

organismic scales are regulated, i.e., enhanced and/or suppressed via natural selection, giving rise to SARS-

CoV-2 variants with increased ﬁtness [5]. Therefore, natural selection is the fundamental driven force for

viral evolution.

It remains to understand what controls the natural selection of SARS-CoV-2. The mechanism of SARS-

CoV-2 evolution was elusive at the beginning of the COVID-19 pandemic. Indeed, the life cycle of SARS-

CoV-2 is extremely sophisticated, involving the viral entry of host cells, the release of the viral genome,

the synthesis of viral NSPs, RNA replication, the transcription, translation, and synthesis of viral structural

proteins, and the packing, assembly, and release of new viruses [6]. The SARS-CoV-2 mutations occur nearly

randomly on all of its 29 genes, as shown in Figure 2. Nonetheless, in early 2020, we hypothesized that SARS-

CoV-2 natural selection is controlled through infectivity-strengthening mutations [5], which primarily occur

at the viral spike (S) protein receptor-binding domain (RBD) that binds with host angiotensin-converting

enzyme 2 (ACE2) to facilitate the viral cell entry [7–11]. Our hypothesis was initially supported by our

genotyping of 15,140 SARS-CoV-2 genomes extracted from patients. We demonstrated that among 89 unique

RBD mutations, the observed frequencies of infectivity-strengthening mutations outpace those of infectivity-

weakening ones in their time evolution. Our infectivity-strengthening mechanism of natural selection was

proven beyond doubt in April 2021, with 506,768 SARS-CoV-2 genomes isolated from patients [12].

29290 Single Mutations in 3607461 hCoV-19 Genomes

Relevant link: Analysis of S protein RBD mutations

ln(Frequency)

NSP1 NPS3 NSP4 3CL NSP6 RdRp Helicase SORF3a EN

GISAID data provided on this website is subject to GISAID’s Terms and Conditions <[Download Summary]>

enabled by data from

20200101 20210425 20220930

Figure 2: Illustration of unique mutations on SARS-CoV-2 genomes extracted from patients. Each dot represents a unique

mutation. The x-axis is the gene position of a mutation and the y-axis represents its observed frequency in the natural

logarithmic scale.

However, we found that not all of the most observable RBD mutations strengthen viral infectivity [13].

This exception took place in the middle and late 2021 when a good portion of the population in many

developed countries was vaccinated. By the genotyping of 2,298,349 complete SARS-CoV-2 genomes, we

discovered vaccination-induced antibody-resistant mutations, which make the virus less infectious [13]. This

discovery leads to a complementary mechanism of natural selection, namely antibody-resistant mutations.

In other words, viral evolution also favors RBD mutations in a population that enable the virus to escape

antibody protection generated from vaccination or infection.

The Omicron variant was the ﬁrst example that was induced by both infectivity strengthening and

antibody resistance mechanisms [13]. It has 32 mutations on the S protein, the main antigenic target of

antibodies [14]. Among them, 15 are on the Omicron RBD, leading to a dramatic increase in SARS-CoV-2

infectivity, vaccine breakthrough, and antibody resistance [15]. The World Health Organization (WHO)

declared Omicron as a variant of concern (VOC) on November 26, 2021. On December 1, 2021, when there

were no experimental data available, we announced our topological artiﬁcial intelligence (AI) predictions

based on the genotyping of viral genomes, biophysics, experimental data of protein-protein interactions,

algebraic topology, and deep learning [15]. We predicted that Omicron is about 2.8 times as infectious as the

Delta and has nearly 90% likelihood to escape vaccines, which would compromise essentially all of existing

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EmergingdominantSARS-CoV-2variantsJiahuiChen1,RuiWang1,YutaHozumi1,GengzhuoLiu1,YuchiQiu1,XiaoqiWei1andGuo-WeiWei1;3;4*1DepartmentofMathematics,MichiganStateUniversity,MI48824,USA.2DepartmentofElectricalandComputerEngineering,MichiganStateUniversity,MI48824,USA.3DepartmentofBiochemistryandMolecularB...

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Emerging dominant SARS-CoV-2 variants Jiahui Chen1 Rui Wang1 Yuta Hozumi1 Gengzhuo Liu1 Yuchi Qiu1 Xiaoqi Wei1and Guo-Wei Wei134

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