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The evolution of the mechanisms of SARS-CoV-2 evolution revealing vaccine-resistant mutations in Europe and America

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Abstract

The importance of understanding SARS-CoV-2 evolution cannot be overemphasized. Recent studies confirm that natural selection is the dominating mechanism of SARS-CoV-2 evolution, which favors mutations that strengthen viral infectivity. We demonstrate that vaccine-breakthrough or antibody-resistant mutations provide a new mechanism of viral evolution. Specifically, vaccine-resistant mutation Y449S in the spike (S) protein receptor-bonding domain (RBD), which occurred in co-mutation [Y449S, N501Y], has reduced infectivity compared to the original SARS-CoV-2 but can disrupt existing antibodies that neutralize the virus. By tracing the evolutionary trajectories of vaccine-resistant mutations in over 1.9 million SARS-CoV-2 genomes, we reveal that the occurrence and frequency of vaccine-resistant mutations correlate strongly with the vaccination rates in Europe and America. We anticipate that as a complementary transmission pathway, vaccine-resistant mutations will become a dominating mechanism of SARS-CoV-2 evolution when most of the world's population is vaccinated. Our study sheds light on SARS-CoV-2 evolution and transmission and enables the design of the next-generation mutation-proof vaccines and antibody drugs.
The evolution of the mechanisms of SARS-CoV-2 evolution
revealing vaccine-resistant mutations in Europe and America
Rui Wang1, Jiahui Chen1and Guo-Wei Wei1,2,3*
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 12, 2021
Abstract
The importance of understanding SARS-CoV-2 evolution cannot be overemphasized. Recent studies
confirm that natural selection is the dominating mechanism of SARS-CoV-2 evolution, which favors mu-
tations that strengthen viral infectivity. We demonstrate that vaccine-breakthrough or antibody-resistant
mutations provide a new mechanism of viral evolution. Specifically, vaccine-resistant mutation Y449S in
the spike (S) protein receptor-bonding domain (RBD), which occurred in co-mutation [Y449S, N501Y], has
reduced infectivity compared to the original SARS-CoV-2 but can disrupt existing antibodies that neutral-
ize the virus. By tracing the evolutionary trajectories of vaccine-resistant mutations in over 1.9 million
SARS-CoV-2 genomes, we reveal that the occurrence and frequency of vaccine-resistant mutations corre-
late strongly with the vaccination rates in Europe and America. We anticipate that as a complementary
transmission pathway, vaccine-resistant mutations will become a dominating mechanism of SARS-CoV-
2 evolution when most of the world’s population is vaccinated. Our study sheds light on SARS-CoV-2
evolution and transmission and enables the design of the next-generation mutation-proof vaccines and
antibody drugs.
Keywords: COVID-19, SARS-CoV-2, evolution, vaccine-resistant mutation, vaccine-breakthrough, infectiv-
ity, Y449S
*Corresponding author. Email: weig@msu.edu
1
arXiv:2110.04626v1 [q-bio.PE] 9 Oct 2021
1 Introduction
Started in late 2019, the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory
syndrome coronavirus 2 (SARS-CoV-2) has had devastating impacts worldwide, which has plunged the
world into an economic recession. Although several authorized vaccines have offered promise to control
the disease in early 2021, the emergence of multiple variants of SARS-CoV-2 indicates that the combat with
SARS-CoV-2 will be protracted. At this stage, almost all SARS-CoV-2 vaccines and monoclonal antibod-
ies (mAbs) are targeted at the spike (S) protein [1], while mutations on the S protein have been verified
to link to the efficacy of existing vaccines and viral infectivity [2, 3]. Therefore, it is imperative to under-
stand the mechanisms of viral mutations, especially on the S gene of SARS-CoV-2, which will promote the
development of mutation-proof vaccines and mAbs.
The mechanism of mutagenesis is driven by various competitive processes [4–8], which can be catego-
rized into 3 different scales with many factors as illustrated in Figure 1 a: 1) the molecular scale, 2) the
organism scale, and 3) the population scale. From the molecular-scale perspective, the random shifts, repli-
cation errors, transcription errors, translation errors, viral proofreading, and viral recombination are the
main driven sources. Moreover, the host gene editing induced by the adaptive immune response [8] and
the recombination between the host and virus are the key-driven factors at the organism level. Furthermore,
the natural selection popularized by Charles Darwin is a critical process, which favors mutations that have
reproductive advantages for the virus to have adaptive traits in evolution. Such complicated mechanisms
of viral mutagenesis make the comprehension of viral transmission and evolution a grand challenge.
Although there are 28,780 unique single mutations distributed evenly on the whole SARS-CoV-2 genome,
the mutations on the S gene stand out among all 29 genes on SARS-CoV-2 due to the mechanism of viral
infection. Under assistant with host transmembrane protease, serine 2 (TMPRSS2), SARS-CoV-2 enters the
host cell by interacting with its S protein and the host angiotensin-converting enzyme 2 (ACE2) [9] (See
Figure 1 b). Later on, antibodies will be generated by the host immune system, aiming to eliminate the in-
vading virus through direct neutralization or non-neutralizing binding [10,11], which makes the S protein
the main target for the current vaccines. Specifically, there is a short immunogenic fragment located on the S
protein of SARS-CoV-2 that can facilitate the SARS-CoV-2 S protein binding with ACE2, which is called the
receptor-binding domain (RBD) [12]. Studies have shown that the binding free energy (BFE) between the S
RBD and the ACE2 is proportional to the infectivity [9,13–16]. Therefore, tracking and monitoring the RBD
mutations and their corresponding BFE changes will expedite understanding the infectivity, transmission,
and evolution of SARS-CoV-2, especially for the new SARS-CoV-2 variants, such as Alpha, Beta, Gamma,
Delta, and Lambda, etc. [17]
The current prevailing variants Alpha, Beta, Gamma, Delta, Kappa, Theta, Lambda, and Mu carry at
least one vital mutation at residues 452 and 501 on the S RBD. Notably, in July 2020, we successfully pre-
dicted that residues 452 and 501 ”have high changes to mutate into significantly more infectious COVID-19
strains” [18]. In the same work, we hypothesized that “natural selection favors those mutations that en-
hance the viral transmission” and provided the first evidence for infectivity-based natural selection. In
other words, we revealed the mechanism of SARS-CoV-2 evolution and transmission based on very limited
genome data in July 2020 [18]. Additionally, we predicted three categories of RBD mutations: 1) most likely
(1149 mutations), 2) likely (1912 mutations), and 3) unlikely (625 mutations) [18]. Up to now, all of the
RBD mutations we detected fall into our first category [3, 19]. Until now, all of the top 100 most observed
RBD mutations have BFE change greater than the average BFE changes of -0.28kcal/mol (the average BFE
changes for all RBD mutations [20]). It is an extremely low odd (i.e., 1
1.27×1030 ) for 100 RBD mutations to
accidentally have BFE changes simultaneously above the average value, which confirms our hypothesis
that the transmission and evolution of new SARS-CoV-2 variants are governed by infectivity-based natural
selection, despite all other competing mechanisms [18]. Our predictions rely on algebraic topology [21–23]-
assisted deep learning [18, 24], but have been extensively validated [3, 25]. However, infectivity is not the
1
only transmission pathway that governs viral evolution. Vaccine-resistant mutations or more precisely,
antibody-resistant mutations, that can disrupt the protection of antibodies has become a viable mechanism
for new variants to transmit among the vaccinated population since the vaccine was put on the market. In
early January 2021, we have predicted that RBD mutations W353R, I401N, Y449D, Y449S, P491R, P491L,
Q493P, etc., will weaken most antibody bindings to the S protein [3]. Later on, we have provided a list
of most likely vaccine escape RBD mutations with high frequency, including S494P, Q493L, K417N, F490S,
F486L, R403K, E484K, L452R, K417T, F490L, E484Q, and A475S [19]. Moreover, we have pointed out that
Y449S and Y449H are two vaccine-resistant mutations, and “Y449S, S494P, K417N, F490S, L452R, E484K,
K417T, E484Q, L452Q, and N501Y” are the top 10 mutations that will disrupt most antibodies with high-
frequency [20]. As mentioned in Ref. [26], RBD mutations such as E484K/A, Y489H, Q493K, and N501Y
found in late-stage evolved S variants “confer resistance to a common class of SARS-CoV-2 neutralizing
antibodies”, which suggests the viral evolution is also regulated by vaccine-resistant mutations.
The objective of this work is to analyze the evolution of the mechanisms of SARS-CoV-2 evolution,
driven by complementary viral transmission pathways. We demonstrate how the interplay among molecular-
scale, organism-scale, population-scale mechanisms of SARS-CoV-2 mutations have affected the evolution
of SARS-CoV-2. As a primary driven source of mutagenesis, the molecular-based mechanisms such as
random shifts, transcription errors, proofreading, etc., changing the genetic information initially. Next,
gene editing takes charge of the organism-based mechanism, suggesting the host immune response to the
virus [8]. Then, the population-level mechanism governs the transmission pathways of viral evolution. Two
complementary pathways (infectivity and vaccine-resistance) regulated by natural selection become the
preponderance of evolution-driven force. The RBD mutations regulated by infectivity-based pathways ex-
ist in the prevailing variants, while the mutations regulated by the vaccine-resistant pathway start to emerge
in countries with relatively high vaccination rates. In this work, 1,983,328 complete SARS-CoV-2 genomes
that isolate from patients are decoded by single nucleotide polymorphism (SNP) calling, from where a total
of 28,780 unique single mutations are detected. Among them, 737 RBD mutations are discovered up to
September 20, 2021 (The detailed information can be found in the Supporting Information S5). Based on
our comprehensive topology-based artificial intelligence (AI) model to predict RBD mutation-induced BFE
changes of RBD and ACE2/antibody complexes [3,18], the transmission trajectory of vaccine-resistant RBD
mutations will be analyzed (The detailed information about methods and model can be viewed in the Sup-
porting Information S1 and S2). Moreover, vaccine-resistant RBD mutation Y449S that has been found in
more than 1000 isolates will be discussed. Furthermore, the vaccination rates of 12 countries where Y449S
is distributed are also analyzed, which provides a reliable explanation of the relation between the emer-
gence of vaccine-resistant mutations and the vaccination rate. Such understanding of two complementary
transmission pathways will shed light on the long-term efficacy of S-targeted antibodies countermeasures
and benefit the development of next-generation mutation-proof vaccines and mAbs.
2 Results
2.1 Evolutionary trajectories of viral RBD single mutations
Studying the mechanisms of SARS-CoV-2 mutagenesis is beneficial to the understanding of viral transmis-
sion and evolution. The mainly driven force of viral evolution is regulated by natural selection, which
is employed by two complementary transmission pathways: 1) infectivity-based pathway and 2) vaccine-
resistant pathway. We have discussed the infectivity-based pathways in Ref. [20] and [27]. This section
focuses on the vaccine-resistant pathway and its impact on the transmission and evolution of SARS-CoV-2.
To understand the mechanisms of vaccine-resistant mutations, we first analyze 1,983,328 complete SARS-
CoV-2 genomes, and a total of 28,780 unique single mutations are decoded. Among them, there are 737
non-degenerate RBD mutations. The infectivity of SARS-CoV-2 is proportional to the BFE between the S
2
Y449
Mechanism of
SARS-CoV-2
Mutagenesis
Recomb
Proof
Trans
Transcr
Rep
Random
Molecular scale
Organism
scale
Population
scale
Gene Natural
editing
Recomb
Host cell ACE2
a
b
c
d
Spike
TMPRSS2
ACE2
SARS-CoV-2
227
039 24
17 51
10 18
053 92352638 62 037 30
1
85
T478K
L452Q
N440K
L452R
N501Y
N501T
F490S
A475V
P348L
N439K
V367F
K417N
G446V
N440S
A520S
R357K
A522S
R346K
E484K
S494P
S477N
K417N
E484Q
D427N
Y449S
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BFE change
Natural log of frequency
Vaccine-resistant mutation
Spike
Figure 1: aThe mechanism of mutagenesis. Nine mechanisms are grouped into three scales: 1) molecular-based mechanism (green
color); 2) organism-based mechanism (red color); 3) population-based mechanism (blue color). The random shifts (Random), repli-
cation error (Rep), Transcription error (Transcr), viral proofreading (Proof), and recombination (Recomb) are the six molecular-based
mechanisms. The gene editing and the host-virus recombination are the organism-based mechanism. In addition, the natural selection
(Natural) is the population-based mechanism, which is the mainly driven source in the transmission of SARS-CoV-2. bA sketch of
SARS-CoV-2 and its interaction with host cell. cIllustration of 25 single-site RBD mutations with top frequencies. The height of each
bar shows the BFE change of each mutation, the color of each bar represents the natural log of frequency of each mutation, and the
number at the top of each bar means the AI-predicted number of antibody and RBD complexes that may be significantly disrupted
by a single site mutation. dIllustration of SARS-CoV-2 S protein with human ACE2. The blue chain represents the human ACE2, the
pink chain represents the S protein, and the purple fragment on the S protein points out the two vaccine-resistant mutations Y449S/H.
RBD and ACE2 [9, 13–16]. Therefore, the BFE change induced by a specific RBD mutation reveals whether
the RBD mutation is an infectivity-strengthen or an infectivity-weaken mutation. Similarly, the BFE change
between S RBD and antibody induced by a given mutation reveals whether this mutation will strengthen
the binding between S and antibody or not. Up to now, we have collected 130 antibody structures (see the
Supporting Information S4), which includes Food and Drug Administration (FDA)-approved mAbs from
Eli Lilly and Regeneron. For a specific RBD mutation, its antibody disruption count shows the number
of antibodies that have antibody-S BFE changes smaller than -0.3 kcal/mol. The ACE2-S and antibody-S
BFE changes induced by RBD mutations are predicted from our TopNetTree model [18], which is available
at TopNetmAb. All of the predicted BFE changes induced by RBD mutations can be found at Mutation
Analyzer. Figure 1 cillustrates the top 25 most observed RBD mutations. The height and color of each
bar represent the ACE2-S BFE changes and frequency of each RBD mutation. The number at the top of
each bar shows the antibody disruption count of each mutation. The detailed information can be viewed
in Supplementary Information S4. It can be seen that 23 mutations have positive ACE2-S BFE changes,
suggesting they are regulated by the infectivity-based transmission pathway. Howbeit, 2 RBD mutations
3
D427N and Y449S, have negative BFE changes. Notably, mutation Y449S has a significantly negative BFE
change (-0.8112 kcal/mol) and a pretty large antibody disruption count (89), revealing a non-typical mech-
anism of mutagenesis. Such a mutation with significantly negative ACE2-S BFE change together with a
high antibody disruption count is called a vaccine-resistant or antibody-resistant mutation. Figure 1 dis
the illustration of SARS-CoV-2 S protein (blue color) with human ACE2 (pink color), and the Y449 residue
(purple color) is located on the random coil of the S protein. Among all of the vaccine-resistant mutations,
Y449S has the highest frequency (1189). In addition, at residue 449, mutations Y449H, Y449N, Y449D are
all vaccine-resistant mutations that have been observed in more than 20 SARS-CoV-2 genome isolates.
To track the evolution trajectory of vaccine-resistant mutations, the BFE changes, log2 enrichment ra-
tios 1, and log10 frequencies of RBD mutations are analyzed from April 30, 2020, to August 23, 2021, in
every 60 days, as illustrated in Figure 2. Here, the top 100 most observed RBD mutations are displayed.
In Figure 2 a, red stars mark the vaccine-resistant mutations that have negative BFE changes. Although
a few vaccine-resistant mutations S438F, I434K, Y505C, and Q506K were detected before November 2020,
they had relatively low frequencies. However, since December 2020, such vaccine-resistant mutations were
no longer in the top 100 most observed RBD mutation list, suggesting that in this period, the evolution of
SARS-CoV-2 is mainly regulated by natural selection through the infectivity-based transmission pathway.
Notably, in May 2021, two vaccine-resistant mutations Y449S and Y449H, came back to the top 100 most
observed RBD mutation list. In addition, Y449S has a relatively high frequency. Such finding indicates
that natural selection not only favors those mutations that enhance the transmission but also those muta-
tions that can disrupt plenty of antibodies since SARS-CoV-2 vaccines started to provide protection among
populations in early May. Similarly, patterns can be found in Figure 2 b, suggesting our AI-predicted BFE
changes are highly consistent with the deep mutational enrichment ratio from experiments [28].
2.2 Evolutionary trajectories of viral RBD co-mutations
The vaccine-resistant mutations are usually found along with other RBD mutations. Therefore, analyz-
ing the time evolution of RBD co-mutations offers a better understanding of the mechanisms of vaccine-
resistant mutations. Figures 3 a,b, and cillustrate the time evolution of 2, 3, and 4 RBD co-mutations
with their corresponding BFE changes every 30 days. Here, the height and color of each bar represent the
log10 frequency and total BFE change induced by a given RBD co-mutation. Considering the number of
co-mutations is quite low in the year 2020, the time range of analysis is set to [01/25/2021, 08/23/2021]
for the time evolution analysis of 2 co-mutations. For 3 and 4 co-mutations, their time ranges are set to
[02/04/2021, 08/23/2021] and [04/25/2021, 08/23/2021], respectively. In Figure 3 a, red star marks the 2
co-mutations with significantly negative BFE changes. At the end of March 2021, vaccine-resistant muta-
tion Y449D showed up with mutation N501Y in some genome isolates, resulting in a negative BFE change
(-0.473kcal/mol) and a high antibody disruption count (98) for RBD 2 co-mutation [Y449D, N501Y]. How-
ever, its global frequency is relatively low. Since late April 2021, vaccine-resistant mutation Y449S showed
up with N501Y, making RBD co-mutation [Y449S, N501Y] one of the most prevailing vaccine-resistant co-
mutations. Figure 3 dshows the top 25 most observed RBD co-mutations, the length and color of each bar
represent the total BFE change and the natural log of frequency of an RBD co-mutation. The number at
the side of each bar is the count of antibody disruption. Among the 25 most observed RBD co-mutations,
[Y449S, N501Y] is the only co-mutation with a significantly negative BFE change and extremely high anti-
body disruption count (94). Observing the evolution trajectory of [Y449S, N501Y] shows that the infectivity
transmission pathway regulated by natural selection in the population level is the major evolution-driven
force of SARS-CoV-2 mutagenesis before March 2021. Starting in January 2021, several vaccines were au-
thorized for emergent use. Two months later, since many people have been protected by the vaccines, the
mutations that disrupt the binding between the S and antibodies are able to transmit among vaccinated
1Log2 enrichment ratio is collected from the experimental deep mutation enrichment data in Ref. [28]
4
0
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02/24/21
04/25/21
06/24/21
08/23/21
BFE change Log2 enrichment change
-2 -1 0
1
-3 2
-1 01
Log10(Frequency)
ab
10/27/20
12/26/20
06/29/20
10/27/20
12/26/20
06/29/20
*
*
*
*
*
*
*
*
*
*
*
*
S438F
I434K
*
Q506K
**
Y505C
S438F
Y505C
Q506K
*
Y505C
Y449S Y449H
*
*
Y449S Y449H
*
*
Y449S Y449H
Y449S Y449H
Y505C
Y505C
Q506K
*
S438F
I434K
*
Q506K
**
Y505C
S438F
Q506K
*
Q506K
*
Figure 2: Most significant RBD mutations. aTime evolution of RBD mutations with its mutation-induced BFE changes per 60-day from
April 30, 2020, to August 31, 2021. Here, only the top 100 most observed RBD mutations are displayed. The height and color of each
bar represent the log frequency and ACE-S BFE change induced by a given RBD mutation. The red star marks the vaccine-resistant
mutations with significantly negative BFE changes. bTime evolution of RBD mutations with its experimental mutation-induced
log2 enrichment ratio changes per 60-day from April 30, 2020, to August 31, 2021. The height and color of each bar represent the
log frequency and enrichment ratio change induced by a given RBD mutation. The red star marks vaccine-resistant mutations with
significantly negative BFE changes.
people, especially in countries with high vaccination rates. Such a vaccine-resistant pathway reduces the
efficacy of vaccines and antibody therapies, indicating the combat with COVID-19 will be a prolonged
battle.
Similar time evolution trajectories are drown for RBD 3 and 4 co-mutations (see Figures 3 band c).
Apparently, there are no vaccine-resistant 3 and 4 co-mutations at present, which indicates vaccine-resistant
5
co-mutation [Y449S, N501Y] is quite unique.
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c
Total BFE change (kcal/mol) 0 1 2
01/25/21
02/24/21
03/26/21
04/25/21
05/25/21
06/24/21
07/24/21
08/23/21
04/25/21 05/25/21
02/24/21
03/26/21
06/24/21
07/24/21
08/23/21
04/25/21 05/25/21
06/24/21
07/24/21
08/23/21
*
*
*
*
*
*
d
[Y449D, N501Y]
[Y449S, N501Y]
[Y449S, N501Y]
[Y449S, N501Y]
[Y449S, N501Y]
[Y449S, N501Y]
59
42
49
43
82
46
41
40
53
40
41
42
59
31
58
29
26
56
27
72
30
42
90
64
24
58
38
25
27
26
26
47
81
60
73
24
51
24
52
41
54
30
82
35
13
25
78
2
38
94
[L452R, T478K, N501Y]
[L452R, T478K, V483F]
[A411S, L452R, T478K]
[L452R, T478K, P479S]
[K417N, L452R, T478K]
[G446V, L452R, T478K]
[L452R, T478K, A522V]
[V367L, L452R, T478K]
[L452R, T478K, E484Q]
[L452R, T478K]
[L452R, S477I, T478K]
[Q414H, L452R, T478K]
[L452Q, F490S]
[L452M, N501Y]
[L452R, N501Y]
[V483F, N501Y]
[E471Q, N501Y]
[F490L, N501Y]
[A348S, N501Y]
[F490S, N501Y]
[A475V, N501Y]
[P384L, N501Y]
[K417N, E484K, N501Y]
[R346K, E484K, N501Y]
[V367F, N501Y]
[K417N, N501Y]
[N440K, E484K]
[N440S, N501Y]
[N501Y, A520S]
[N501Y, A522S]
[T478I, N501Y]
[P384S, N501Y]
[K417T, E484K, N501Y]
[E484K, N501Y]
[S494P, N501Y]
[S477R, N501Y]
[R346S, L452R]
[V367L, N501Y]
[L452R, E484Q]
[G496S, N501Y]
[E484Q, N501Y]
[T385N, N501Y]
[K417T, D427N, E484K, N501Y]
[L455F, N501Y]
[Q414K, N450K]
[D427N, N501Y]
[E484K, S494P]
[S477N, A522S]
[S477N, E484K]
[Y449S, N501Y]
Natural log of frequency
00.5 1 1.5 2
Total BFE changes
Beta
Gamma
Mu
Theta
Lambda
Delta
5
6
7
8
9
10
11
12
13
d
Log10(Frequency)
Vaccine-resistant mutation
Figure 3: RBD co-mutation analysis. aTime evolutionary trajectory of RBD 2 co-mutations with its mutation-induced BFE changes
per 30-day from January 25, 2021, to August 23, 2021. The height and color of each bar represent the log frequency and ACE-S
BFE change induced by a given RBD mutation. Red stars mark the 2 co-mutations with significantly negative BFE changes. bTime
evolutionary trajectory of RBD 3 co-mutations with its mutation-induced BFE changes per 30-day from February 24, 2021, to August
23, 2021. The height and color of each bar represent the log frequency and ACE-S BFE change induced by a given RBD mutation. c
Time evolutionary trajectory of RBD 4 co-mutations with its mutation-induced BFE changes per 30-day from April 25, 2021, to August
23, 2021. The height and color of each bar represent the log frequency and ACE-S BFE change induced by a given RBD mutation. d
Illustration of top 25 most observed RBD co-mutations. Here, the length of each bar represents the total ACE2-S BFE changes induced
by a specific RBD co-mutation, the color of each bar represents the natural log frequency of each co-mutation, and the number at the
side of each bar is the AI-predicted antibody disruption count.
2.3 Vaccine-resistant mutations and vaccination rates in 12 countries
Analysis of the vaccination trends and vaccine-resistant mutations leads to a fundamental understanding
of the transmission and evolution of vaccine-resistant mutations. We investigate the distribution and time
evolution of vaccine-resistant RBD mutation Y449 in 12 countries. As the most observed vaccine-resistant
RBD mutation, Y449S has been detected in 12 countries, including Denmark (DK), the United Kingdom
(UK), France (FR), Bulgaria (BG), the United States (US), Brazil (BR), Sweden(SE), Canada (CA), Germany
(DE), Switzerland (CH), Spain (ES), and Belgium (BE), as illustrated in Figure 4 a. Here, 12 countries that
Y449S was found in are in blue. The darker the blue is, the higher frequency of Y449S will be. The number
6
on the side of each country is the total positive cases up to August 31, 2021. Although DK has the smallest
positive cases among 12 countries, the frequency of Y449S is the highest. More than 800 patients carry
vaccine-resistant mutation Y449S in DK. All of the Y449S-related cases are found in Europe and America,
where the vaccination rates in those areas are relatively high. Figure 4 bshows the time evolution of
vaccination ratio and the frequency of Y449S in the 12 countries as mentioned above in 30-day periods. The
x-axis records the date, which ranges from 12/26/2020 to 08/23/2021. The left-hand side y-axis shows the
frequency of Y499S (red lines), and the right-hand side y-axis shows the vaccination ratio. In addition, the
orange region shows at least one dose ratio, while the purple region means the fully vaccinated ratio. It can
be seen that Y449S was first found in BG and the US in December 2020. However, the frequency of Y449S in
BG and the US is quite low before April 2021. After April 2021, Y449S has been quickly spread out to other
ten countries. Among them, the total number of cases related to Y449S has a rapid increment tendency,
especially in DK, the UK, and FR. Notably, all these three countries have relatively high vaccination ratios
(over 70% up to late August 2021). It is worthy to mention that the frequency of Y449S is low in DE, CH,
ES, and BE, etc., which is mainly due to the first Y449-related case in these countries was detected after
June 2021. Since then, Delta variants dominated in the prevailing variants, which gave Y449S a limited
chance to spread out rapidly. Moreover, from Figure 4 , it can be seen that the frequency of Y449S has a
similar growing tendency as the fully vaccinated ratio, suggesting that the vaccine-resistant mutations will
gradually become one of the main evolution driven forces of SARS-CoV-2, especially in those areas with
high vaccination rates.
3 Conclusion
Due to the appearance of multiple mutations known to reduce the efficacy of antibody neutralization gen-
erated by vaccines, it is vital to better comprehend the mechanisms of SARS-CoV-2 mutagenesis, which will
be of paramount importance to understanding the transmission and evolution of SARS-CoV-2. The driven
forces of mutagenesis can be categorized into three groups: 1) molecular-scale mechanism, 2) organism-
scale mechanism, and 3) population-level mechanism. As an initial driven source of mutagenesis, the ge-
netic information is changed by random shifts, viral proofreading, translation errors, etc., which all belong
to molecular-scale mechanisms. Also, regulated by the host immune system, host gene editing, and rarely
occurring host-viral recombination are two organism-scale mechanisms. The molecular- and organism-
scale mechanisms provide a large number of candidate mutations in the SARS-CoV-2 genome, while it is
the population-scale mechanism that determines what mutations become dominating.
Natural selection is a population-scale mechanism, which promotes the surge of the emerging SARS-
CoV-2 variants by two complementary pathways: infectivity and vaccine resistance. The early stage of
SARS-CoV-2 evolution was entirely dominated by infectivity-strengthening mutations. However, since
late March 2021, once vaccines had provided protection to highly vaccinated populations, several vaccine-
resistant mutations such as Y449S and Y449H have been observed relatively frequently. Considering there
is still a good portion of the population who are not vaccinated, infectivity-strengthen mutations still dom-
inate in the prevailing and future variants. However, antibody-resistant mutations will become a major
mechanism of transmission once most of the populations carrying antibodies either through vaccination
and infection. Our studies are valuable to the development of the next-generation vaccines and mAbs,
which are of great importance in the long-term combat with SARS-CoV-2.
Data and model availability
The SARS-CoV-2 SNP data in the world is available at Mutation Tracker. The most observed SARS-CoV-2
RBD mutations are available at Mutaton Analyzer. The TopNetTree model is available at TopNetmAb. The
detailed methods can be found in the Supporting Information S1 and S2. The validation of our predictions
7
Figure 4: aDistribution of vaccine-resistant mutation Y449S. The color bar represents the log10 frequency of Y449S in 12 countries:
Denmark (DK), the United Kingdom (UK), France (FR), Bulgaria (BG), the United States (US), Brazil (BR), Sweden(SE), Canada (CA),
Germany (DE), Switzerland (CH), Spain (ES), and Belgium (BE). The number located at the side of the country shows the total positive
SARS-CoV-2 cases up to August 31. bTime evolution of vaccination rate and the frequency of Y449S in 12 countries from December
26, 2020, to August 23, 2021. The data is collected per 30-day. The red line shows the frequency of mutation Y449S. The orange and
purple areas represent at least one dose rate and fully vaccinated rate in each country.
with experimental data can be located in Supporting Information S3. The information of 130 antibodies
with their corresponding PDB IDs, the SARS-CoV-2 S protein RBD SNP and non-degenerate co-mutations
data can be found in Section S5 of the Supporting Information.
Supporting information
The supporting information is available for
S1 Supplementary data pre-processing and feature generation methods
S2 Supplementary machine learning methods
S3 Supplementary validation: validations of our machine learning predictions with experimental data.
S4 Supplementary table: the top 25 most observed S protein RBD mutations up to September 20, 2021.
S5 Supplementary data: The Supplementary Data.zip contains four files: S5.0.1: antibodies disruptmutation.csv
shows the name of antibodies disrupted by mutations. S5.0.2: antibodies.csv lists the PDB IDs for all
of the 130 SARS-CoV-2 antibodies. S5.0.3: RBD comutation residue 09202021.csv lists all of the SNPs
8
of RBD co-mutations up to September 20, 2021. S5.0.4: Track Comutation 09202021.xlsx preserves
all of the non-degenerate RBD co-mutations with their frequencies, antibody disruption counts, total
BFE changes, and the first detection dates and countries.
Acknowledgment
This work was supported in part by NIH grant GM126189, NSF grants DMS-2052983, DMS-1761320, and
IIS-1900473, NASA grant 80NSSC21M0023, Michigan Economic Development Corporation, MSU Founda-
tion, Bristol-Myers Squibb 65109, and Pfizer.
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... The first two mechanisms provide numerous candidate mutations in the SARS-CoV-2 genome. The population-level mechanism determines what mutations are predominant by natural selection that have two pathways of infectivity and vaccine resistance [8]. In the summer of 2020, Michigan State University established a model of infection based on natural selection and predicted that SARS-CoV-2 was more infectious on the mutations of residues 452, 489, 500, 501, and 505 in the RBD region, after computing the BFE changes of possible mutations by integrating genotyping, deep learning, biophysics, and mathematics [14]. ...
... Recently, vaccine-resistant mutations that are emerged after vaccinations, have been confirmed. Vaccine-resistant mutations with negative BFE changes, that disrupted the binding between the spike protein and antibodies, have been observed frequently, such as Y449S and Y449H, after many people in many countries were vaccinated in high rates [8]. The trend of increasing frequency of vaccine-resistant mutations correlated strongly with the proportion of fully vaccinated in Europe and America. ...
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Many individuals mount nearly identical antibody responses to SARS-CoV-2. To gain insight into how the viral spike (S) protein receptor-binding domain (RBD) might evolve in response to common antibody responses, we studied mutations occurring during virus evolution in a persistently infected immunocompromised individual. We use antibody Fab/RBD structures to predict, and pseudotypes to confirm, that mutations found in late-stage evolved S variants confer resistance to a common class of SARS-CoV-2 neutralizing antibodies we isolated from a healthy COVID-19 convalescent donor. Resistance extends to the polyclonal serum immunoglobulins of four out of four healthy convalescent donors we tested and to monoclonal antibodies in clinical use. We further show that affinity maturation is unimportant for wildtype virus neutralization but is critical to neutralization breadth. As the mutations we studied foreshadowed emerging variants that are now circulating across the globe, our results have implications to the long-term efficacy of S-directed countermeasures.
Preprint
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Recent months have seen surges of SARS-CoV-2 infection across the globe along with considerable viral evolution. Extensive mutations in the spike protein of variants B.1.1.7, B1.351, and P.1 have raised concerns that the efficacy of current vaccines and therapeutic monoclonal antibodies could be threatened. In vitro studies have shown that one mutation, E484K, plays a crucial role in the loss of neutralizing activity of some monoclonal antibodies as well as most convalescent and vaccinee sera against variant B.1.351. In fact, two vaccine trials have recently reported lower protective efficacy in South Africa, where B.1.351 is dominant. To survey for these novel variants in our patient population in New York City, PCR assays were designed to identify viruses with two signature mutations, E484K and N501Y. We observed a steady increase in the detection rate from late December to mid-February, with an alarming rise to 12.3% in the past two weeks. Whole genome sequencing further demonstrated that most of our E484K isolates (n=49/65) fell within a single lineage: NextStrain clade 20C or Pangolin lineage B.1.526. Patients with this novel variant came from diverse neighborhoods in the metropolitan area, and they were on average older and more frequently hospitalized. Phylogenetic analyses of sequences in the database further reveal that this B.1.526 variant is scattered in the Northeast of US, and its unique set of spike mutations may also pose an antigenic challenge for current interventions.
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The SARS-CoV-2, previously called a novel coronavirus, that broke out in the Wuhan city of China caused a significant number of morbidity and mortality in the world. It is spreading at peak levels since the first case reported and the need for vaccines is in immense demand globally. Numerous treatment and vaccination strategies that were previously employed for other pathogens including coronaviruses are now beingbeen adopted to guide the formulation of new SARS-CoV-2 vaccines.Several vaccine targets can be utilized for the development ofthe SARS-CoV-2 vaccine. In this review, wehighlightedthe potential of various antigenic targets and other modes of formulating an effective vaccine against SARS-CoV-2. There are a varying number of challenges encountered during developing the most effective vaccines, andmeasures for tackling such challenges will assist in fast pace development of vaccines. This review will give a concise overview of various aspects of the vaccine development process against SARS-CoV-2 including 1) potential antigen targets 2) different vaccination strategies from conventional to novel platforms, 3) ongoing clinical trials, 4) varying challenges encountered during developing the most effective vaccine and the futuristicapproaches.
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Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (Covid-19), which is most frequently mild yet can be severe and life-threatening. Virus-neutralizing monoclonal antibodies are predicted to reduce viral load, ameliorate symptoms, and prevent hospitalization. Methods In this ongoing phase 2 trial involving outpatients with recently diagnosed mild or moderate Covid-19, we randomly assigned 452 patients to receive a single intravenous infusion of neutralizing antibody LY-CoV555 in one of three doses (700 mg, 2800 mg, or 7000 mg) or placebo and evaluated the quantitative virologic end points and clinical outcomes. The primary outcome was the change from baseline in the viral load at day 11. The results of a preplanned interim analysis as of September 5, 2020, are reported here. Results At the time of the interim analysis, the observed mean decrease from baseline in the log viral load for the entire population was −3.81, for an elimination of more than 99.97% of viral RNA. For patients who received the 2800-mg dose of LY-CoV555, the difference from placebo in the decrease from baseline was −0.53 (95% confidence interval [CI], −0.98 to −0.08; P=0.02), for a viral load that was lower by a factor of 3.4. Smaller differences from placebo in the change from baseline were observed among the patients who received the 700-mg dose (−0.20; 95% CI, −0.66 to 0.25; P=0.38) or the 7000-mg dose (0.09; 95% CI, −0.37 to 0.55; P=0.70). On days 2 to 6, the patients who received LY-CoV555 had a slightly lower severity of symptoms than those who received placebo. The percentage of patients who had a Covid-19–related hospitalization or visit to an emergency department was 1.6% in the LY-CoV555 group and 6.3% in the placebo group. Conclusions In this interim analysis of a phase 2 trial, one of three doses of neutralizing antibody LY-CoV555 appeared to accelerate the natural decline in viral load over time, whereas the other doses had not by day 11. (Funded by Eli Lilly; BLAZE-1 ClinicalTrials.gov number, NCT04427501.)