As the COVID-19 pandemic has progressed, new variants of the virus SARS-CoV-2 have emerged that are more infectious than the original form. The variants known as Alpha, Beta and Gamma have mutations in a protein on the virus’s surface that is vital for attaching to cells and infecting them. This protein, called Spike, carries out its role by binding to ACE2, a protein on the surface of human cells. Mutations on Spike are found on the region where it binds to ACE2.

The interaction between these two proteins appears to be important to the behaviour of SARS-CoV-2, but the impact of individual mutations in Spike is unknown. In addition, some people have different variants of ACE2 with mutations in the region that interacts with Spike, but it is not known whether this affects these people’s risk of contracting COVID-19.

To answer these questions, Barton et al. measured the precise effect of mutations in Spike and ACE2 on the strength of the interaction between the two proteins. The experiments showed that three of the five common Spike mutations in the Alpha, Beta and Gamma SARS-CoV-2 variants strengthened binding to ACE2. The two mutations that weakened binding were only found together with other mutations that strengthened binding. This meant that the Spike proteins in all three of these SARS-CoV-2 variants bind to ACE2 more strongly than the original form. The experiments also showed that two common variants of ACE2 also increased the strength of binding to Spike. Interestingly, one of these ACE2 variants reversed the effect of a specific SARS-CoV-2 mutation, suggesting that carriers would be resistant to SARS-CoV-2 variants with this mutation.

Identifying the precise effects of Spike mutations on ACE2 binding helps understand why new variants of SARS-CoV-2 spread more rapidly. This could help to identify concerning new variants before they spread widely and inform the response by health authorities. The finding that two common ACE2 variants bind more strongly to Spike suggests that people with these mutations could be more susceptible to SARS-CoV-2.

Since its identification in 2019, a coronavirus able to induce a severe acute respiratory syndrome in humans, SARS-CoV-2, has resulted in arguably the most severe infectious disease pandemic in 100 years. To date, more than 135 million people have been infected, resulting in the deaths from the resulting disease, COVID-19, of more than 3 million people (WHO, 2021), and measures introduced to control spread have had harmful social and economic impacts. Fortunately, effective vaccines have been developed, and a global vaccination programme is underway (Mahase, 2021). New SARS-CoV-2 variants of concern are emerging that are making containment of the pandemic more difficult, perhaps by increasing transmissibility of the virus (Davies et al., 2021; Korber et al., 2020; Volz et al., 2021b; Volz et al., 2021b; Washington et al., 2021) and/or its resistance to protective immunity induced by previous infection or vaccines (Darby and Hiscox, 2021; Dejnirattisai et al., 2021; Garcia-Beltran et al., 2021; Madhi et al., 2021a; Madhi et al., 2021b; Mahase, 2021; Volz et al., 2021b; Volz et al., 2021b).

The SARS-CoV-2 virus enters cells following an interaction between the Spike (S) protein on its surface with angiotensin-converting enzyme 2 (ACE2) on cell surfaces (V’kovski et al., 2021). The receptor-binding domain (RBD) of the Spike protein binds the membrane-distal portion of the ACE2 protein. The S protein forms a homotrimer, which is cleaved shortly after synthesis into two fragments that remain associated non-covalently: S1, which contains the RBD, and S2, which mediates membrane fusion following the binding of Spike to ACE2 (V’kovski et al., 2021). During the pandemic, mutations have appeared in the Spike protein that may increase transmissibility (Davies et al., 2021; Korber et al., 2020; Richard et al., 2021; Volz et al., 2021b; Volz et al., 2021b; Washington et al., 2021). One that emerged early in Europe, D614G, and quickly became dominant globally (Korber et al., 2020), increases the density of intact Spike trimer on the virus surface by preventing premature dissociation of S1 from S2 following cleavage (Zhang et al., 2021; Zhang et al., 2020). A later mutation, N501Y, which has appeared in multiple lineages, lies within the RBD, and increases its affinity for ACE2 (Starr et al., 2020; Supasa et al., 2021). These findings suggest that mutations that directly or indirectly enhance Spike binding to ACE2 may increase transmissibility.

Prior infection by SARS-CoV-2 and current vaccines induce antibody responses to the Spike protein, and most neutralising antibodies appear to bind to the Spike RBD (Garcia-Beltran et al., 2021; Greaney et al., 2021a; Rogers et al., 2020). Some variants of concern have mutations in their RBD that confer resistance to neutralising antibodies (Darby and Hiscox, 2021; Dejnirattisai et al., 2021; Garcia-Beltran et al., 2021; Madhi et al., 2021a; Madhi et al., 2021b; Mahase, 2021). What is less clear is the precise effect of these mutations on the affinity and kinetics of the binding of RBD to ACE2. Previous studies of the interaction between the Spike RBD and ACE2 have produced a wide range of affinity and kinetic estimates under conditions (e.g. temperature) that are not always well defined (Lei et al., 2020; Shang et al., 2020; Supasa et al., 2021; Wrapp et al., 2020; Zhang et al., 2021; Zhang et al., 2020). Precise information is needed to assess the extent to which RBD mutations have been selected because they enhance ACE2 binding or facilitate immune evasion.

In this study, we undertook a detailed affinity and kinetic analysis of the interaction between Spike RBD and ACE2 at a physiological temperature (37°C), taking care to avoid common pitfalls. We used this optimised approach to analyse the effect of important common mutations identified in variants of RBD and ACE2. Both mutations of ACE2 (S19P, K26R) and most of the mutations of RBD (N501Y, E484K, and S477N) enhanced the interaction, with one RBD mutation (N501Y) increasing the affinity by ~10-fold. Increased binding was the result of decreases in dissociation rate constants (N501Y, S477N) and/or increases in association rate constants (N501Y, E484K). Although the K417N/T mutations found in the South African (B.1.351) and Brazilian (P.1) variants both decreased the affinity, the affinity-enhancing N501Y and E484K mutations that are also present in both variants confer a net ~4-fold increase in the affinity of their RBDs for ACE2.

While our finding that the SARS-CoV-2 RBD binds ACE2 with an affinity of K_D 74 nM at 37°C is consistent with previous studies (K_D 6–133 nM) (Laffeber et al., 2021; Lei et al., 2020; Liu et al., 2021; Shang et al., 2020; Supasa et al., 2021; Wrapp et al., 2020; Zhang et al., 2021; Zhang et al., 2020), the rate constants that we measured (k_on 0.9 μM^–1.s^–1 and k_off 0.067 s^–1) were faster than all previous reports. One likely reason for this is that previous measurements were performed at a lower temperature, which almost always decreases rate constants. While some studies stated that binding constants were measured at 25°C (Laffeber et al., 2021; Zhang et al., 2020), most studies did not report the temperature, suggesting that they were performed at room temperature or the standard instrument temperature (20°C–25°C). A second likely reason is that previous kinetic studies were performed under conditions in which the rate of diffusion of soluble molecule to the sensor surface limits the association rate, and rebinding of dissociated molecules to the surface reduces the measured dissociation rate. These are known pitfalls of both techniques used in these studies, surface plasmon resonance (Myszka, 1997), and bilayer interferometry (Abdiche et al., 2008). In the present study, we avoided these issues by immobilising a very low level of ligand on the sensor surface. A third possible reason is that the proteins were aggregated, which can cause problems even when aggregates are a very minor contaminant (van der Merwe and Barclay, 1996). The presence of aggregates results in complex binding kinetics, which can be excluded if the simple 1:1 Langmuir binding model fits the kinetic data. While this was demonstrated in the present study, and some previous studies (Shang et al., 2020; Wrapp et al., 2020; Zhang et al., 2021), such fits were not shown in all studies, one of which reported more than 20-fold slower kinetics than reported here (Lei et al., 2020; Supasa et al., 2021).

The RBD mutations that we selected for analysis have all emerged independently and became dominant in a region at least once in different lineages, suggesting that they provide a selective advantage. Our finding that the N501Y, E484K, and S477N all increase the binding affinity of RBD for ACE2 raises the question as to whether this contributed to their selection. Several lines of evidence suggest that enhancing the Spike/ACE2 interaction would be advantageous for the virus. First, the virus has spread only very recently to humans from another mammalian host, providing insufficient time for optimisation of the affinity. Second, epidemiological studies have suggested that the Alpha variant, which has the N501Y mutation, has enhanced transmissibility (Volz et al., 2021a; Washington et al., 2021). Finally, a SARS-CoV-2 variant with the Spike mutation D614G, which increases its activity by stabilising it following furin cleavage (Zhang et al., 2021; Zhang et al., 2020), rapidly became dominant globally after it emerged (Korber et al., 2020; Volz et al., 2021b). Taken together, these findings suggest that the WT Spike/ACE2 interaction is limiting for transmission and that mutations that enhance it, including the N501Y, E484K, and S477N mutations, could provide a selective advantage by increasing transmissibility. This raises two questions. First, will other RBD mutations appear in SARS-CoV-2 which further enhance transmission? This seems likely, given that a large number of RBD mutations have been identified that increase the RBD/ACE2 affinity (Starr et al., 2020; Zahradník et al., 2021). Second, will combinations of existing mutations be selected because they further increase the affinity? While the appearance E484K, together with N501Y in three lineages (Alpha, Beta, and Gamma), supports this, it is also possible that E484K was selected because it disrupts antibody neutraliaation, as discussed below.

Our affinity and kinetic data on RBD variants are broadly consistent with some (Laffeber et al., 2021; Liu et al., 2021), but not all (Dejnirattisai et al., 2021; Zhou et al., 2021a), recent reports on the K417T/N, N501Y, and E484K variants. One caveat to our study is that we used monomeric forms of RBD and ACE2. The native Spike protein is a trimer and has several other domains, including the nearby N-teminal domain, and the native ACE2 protein can exist as a dimer (Yan et al., 2020). Because of these differences, our analysis may not detect the full effects of RBD and ACE2 mutations on the Spike/ACE2 interaction. A second caveat is that we have not examined the effect of these mutations on viral attachment to cells.

Studies of other enveloped viruses, including SARS-CoV, suggest that increases in affinity of viral ligands for their cellular receptors can increase cell infection and disease severity (Hasegawa et al., 2007; Li et al., 2005). One study found that increasing this affinity enabled the virus to infect cells with lower receptor surface density (Hasegawa et al., 2007). It follows that increases in affinity could increase the number of host tissues infected, which could increase the severity of disease (Cao and Li, 2020) and/or increase the viral load in the upper respiratory tract (Hoffmann et al., 2020; Wölfel et al., 2020), thereby increasing spread.

Another mechanism by which mutations of RBD could provide a selective advantage is through evasion of immune responses. This is supported by the observation that neutralising antibodies present in those infected by or vaccinated against SARS-CoV-2 primarily target the RBD (Garcia-Beltran et al., 2021; Greaney et al., 2021a; Rogers et al., 2020). Furthermore, two variants with RBD mutations that abrogate antibody neutralisation, Beta and Gamma, became dominant in regions with very high levels of prior SARS-CoV-2 infection (Cele et al., 2021; Dejnirattisai et al., 2021; Hoffmann et al., 2021; Sabino et al., 2021; Tegally et al., 2021; Zhou et al., 2021b). Both lineages include the N501Y mutation, but this appears to have modest effects on antibody neutralisation (Greaney et al., 2021a; Greaney et al., 2021b). In contrast, the E484K mutation, also present in both variants, potently disrupts antibody neutralisation (Greaney et al., 2021a; Greaney et al., 2021b). Our finding that the K417N/T mutations present in Beta and Gamma variants decrease the affinity of RBD for ACE2 suggests that they were selected because they facilitate immune escape. Indeed, mutations of K417 can block antibody neutralisation, albeit less effectively than E484K (Greaney et al., 2021a; Greaney et al., 2021b; Wang et al., 2021). It is notable that these affinity-reducing K417N/T mutations have only emerged together with mutations (N501Y and E484K) that increase the affinity of RBD for ACE2, suggesting a cooperative effect between mutations that enhance immune escape and mutations that increase affinity.

The effect of the increased affinity for SARS-CoV-2 Spike RBD of the K26R and S19P ACE2 mutants is less clear. The evidence summarised above that WT RBD/ACE2 binding is limiting for SARS-CoV-2 transmission, suggest that carriers of these ACE2 variants will be at greater risk of infection and/or severe disease. However, in contrast to SARS-CoV-2 RBD mutations, the effects of ACE2 variants are primarily relevant to the carriers of these mutations. A preliminary analysis (MacGowan et al., 2021) suggests that the carriers of the K26R ACE allele might be at increased risk of severe disease, but the findings did not reach statistical significance, and further studies are required.

The interaction that we identified between the RBD S477N and ACE2 S19P mutants highlights the importance of considering variation in the host population when studying the evolution of viral variants. In this case, the opposite effect of the RBD S477N mutation on its affinity for ACE2 S19P (decreased), compared with ACE2 WT (increased), suggests that this RBD variant may have a selective disadvantage amongst carriers of the ACE2 S19P variant, in contrast to those with ACE2 WT, where it appears to be advantageous. However, the low frequency of this variant means that this is unlikely to be important at a population level and will be difficult to detect.

It is noteworthy that the two most common ACE2 variants are in positions on ACE2 with no known functional activity. This raises the question as to whether these mutations are a remnant of historic adaption to pathogens that utilised this portion of ACE2. The fact that ACE2 S19P mutation is largely confined to African/African-American populations, suggests that it is more recent than K26R and/or selected by pathogen(s) confined to the African continent.

All data generated and analysed during this study are included in the manuscript and supporting files.

1. 1. Abdiche Y
   2. Malashock D
   3. Pinkerton A
   4. Pons J

   (2008) Determining kinetics and affinities of protein interactions using a parallel real-time label-free biosensor, the octet

   Analytical Biochemistry 377:209–217.

   https://doi.org/10.1016/j.ab.2008.03.035

   • PubMed
   • Google Scholar
2. 1. Agius R
   2. Torchala M
   3. Moal IH
   4. Fernández-Recio J
   5. Bates PA

   (2013) Characterizing changes in the rate of protein-protein dissociation upon interface mutation using hotspot energy and organization

   PLOS Computational Biology 9:e1003216.

   https://doi.org/10.1371/journal.pcbi.1003216

   • PubMed
   • Google Scholar
3. 1. Amanat F
   2. Stadlbauer D
   3. Strohmeier S
   4. Nguyen THO
   5. Chromikova V
   6. McMahon M
   7. Jiang K
   8. Arunkumar GA
   9. Jurczyszak D
   10. Polanco J
   11. Bermudez-Gonzalez M
   12. Kleiner G
   13. Aydillo T
   14. Miorin L
   15. Fierer DS
   16. Lugo LA
   17. Kojic EM
   18. Stoever J
   19. Liu STH
   20. Cunningham-Rundles C
   21. Felgner PL
   22. Moran T
   23. García-Sastre A
   24. Caplivski D
   25. Cheng AC
   26. Kedzierska K
   27. Vapalahti O
   28. Hepojoki JM
   29. Simon V
   30. Krammer F

   (2020) A serological assay to detect sars-cov-2 seroconversion in humans

   Nature Medicine 26:1033–1036.

   https://doi.org/10.1038/s41591-020-0913-5

   • PubMed
   • Google Scholar
4. 1. Cao W
   2. Li T

   (2020) Covid-19: Towards understanding of pathogenesis

   Cell Research 30:367–369.

   https://doi.org/10.1038/s41422-020-0327-4

   • PubMed
   • Google Scholar
5. 1. Cele S
   2. Gazy I
   3. Jackson L
   4. Hwa S-H
   5. Tegally H
   6. Lustig G
   7. Giandhari J
   8. Pillay S
   9. Wilkinson E
   10. Naidoo Y
   11. Karim F
   12. Ganga Y
   13. Khan K
   14. Bernstein M
   15. Balazs AB
   16. Gosnell BI
   17. Hanekom W
   18. Moosa M-YS
   19. Network for Genomic Surveillance in South Africa
   20. COMMIT-KZN Team
   21. Lessells RJ
   22. de Oliveira T
   23. Sigal A

   (2021) Escape of SARS-COV-2 501y.V2 from neutralization by convalescent plasma

   Nature 593:142–146.

   https://doi.org/10.1038/s41586-021-03471-w

   • PubMed
   • Google Scholar
6. 1. Darby AC
   2. Hiscox JA

   (2021) Covid-19: Variants and vaccination

   BMJ 372:n771.

   https://doi.org/10.1136/bmj.n771

   • PubMed
   • Google Scholar
7. 1. Davies NG
   2. Abbott S
   3. Barnard RC
   4. Jarvis CI
   5. Kucharski AJ
   6. Munday JD
   7. Pearson CAB
   8. Russell TW
   9. Tully DC
   10. Washburne AD
   11. Wenseleers T
   12. Gimma A
   13. Waites W
   14. Wong KLM
   15. van Zandvoort K
   16. Silverman JD
   17. CMMID COVID-19 Working Group
   18. COVID-19 Genomics UK (COG-UK) Consortium
   19. Diaz-Ordaz K
   20. Keogh R
   21. Eggo RM
   22. Funk S
   23. Jit M
   24. Atkins KE
   25. Edmunds WJ

   (2021) Estimated transmissibility and impact of SARS-COV-2 lineage b.1.1.7 in England

   Science 372:abg3055.

   https://doi.org/10.1126/science.abg3055

   • PubMed
   • Google Scholar
8. 1. Dejnirattisai W
   2. Zhou D
   3. Supasa P
   4. Liu C
   5. Mentzer AJ
   6. Ginn HM
   7. Zhao Y
   8. Duyvesteyn HME
   9. Tuekprakhon A
   10. Nutalai R
   11. Wang B
   12. López-Camacho C
   13. Slon-Campos J
   14. Walter TS
   15. Skelly D
   16. Costa Clemens SA
   17. Naveca FG
   18. Nascimento V
   19. Nascimento F
   20. Fernandes da Costa C
   21. Resende PC
   22. Pauvolid-Correa A
   23. Siqueira MM
   24. Dold C
   25. Levin R
   26. Dong T
   27. Pollard AJ
   28. Knight JC
   29. Crook D
   30. Lambe T
   31. Clutterbuck E
   32. Bibi S
   33. Flaxman A
   34. Bittaye M
   35. Belij-Rammerstorfer S
   36. Gilbert SC
   37. Carroll MW
   38. Klenerman P
   39. Barnes E
   40. Dunachie SJ
   41. Paterson NG
   42. Williams MA
   43. Hall DR
   44. Hulswit RJG
   45. Bowden TA
   46. Fry EE
   47. Mongkolsapaya J
   48. Ren J
   49. Stuart DI
   50. Screaton GR

   (2021) Antibody evasion by the p.1 strain of SARS-COV-2

   Cell 184:2939–2954.

   https://doi.org/10.1016/j.cell.2021.03.055

   • PubMed
   • Google Scholar
9. 1. Garcia-Beltran WF
   2. Lam EC
   3. KSt D
   4. Nitido AD
   5. Garcia ZH
   6. Hauser BM
   7. Feldman J
   8. Pavlovic MN
   9. Gregory DJ
   10. Poznansky MC
   11. Sigal A
   12. Schmidt AG
   13. Iafrate AJ
   14. Naranbhai V
   15. Balazs AB

   (2021) Multiple SARS-CoV-2 variants escape neutralization by vaccine-induced humoral immunity

   Cell 184:2372–2383.

   https://doi.org/10.1016/j.cell.2021.03.013

   • PubMed
   • Google Scholar
10. 1. Greaney AJ
    2. Loes AN
    3. Crawford KHD
    4. Starr TN
    5. Malone KD
    6. Chu HY
    7. Bloom JD

    (2021a) Comprehensive mapping of mutations in the SARS-CoV-2 receptor-binding domain that affect recognition by polyclonal human plasma antibodies

    Cell Host Microbe 29:463–476.

    https://doi.org/10.1016/j.chom.2021.02.003

    • PubMed
    • Google Scholar
11. 1. Greaney AJ
    2. Starr TN
    3. Gilchuk P
    4. Zost SJ
    5. Binshtein E
    6. Loes AN
    7. Hilton SK
    8. Huddleston J
    9. Eguia R
    10. Crawford KHD
    11. Dingens AS
    12. Nargi RS
    13. Sutton RE
    14. Suryadevara N
    15. Rothlauf PW
    16. Liu Z
    17. Whelan SPJ
    18. Carnahan RH
    19. Crowe JE
    20. Bloom JD

    (2021b) Complete Mapping of Mutations to the SARS-CoV-2 Spike Receptor-Binding Domain that Escape Antibody Recognition

    Cell Host Microbe 29:44–57.

    https://doi.org/10.1016/j.chom.2020.11.007

    • PubMed
    • Google Scholar
12. 1. Hadfield J
    2. Megill C
    3. Bell SM
    4. Huddleston J
    5. Potter B
    6. Callender C
    7. Sagulenko P
    8. Bedford T
    9. Neher RA

    (2018) Nextstrain: real-time tracking of pathogen evolution

    Bioinformatics 34:4121–4123.

    https://doi.org/10.1093/bioinformatics/bty407

    • PubMed
    • Google Scholar
13. 1. Hasegawa K
    2. Hu C
    3. Nakamura T
    4. Marks JD
    5. Russell SJ
    6. Peng KW

    (2007) Affinity thresholds for membrane fusion triggering by viral glycoproteins

    Journal of Virology 81:13149–13157.

    https://doi.org/10.1128/jvi.01415-07

    • PubMed
    • Google Scholar
14. Website

    1. Hodcroft EB

    (2021) Covariants: SARS-COV-2 mutations and variants of interest

    Accessed April 15, 2021.

    https://covariants.org/
15. 1. Hoffmann M
    2. Kleine-Weber H
    3. Schroeder S
    4. Krüger N
    5. Herrler T
    6. Erichsen S
    7. Schiergens TS
    8. Herrler G
    9. N-H W
    10. Nitsche A
    11. Müller MA
    12. Drosten C
    13. Pöhlmann S

    (2020) SARS-CoV-2 Cell Entry Depends on ACE2 and TMPRSS2 and Is Blocked by a Clinically Proven Protease Inhibitor

    Cell 181:271–280.

    https://doi.org/10.1016/j.cell.2020.02.052

    • PubMed
    • Google Scholar
16. 1. Hoffmann M
    2. Arora P
    3. Groß R
    4. Seidel A
    5. Hörnich BF
    6. Hahn AS
    7. Krüger N
    8. Graichen L
    9. Hofmann-Winkler H
    10. Kempf A
    11. Winkler MS
    12. Schulz S
    13. Jäck HM
    14. Jahrsdörfer B
    15. Schrezenmeier H
    16. Müller M
    17. Kleger A
    18. Münch J
    19. Pöhlmann S

    (2021) SARS-CoV-2 variants B.1.351 and P.1 escape from neutralizing antibodies

    Cell 184:2384–2393.

    https://doi.org/10.1016/j.cell.2021.03.036

    • PubMed
    • Google Scholar
17. 1. Karczewski KJ
    2. Francioli LC
    3. Tiao G
    4. Cummings BB
    5. Alföldi J
    6. Wang Q
    7. Collins RL
    8. Laricchia KM
    9. Ganna A
    10. Birnbaum DP
    11. Gauthier LD
    12. Brand H
    13. Solomonson M
    14. Watts NA
    15. Rhodes D
    16. Singer-Berk M
    17. England EM
    18. Seaby EG
    19. Kosmicki JA
    20. Walters RK
    21. Tashman K
    22. Farjoun Y
    23. Banks E
    24. Poterba T
    25. Wang A
    26. Seed C
    27. Whiffin N
    28. Chong JX
    29. Samocha KE
    30. Pierce-Hoffman E
    31. Zappala Z
    32. O’Donnell-Luria AH
    33. Minikel EV
    34. Weisburd B
    35. Lek M
    36. Ware JS
    37. Vittal C
    38. Armean IM
    39. Bergelson L
    40. Cibulskis K
    41. Connolly KM
    42. Covarrubias M
    43. Donnelly S
    44. Ferriera S
    45. Gabriel S
    46. Gentry J
    47. Gupta N
    48. Jeandet T
    49. Kaplan D
    50. Llanwarne C
    51. Munshi R
    52. Novod S
    53. Petrillo N
    54. Roazen D
    55. Ruano-Rubio V
    56. Saltzman A
    57. Schleicher M
    58. Soto J
    59. Tibbetts K
    60. Tolonen C
    61. Wade G
    62. Talkowski ME
    63. Salinas CAA
    64. Ahmad T
    65. Albert CM
    66. Ardissino D
    67. Atzmon G
    68. Barnard J
    69. Beaugerie L
    70. Benjamin EJ
    71. Boehnke M
    72. Bonnycastle LL
    73. Bottinger EP
    74. Bowden DW
    75. Bown MJ
    76. Chambers JC
    77. Chan JC
    78. Chasman D
    79. Cho J
    80. Chung MK
    81. Cohen B
    82. Correa A
    83. Dabelea D
    84. Daly MJ
    85. Darbar D
    86. Duggirala R
    87. Dupuis J
    88. Ellinor PT
    89. Elosua R
    90. Erdmann J
    91. Esko T
    92. Färkkilä M
    93. Florez J
    94. Franke A
    95. Getz G
    96. Glaser B
    97. Glatt SJ
    98. Goldstein D
    99. Gonzalez C
    100. Groop L
    101. Haiman C
    102. Hanis C
    103. Harms M
    104. Hiltunen M
    105. Holi MM
    106. Hultman CM
    107. Kallela M
    108. Kaprio J
    109. Kathiresan S
    110. Kim BJ
    111. Kim YJ
    112. Kirov G
    113. Kooner J
    114. Koskinen S
    115. Krumholz HM
    116. Kugathasan S
    117. Kwak SH
    118. Laakso M
    119. Lehtimäki T
    120. Loos RJF
    121. Lubitz SA
    122. MacArthur DG
    123. Marrugat J
    124. Mattila KM
    125. McCarroll S
    126. McCarthy MI
    127. McGovern D
    128. McPherson R
    129. Meigs JB
    130. Melander O
    131. Metspalu A
    132. Neale BM
    133. Nilsson PM
    134. O’Donovan MC
    135. Ongur D
    136. Orozco L
    137. Owen MJ
    138. Palmer CNA
    139. Palotie A
    140. Park KS
    141. Pato C
    142. Pulver AE
    143. Rahman N
    144. Remes AM
    145. Rioux JD
    146. Ripatti S
    147. Roden DM
    148. Saleheen D
    149. Salomaa V
    150. Samani NJ
    151. Scharf J
    152. Schunkert H
    153. Shoemaker MB
    154. Sklar P
    155. Soininen H
    156. Sokol H
    157. Spector T
    158. Sullivan PF
    159. Suvisaari J
    160. Tai ES
    161. Teo YY
    162. Tiinamaija T
    163. Tsuang M
    164. Turner D
    165. Tusie-Luna T
    166. Vartiainen E
    167. Vawter MP
    168. Ware JS
    169. Watkins H
    170. Weersma RK
    171. Wessman M
    172. Wilson JG
    173. Xavier RJ
    174. Neale BM
    175. Daly MJ
    176. MacArthur DG

    (2020) The mutational constraint spectrum quantified from variation in 141,456 humans

    Nature 581:434–443.

    https://doi.org/10.1038/s41586-020-2308-7

    • PubMed
    • Google Scholar
18. 1. Korber B
    2. Fischer WM
    3. Gnanakaran S
    4. Yoon H
    5. Theiler J
    6. Abfalterer W
    7. Hengartner N
    8. Giorgi EE
    9. Bhattacharya T
    10. Foley B
    11. Hastie KM
    12. Parker MD
    13. Partridge DG
    14. Evans CM
    15. Freeman TM
    16. de Silva TI
    17. Sheffield COVID-19 Genomics Group
    18. McDanal C
    19. Perez LG
    20. Tang H
    21. Moon-Walker A
    22. Whelan SP
    23. LaBranche CC
    24. Saphire EO
    25. Montefiori DC

    (2020) Tracking changes in sars-cov-2 spike: Evidence that d614g increases infectivity of the COVID-19 virus

    Cell 182:812–827.

    https://doi.org/10.1016/j.cell.2020.06.043

    • PubMed
    • Google Scholar
19. 1. Laffeber C
    2. de Koning K
    3. Kanaar R
    4. Lebbink JHG

    (2021) Experimental evidence for enhanced receptor binding by rapidly spreading SARS-COV-2 variants

    Journal of Molecular Biology 433:167058.

    https://doi.org/10.1016/j.jmb.2021.167058

    • PubMed
    • Google Scholar
20. 1. Lan J
    2. Ge J
    3. Yu J
    4. Shan S
    5. Zhou H
    6. Fan S
    7. Zhang Q
    8. Shi X
    9. Wang Q
    10. Zhang L
    11. Wang X

    (2020) Structure of the SARS-COV-2 spike receptor-binding domain bound to the ace2 receptor

    Nature 581:215–220.

    https://doi.org/10.1038/s41586-020-2180-5

    • PubMed
    • Google Scholar
21. 1. Lei C
    2. Qian K
    3. Li T
    4. Zhang S
    5. Fu W
    6. Ding M
    7. Hu S

    (2020) Neutralization of SARS-COV-2 spike pseudotyped virus by recombinant ACE2-IG

    Nature Communications 11:2070.

    https://doi.org/10.1038/s41467-020-16048-4

    • PubMed
    • Google Scholar
22. 1. Li W
    2. Zhang C
    3. Sui J
    4. Kuhn JH
    5. Moore MJ
    6. Luo S
    7. Wong S-K
    8. Huang I-C
    9. Xu K
    10. Vasilieva N
    11. Murakami A
    12. He Y
    13. Marasco WA
    14. Guan Y
    15. Choe H
    16. Farzan M

    (2005) Receptor and viral determinants of SARS-coronavirus adaptation to human ACE2

    The EMBO Journal 24:1634–1643.

    https://doi.org/10.1038/sj.emboj.7600640

    • PubMed
    • Google Scholar
23. 1. Liu H
    2. Zhang Q
    3. Wei P
    4. Chen Z
    5. Aviszus K
    6. Yang J
    7. Downing W
    8. Jiang C
    9. Liang B
    10. Reynoso L
    11. Downey GP
    12. Frankel SK
    13. Kappler J
    14. Marrack P
    15. Zhang G

    (2021) The basis of a more contagious 501y.V1 variant of sars-cov-2

    Cell Research 31:720–722.

    https://doi.org/10.1038/s41422-021-00496-8

    • PubMed
    • Google Scholar
24. Preprint

    1. MacGowan SA
    2. Barton MI
    3. Kutuzov M
    4. Dushek O
    5. Van der M
    6. Barton GJ

    (2021) Missense Variants in Human Ace2 Modify Binding to SARS-COV-2 Spike

    bioRxiv.

    https://doi.org/10.1101/2021.05.21.445118

    • Google Scholar
25. 1. Madhi SA
    2. Izu A
    3. Pollard AJ

    (2021a) ChAdOx1 nCoV-19 Vaccine Efficacy against the B.1.351 Variant

    The New England Journal of Medicine 385:571–572.

    https://doi.org/10.1056/NEJMc2110093

    • PubMed
    • Google Scholar
26. Preprint

    1. Madhi SA
    2. Baillie V
    3. Cutland CL
    4. Voysey M
    5. Koen AL
    6. Fairlie L
    7. Padayachee SD
    8. Dheda K
    9. Barnabas SL
    10. Bhorat QE
    11. Briner C
    12. Kwatra G
    13. Ahmed K
    14. Aley P
    15. Bhikha S
    16. Bhiman JN
    17. Bhorat AE
    18. du PJ
    19. Esmail A
    20. Groenewald M
    21. Horne E
    22. Hwa SH
    23. Jose A
    24. Lambe T
    25. Laubscher M
    26. Malahleha M
    27. Masenya M
    28. Masilela M
    29. McKenzie S
    30. Molapo K
    31. Moultrie A
    32. Oelofse S
    33. Patel F
    34. Pillay S
    35. Rhead S
    36. Rodel H
    37. Rossouw L
    38. Taoushanis C
    39. Tegally H
    40. Thombrayil A
    41. van ES
    42. Wibmer CK
    43. Durham NM
    44. Kelly EJ
    45. Villafana TL
    46. Gilbert S
    47. Pollard AJ
    48. de OT
    49. Moore PL
    50. Sigal A
    51. Izu A

    (2021b) Safety and Efficacy of the Chadox1 NCOV-19 (AZD1222) Covid-19 Vaccine against the b.1.351 Variant in South Africa

    medRxiv.

    https://doi.org/10.1101/2021.02.10.21251247

    • Google Scholar
27. 1. Mahase E

    (2021) Covid-19: Where are we on vaccines and variants

    BMJ 372:n597.

    https://doi.org/10.1136/bmj.n597

    • Google Scholar
28. 1. Myszka DG

    (1997) Kinetic analysis of macromolecular interactions using surface plasmon resonance biosensors

    Current Opinion in Biotechnology 8:50–57.

    https://doi.org/10.1016/s0958-1669(97)80157-7

    • PubMed
    • Google Scholar
29. 1. Myszka DG

    (1999) Improving biosensor analysis

    Journal of Molecular Recognition 12:279–284.

    https://doi.org/10.1002/(SICI)1099-1352(199909/10)12:5<279::AID-JMR473>3.0.CO;2-3

    • PubMed
    • Google Scholar
30. 1. Pettersen EF
    2. Goddard TD
    3. Huang CC
    4. Couch GS
    5. Greenblatt DM
    6. Meng EC
    7. Ferrin TE

    (2004) UCSF Chimera--a visualization system for exploratory research and analysis

    Journal of Computational Chemistry 25:1605–1612.

    https://doi.org/10.1002/jcc.20084

    • PubMed
    • Google Scholar
31. 1. Rambaut A
    2. Holmes EC
    3. O’Toole Á
    4. Hill V
    5. McCrone JT
    6. Ruis C
    7. du Plessis L
    8. Pybus OG

    (2020) A dynamic nomenclature proposal for sars-cov-2 lineages to assist genomic epidemiology

    Nature Microbiology 5:1403–1407.

    https://doi.org/10.1038/s41564-020-0770-5

    • PubMed
    • Google Scholar
32. Preprint

    1. Richard D
    2. Shaw LP
    3. Lanfear R
    4. Acman M
    5. Owen CJ
    6. Tan CC
    7. van DL
    8. Balloux F

    (2021) A Phylogeny-Based Metric for Estimating Changes in Transmissibility from Recurrent Mutations in SARS-COV-2

    bioRxiv.

    https://doi.org/10.1101/2021.05.06.442903

    • Google Scholar
33. 1. Rogers TF
    2. Zhao F
    3. Huang D
    4. Beutler N
    5. Burns A
    6. He W-T
    7. Limbo O
    8. Smith C
    9. Song G
    10. Woehl J
    11. Yang L
    12. Abbott RK
    13. Callaghan S
    14. Garcia E
    15. Hurtado J
    16. Parren M
    17. Peng L
    18. Ramirez S
    19. Ricketts J
    20. Ricciardi MJ
    21. Rawlings SA
    22. Wu NC
    23. Yuan M
    24. Smith DM
    25. Nemazee D
    26. Teijaro JR
    27. Voss JE
    28. Wilson IA
    29. Andrabi R
    30. Briney B
    31. Landais E
    32. Sok D
    33. Jardine JG
    34. Burton DR

    (2020) Isolation of potent SARS-COV-2 neutralizing antibodies and protection from disease in a small animal model

    Science 369:956–963.

    https://doi.org/10.1126/science.abc7520

    • PubMed
    • Google Scholar
34. 1. Sabino EC
    2. Buss LF
    3. Carvalho MPS
    4. Prete CA Jr
    5. Crispim MAE
    6. Fraiji NA
    7. Pereira RHM
    8. Parag KV
    9. da Silva Peixoto P
    10. Kraemer MUG
    11. Oikawa MK
    12. Salomon T
    13. Cucunuba ZM
    14. Castro MC
    15. de Souza Santos AA
    16. Nascimento VH
    17. Pereira HS
    18. Ferguson NM
    19. Pybus OG
    20. Kucharski A
    21. Busch MP
    22. Dye C
    23. Faria NR

    (2021) Resurgence of COVID-19 in Manaus, Brazil, despite high seroprevalence

    Lancet 397:452–455.

    https://doi.org/10.1016/s0140-6736(21)00183-5

    • PubMed
    • Google Scholar
35. 1. Sagulenko P
    2. Puller V
    3. Neher RA

    (2018) Treetime: Maximum-likelihood phylodynamic analysis

    Virus Evolution 4:vex042.

    https://doi.org/10.1093/ve/vex042

    • PubMed
    • Google Scholar
36. Website

    1. SARS-CoV-2 Variants of concern and variants under investigation - GOV.UK

    (2021) Variants of concern or under investigation: Data up to 19 May 2021

    Accessed May 22, 2021.

    https://www.gov.uk/government/publications/covid-19-variants-genomically-confirmed-case-numbers/variants-distribution-of-cases-data
37. 1. Schreiber G
    2. Fersht AR

    (1996) Rapid, electrostatically assisted Association of proteins

    Nature Structural Biology 3:427–431.

    https://doi.org/10.1038/nsb0596-427

    • PubMed
    • Google Scholar
38. 1. Shang J
    2. Ye G
    3. Shi K
    4. Wan Y
    5. Luo C
    6. Aihara H
    7. Geng Q
    8. Auerbach A
    9. Li F

    (2020) Structural basis of receptor recognition by SARS-COV-2

    Nature 581:221–224.

    https://doi.org/10.1038/s41586-020-2179-y

    • PubMed
    • Google Scholar
39. 1. Shu Y
    2. McCauley J

    (2017) GISAID: Global initiative on sharing all influenza data - from vision to reality

    Euro Surveillance 22:30494.

    https://doi.org/10.2807/1560-7917.es.2017.22.13.30494

    • PubMed
    • Google Scholar
40. 1. Starr TN
    2. Greaney AJ
    3. Hilton SK
    4. Ellis D
    5. Crawford KHD
    6. Dingens AS
    7. Navarro MJ
    8. Bowen JE
    9. Tortorici MA
    10. Walls AC
    11. King NP
    12. Veesler D
    13. Bloom JD

    (2020) Deep mutational scanning of SARS-COV-2 receptor binding domain reveals constraints on folding and ace2 binding

    Cell 182:1295–1310.

    https://doi.org/10.1016/j.cell.2020.08.012

    • PubMed
    • Google Scholar
41. 1. Supasa P
    2. Zhou D
    3. Dejnirattisai W
    4. Liu C
    5. Mentzer AJ
    6. Ginn HM
    7. Zhao Y
    8. Duyvesteyn HME
    9. Nutalai R
    10. Tuekprakhon A
    11. Wang B
    12. Paesen GC
    13. Slon-Campos J
    14. López-Camacho C
    15. Hallis B
    16. Coombes N
    17. Bewley KR
    18. Charlton S
    19. Walter TS
    20. Barnes E
    21. Dunachie SJ
    22. Skelly D
    23. Lumley SF
    24. Baker N
    25. Shaik I
    26. Humphries HE
    27. Godwin K
    28. Gent N
    29. Sienkiewicz A
    30. Dold C
    31. Levin R
    32. Dong T
    33. Pollard AJ
    34. Knight JC
    35. Klenerman P
    36. Crook D
    37. Lambe T
    38. Clutterbuck E
    39. Bibi S
    40. Flaxman A
    41. Bittaye M
    42. Belij-Rammerstorfer S
    43. Gilbert S
    44. Hall DR
    45. Williams MA
    46. Paterson NG
    47. James W
    48. Carroll MW
    49. Fry EE
    50. Mongkolsapaya J
    51. Ren J
    52. Stuart DI
    53. Screaton GR

    (2021) Reduced neutralization of SARS-COV-2 b.1.1.7 variant by convalescent and vaccine Sera

    Cell 184:2201–2211.

    https://doi.org/10.1016/j.cell.2021.02.033

    • PubMed
    • Google Scholar
42. 1. Tegally H
    2. Wilkinson E
    3. Giovanetti M
    4. Iranzadeh A
    5. Fonseca V
    6. Giandhari J
    7. Doolabh D
    8. Pillay S
    9. San EJ
    10. Msomi N
    11. Mlisana K
    12. von GA
    13. Walaza S
    14. Allam M
    15. Ismail A
    16. Mohale T
    17. Glass AJ
    18. Engelbrecht S
    19. Zyl GV
    20. Preiser W
    21. Petruccione F
    22. Sigal A
    23. Hardie D
    24. Marais G
    25. Hsiao M
    26. Korsman S
    27. Davies MA
    28. Tyers L
    29. Mudau I
    30. York D
    31. Maslo C
    32. Goedhals D
    33. Abrahams S
    34. Laguda-Akingba O
    35. Alisoltani-Dehkordi A
    36. Godzik A
    37. Wibmer CK
    38. Sewell BT
    39. Lourenço J
    40. Alcantara LCJ
    41. Pond SLK
    42. Weaver S
    43. Martin D
    44. Lessells RJ
    45. Bhiman JN
    46. Williamson C
    47. de OT

    (2021) Emergence of a SARS-COV-2 variant of concern with mutations in spike glycoprotein

    Nature 592:1–8.

    https://doi.org/10.1038/s41586-021-03402-9

    • Google Scholar
43. 1. van der Merwe PA
    2. Barclay AN

    (1996) Analysis of cell-adhesion molecule interactions using surface plasmon resonance

    Current Opinion in Immunology 8:257–261.

    https://doi.org/10.1016/s0952-7915(96)80065-3

    • PubMed
    • Google Scholar
44. Preprint

    1. Volz E
    2. Mishra S
    3. Chand M
    4. Barrett JC
    5. Johnson R

    (2021a) Transmission of SARS-COV-2 Lineage b.1.1.7 in England: Insights from Linking Epidemiological and Genetic Data

    medRxiv.

    https://doi.org/10.1101/2020.12.30.20249034

    • Google Scholar
45. 1. Volz E
    2. Hill V
    3. McCrone JT
    4. Price A
    5. Jorgensen D
    6. O’Toole Á
    7. Southgate J
    8. Johnson R
    9. Jackson B
    10. Nascimento FF
    11. Rey SM
    12. Nicholls SM
    13. Colquhoun RM
    14. da Silva Filipe A
    15. Shepherd J
    16. Pascall DJ
    17. Shah R
    18. Jesudason N
    19. Li K
    20. Jarrett R
    21. Pacchiarini N
    22. Bull M
    23. Geidelberg L
    24. Siveroni I
    25. COG-UK Consortium
    26. Goodfellow I
    27. Loman NJ
    28. Pybus OG
    29. Robertson DL
    30. Thomson EC
    31. Rambaut A
    32. Connor TR

    (2021b) Evaluating the effects of SARS-COV-2 spike mutation D614G on transmissibility and pathogenicity

    Cell 184:64–75.

    https://doi.org/10.1016/j.cell.2020.11.020

    • PubMed
    • Google Scholar
46. 1. V’kovski P
    2. Kratzel A
    3. Steiner S
    4. Stalder H
    5. Thiel V

    (2021) Coronavirus biology and replication: Implications for SARS-COV-2

    Nature Reviews. Microbiology 19:155–170.

    https://doi.org/10.1038/s41579-020-00468-6

    • PubMed
    • Google Scholar
47. 1. Wang Z
    2. Schmidt F
    3. Weisblum Y
    4. Muecksch F
    5. Barnes CO
    6. Finkin S
    7. Schaefer-Babajew D
    8. Cipolla M
    9. Gaebler C
    10. Lieberman JA
    11. Oliveira TY
    12. Yang Z
    13. Abernathy ME
    14. Huey-Tubman KE
    15. Hurley A
    16. Turroja M
    17. West KA
    18. Gordon K
    19. Millard KG
    20. Ramos V
    21. Da Silva J
    22. Xu J
    23. Colbert RA
    24. Patel R
    25. Dizon J
    26. Unson-O’Brien C
    27. Shimeliovich I
    28. Gazumyan A
    29. Caskey M
    30. Bjorkman PJ
    31. Casellas R
    32. Hatziioannou T
    33. Bieniasz PD
    34. Nussenzweig MC

    (2021) MRNA vaccine-elicited antibodies to SARS-COV-2 and circulating variants

    Nature 592:616–622.

    https://doi.org/10.1038/s41586-021-03324-6

    • PubMed
    • Google Scholar
48. 1. Washington NL
    2. Gangavarapu K
    3. Zeller M
    4. Bolze A
    5. Cirulli ET
    6. Schiabor Barrett KM
    7. Larsen BB
    8. Anderson C
    9. White S
    10. Cassens T
    11. Jacobs S
    12. Levan G
    13. Nguyen J
    14. Ramirez JM
    15. Rivera-Garcia C
    16. Sandoval E
    17. Wang X
    18. Wong D
    19. Spencer E
    20. Robles-Sikisaka R
    21. Kurzban E
    22. Hughes LD
    23. Deng X
    24. Wang C
    25. Servellita V
    26. Valentine H
    27. De Hoff P
    28. Seaver P
    29. Sathe S
    30. Gietzen K
    31. Sickler B
    32. Antico J
    33. Hoon K
    34. Liu J
    35. Harding A
    36. Bakhtar O
    37. Basler T
    38. Austin B
    39. MacCannell D
    40. Isaksson M
    41. Febbo PG
    42. Becker D
    43. Laurent M
    44. McDonald E
    45. Yeo GW
    46. Knight R
    47. Laurent LC
    48. de Feo E
    49. Worobey M
    50. Chiu CY
    51. Suchard MA
    52. Lu JT
    53. Lee W
    54. Andersen KG

    (2021) Emergence and rapid transmission of SARS-COV-2 b.1.1.7 in the United States

    Cell 184:2587–2594.

    https://doi.org/10.1016/j.cell.2021.03.052

    • PubMed
    • Google Scholar
49. 1. Wells JA

    (1990) Additivity of mutational effects in proteins

    Biochemistry 29:8509–8517.

    https://doi.org/10.1021/bi00489a001

    • PubMed
    • Google Scholar
50. Website

    1. WHO

    (2021) Coronavirus (COVID-19) dashboard

    Accessed April 22, 2021.

    https://covid19.who.int/
51. 1. Wölfel R
    2. Corman VM
    3. Guggemos W
    4. Seilmaier M
    5. Zange S
    6. Müller MA
    7. Niemeyer D
    8. Jones TC
    9. Vollmar P
    10. Rothe C
    11. Hoelscher M
    12. Bleicker T
    13. Brünink S
    14. Schneider J
    15. Ehmann R
    16. Zwirglmaier K
    17. Drosten C
    18. Wendtner C

    (2020) Virological assessment of hospitalized patients with covid-2019

    Nature 581:465–469.

    https://doi.org/10.1038/s41586-020-2196-x

    • PubMed
    • Google Scholar
52. 1. Wrapp D
    2. Wang N
    3. Corbett KS
    4. Goldsmith JA
    5. Hsieh CL
    6. Abiona O
    7. Graham BS
    8. McLellan JS

    (2020) Cryo-em structure of the 2019-ncov spike in the prefusion conformation

    Science 367:1260–1263.

    https://doi.org/10.1126/science.abb2507

    • PubMed
    • Google Scholar
53. 1. Yan R
    2. Zhang Y
    3. Li Y
    4. Xia L
    5. Guo Y
    6. Zhou Q

    (2020) Structural basis for the recognition of SARS-COV-2 by full-length human ace2

    Science 367:1444–1448.

    https://doi.org/10.1126/science.abb2762

    • PubMed
    • Google Scholar
54. Preprint

    1. Zahradník J
    2. Marciano S
    3. Shemesh M
    4. Zoler E
    5. Chiaravalli J
    6. Meyer B
    7. Rudich Y
    8. Dym O
    9. Elad N
    10. Schreiber G

    (2021) SARS-COV-2 RBD in Vitro Evolution Follows Contagious Mutation Spread, yet Generates an Able Infection Inhibitor

    bioRxiv.

    https://doi.org/10.1101/2021.01.06.425392

    • Google Scholar
55. 1. Zhang L
    2. Jackson CB
    3. Mou H
    4. Ojha A
    5. Peng H
    6. Quinlan BD
    7. Rangarajan ES
    8. Pan A
    9. Vanderheiden A
    10. Suthar MS
    11. Li W
    12. Izard T
    13. Rader C
    14. Farzan M
    15. Choe H

    (2020) SARS-COV-2 spike-protein D614G mutation increases virion spike density and infectivity

    Nature Communications 11:6013.

    https://doi.org/10.1038/s41467-020-19808-4

    • PubMed
    • Google Scholar
56. 1. Zhang J
    2. Cai Y
    3. Xiao T
    4. Lu J
    5. Peng H
    6. Sterling SM
    7. Walsh RM
    8. Rits-Volloch S
    9. Zhu H
    10. Woosley AN
    11. Yang W
    12. Sliz P
    13. Chen B

    (2021) Structural impact on SARS-COV-2 spike protein by d614g substitution

    Science 372:525–530.

    https://doi.org/10.1126/science.abf2303

    • PubMed
    • Google Scholar
57. Preprint

    1. Zhou H
    2. Dcosta BM
    3. Samanovic MI
    4. Mulligan MJ
    5. Landau NR
    6. Tada T

    (2021a) B.1.526 Sars-Cov-2 Variants Identified in New York City Are Neutralized by Vaccine-Elicited and Therapeutic Monoclonal Antibodies

    bioRxiv.

    https://doi.org/10.1101/2021.03.24.436620

    • Google Scholar
58. 1. Zhou D
    2. Dejnirattisai W
    3. Supasa P
    4. Liu C
    5. Mentzer AJ
    6. Ginn HM
    7. Zhao Y
    8. Duyvesteyn HME
    9. Tuekprakhon A
    10. Nutalai R
    11. Wang B
    12. Paesen GC
    13. Lopez-Camacho C
    14. Slon-Campos J
    15. Hallis B
    16. Coombes N
    17. Bewley K
    18. Charlton S
    19. Walter TS
    20. Skelly D
    21. Lumley SF
    22. Dold C
    23. Levin R
    24. Dong T
    25. Pollard AJ
    26. Knight JC
    27. Crook D
    28. Lambe T
    29. Clutterbuck E
    30. Bibi S
    31. Flaxman A
    32. Bittaye M
    33. Belij-Rammerstorfer S
    34. Gilbert S
    35. James W
    36. Carroll MW
    37. Klenerman P
    38. Barnes E
    39. Dunachie SJ
    40. Fry EE
    41. Mongkolsapaya J
    42. Ren J
    43. Stuart DI
    44. Screaton GR

    (2021b) Evidence of escape of SARS-CoV-2 variant B.1.351 from natural and vaccine-induced sera

    Cell 184:2348–2361.

    https://doi.org/10.1016/j.cell.2021.02.037

    • PubMed
    • Google Scholar

Author details

1. Michael I Barton

   Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

   Contribution

   Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Stuart A MacGowan

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9263-6481

2. Stuart A MacGowan

   School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Conceptualization, Investigation, Visualization, Writing – review and editing

   Contributed equally with

   Michael I Barton

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4233-5071

3. Mikhail A Kutuzov

   Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

   Contribution

   Investigation, Methodology, Supervision

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3386-4350

4. Omer Dushek

   Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Resources, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5847-5226

5. Geoffrey John Barton

   School of Life Sciences, University of Dundee, Dundee, United Kingdom

   Contribution

   Conceptualization, Resources, Supervision, Writing – review and editing

   For correspondence

   g.j.barton@dundee.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9014-5355

6. P Anton van der Merwe

   Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   anton.vandermerwe@path.ox.ac.uk

   Competing interests

   Own shares in BioNTech SE

   "This ORCID iD identifies the author of this article:" 0000-0001-9902-6590

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