SARS-CoV-2 variant prediction and antiviral drug design are enabled by RBD in vitro evolution

Zahradník, Jiří; Marciano, Shir; Shemesh, Maya; Zoler, Eyal; Harari, Daniel; Chiaravalli, Jeanne; Meyer, Björn; Rudich, Yinon; Li, Chunlin; Marton, Ira; Dym, Orly; Elad, Nadav; Lewis, Mark G.; Andersen, Hanné; Gagné, Matthew; Seder, Robert A.; Douek, Daniel C.; Schreiber, Gideon

doi:10.1038/s41564-021-00954-4

Cited by 329 publications

(366 citation statements)

References 50 publications

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“…The N501Y mutation has been reported in mice to emerge as adaptation after serial passaging of a SARS-CoV-2 clinical isolate and increase its virulence (31). Yeast surface in vitro evolution experiments aimed at increasing ACE-2 affinity have shown that the E484K and N501Y were among the first mutations to be selected and fixed (32). Moreover, the authors showed that adding the K417T/N mutation into an E484K/ N501Y background increased the surface expression of the triple mutant, suggesting positive cooperativity between these three mutations.…”

Section: Discussionmentioning

confidence: 99%

Functional Effects of Receptor-Binding Domain Mutations of SARS-CoV-2 B.1.351 and P.1 Variants

et al. 2021

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The recent identification and rise to dominance of the P.1 and B.1.351 SARS-CoV-2 variants have brought international concern because they may confer fitness advantages. The same three positions in the receptor-binding domain (RBD) are affected in both variants, but where the 417 substitution differs, the E484K/N501Y have co-evolved by convergent evolution. Here we characterize the functional and immune evasive consequences of the P.1 and B.1.351 RBD mutations. E484K and N501Y result in gain-of-function with two different outcomes: The N501Y confers a ten-fold affinity increase towards ACE-2, but a modest antibody evasion potential of plasma from convalescent or vaccinated individuals, whereas the E484K displays a significant antibody evasion capacity without a major impact on affinity. On the other hand, the two different 417 substitutions severely impair the RBD/ACE-2 affinity, but in the combined P.1 and B.1.351 RBD variants, this effect is partly counterbalanced by the effect of the E484K and N501Y. Our results suggest that the combination of these three mutations is a two-step forward and one step back in terms of viral fitness.

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Section: Discussionmentioning

confidence: 99%

Functional Effects of Receptor-Binding Domain Mutations of SARS-CoV-2 B.1.351 and P.1 Variants

et al. 2021

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“…The BindingDB is a public database containing measured binding affinities for three types of coronaviruses, SARS-CoV-2, SARS-CoV and MERS-CoV. 1 MT-DTI model was also used to select compounds from 1,400 approved drugs in DrugBank and ZINC databases (Table 1) with strong affinity to the host cell targets crucial for viral infection (Zahradnik et al, 2021). This approach led to identification of drugs candidates with a strong binding affinity (Kd < 100 nM) against ACE2 receptor and transmembrane protease serine 2 (TMPRSS2) (Zahradnik et al, 2021).…”

Section: Deep Learning In Tackling Severe Acute Respiratory Syndrome Coronavirusmentioning

confidence: 99%

“…1 MT-DTI model was also used to select compounds from 1,400 approved drugs in DrugBank and ZINC databases (Table 1) with strong affinity to the host cell targets crucial for viral infection (Zahradnik et al, 2021). This approach led to identification of drugs candidates with a strong binding affinity (Kd < 100 nM) against ACE2 receptor and transmembrane protease serine 2 (TMPRSS2) (Zahradnik et al, 2021). Drug candidates identified in this study include an ACE2 inhibitor Enalaprilat (Kd 1.46 nM) and several drugs with predicted strong affinity for TMPRSS2, namely Venetoclax (Kd 6.12 nM), Posaconazole (Kd 17.11 nM), Daclatasvir (Kd 6.65 nM), and Ombitasvir (Kd 5.91 nM) (Table 2).…”

Section: Deep Learning In Tackling Severe Acute Respiratory Syndrome Coronavirusmentioning

confidence: 99%

“…Strong affinity of Enalaprilat for ACE2 suggests that it might prevent the entry of SARS-CoV-2 to human cells. Notably, two of the drug candidates identified, namely Daclatasvir and Ombitasvir, are known Hepatitis C virus (HCV) inhibitors, thus suggesting that they may act against both HCV and SARS-CoV-2 (Zahradnik et al, 2021). The DrugBank has collected data for 65 drugs against 385 drug targets, which is web accessible at https://go.drugbank.com/covid-19.…”

Section: Deep Learning In Tackling Severe Acute Respiratory Syndrome Coronavirusmentioning

confidence: 99%

“…S protein mediates entry of SARS-CoV-2 into the host cells by binding and fusing with the host's cellular receptor, the angiotensinconverting enzyme 2 (ACE2). Mutations in S protein, particularly in its receptor binding domain (RBD) were shown to play a role in the increased transmissibility and infectivity of the emerging SARS-CoV-2 variants (Zahradnik et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

Zhang

et al. 2021

Front. Microbiol.

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Deep learning significantly accelerates the drug discovery process, and contributes to global efforts to stop the spread of infectious diseases. Besides enhancing the efficiency of screening of antimicrobial compounds against a broad spectrum of pathogens, deep learning has also the potential to efficiently and reliably identify drug candidates against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Consequently, deep learning has been successfully used for the identification of a number of potential drugs against SARS-CoV-2, including Atazanavir, Remdesivir, Kaletra, Enalaprilat, Venetoclax, Posaconazole, Daclatasvir, Ombitasvir, Toremifene, Niclosamide, Dexamethasone, Indomethacin, Pralatrexate, Azithromycin, Palmatine, and Sauchinone. This mini-review discusses recent advances and future perspectives of deep learning-based SARS-CoV-2 drug discovery.

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The Role of Spike Protein Mutations in the Infectious Power of SARS‐COV‐2 Variants: A Molecular Interaction Perspective

et al. 2021

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Specific S477N, N501Y, K417N, K417T, E484K mutations in the receptor binding domain (RBD) of the spike protein in the wild type SARS‐COV‐2 virus have resulted, among others, in the following variants: B.1.160 (20A or EU2, first reported in continental Europe), B1.1.7 (α or 20I501Y.V1, first reported in the United Kingdom), B.1.351 (β or 20H/501Y.V2, first reported in South Africa), B.1.1.28.1 (γ or P.1 or 20J/501Y.V3, first reported in Brazil), and B.1.1.28.2 (ζ, or P.2 or 20B/S484K, also first reported in Brazil). From the analysis of a set of bonding descriptors firmly rooted in the formalism of quantum mechanics, including Natural Bond Orbitals (NBO), Quantum Theory of Atoms In Molecules (QTAIM) and highly correlated energies within the Domain Based Local Pair Natural Orbital Coupled Cluster Method (DLPNO‐CCSD(T)), and from a set of computed electronic spectral patterns with environmental effects, we show that the new variants improve their ability to recognize available sites to either hydrogen bond or to form salt bridges with residues in the ACE2 receptor of the host cells. This results in significantly improved initial virus⋅⋅⋅cell molecular recognition and attachment at the microscopic level, which trigger the infectious cycle.

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SARS-CoV-2 variant prediction and antiviral drug design are enabled by RBD in vitro evolution

Cited by 329 publications

References 50 publications

Functional Effects of Receptor-Binding Domain Mutations of SARS-CoV-2 B.1.351 and P.1 Variants

Functional Effects of Receptor-Binding Domain Mutations of SARS-CoV-2 B.1.351 and P.1 Variants

Deep Learning Driven Drug Discovery: Tackling Severe Acute Respiratory Syndrome Coronavirus 2

The Role of Spike Protein Mutations in the Infectious Power of SARS‐COV‐2 Variants: A Molecular Interaction Perspective

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