Early computational detection of potential high-risk SARS-CoV-2 variants.

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants daily. While most variants do not impact the course of the pandemic, some variants pose an increased risk when the acquired mutations allow better evasion of antibody neutralisation or increased transmissibility. Early detection of such high-risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Presented herein is a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. Both metrics were experimentally validated using in vitro pseudovirus-based neutralisation test and binding assays and were subsequently combined to explore the changing landscape of the pandemic and to create an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. The EWS flagged all 16 variants designated by the World Health Organization (WHO) as variants of interest (VOIs) if applicable or variants of concern (VOCs) otherwise with an average lead time of more than one and a half months ahead of their designation as such.

Early computational detection of potential high-risk SARS-CoV-2 variants

The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants daily. While most variants do not impact the course of the pandemic, some variants pose an increased risk when the acquired mutations allow better evasion of antibody neutralisation or increased transmissibility. Early detection of such high-risk variants (HRVs) is paramount for the proper management of the pandemic. However, experimental assays to determine immune evasion and transmissibility characteristics of new variants are resource-intensive and time-consuming, potentially leading to delays in appropriate responses by decision makers. Presented herein is a novel in silico approach combining spike (S) protein structure modelling and large protein transformer language models on S protein sequences to accurately rank SARS-CoV-2 variants for immune escape and fitness potential. Both metrics were experimentally validated using in vitro pseudovirus-based neutralisation test and binding assays and were subsequently combined to explore the changing landscape of the pandemic and to create an automated Early Warning System (EWS) capable of evaluating new variants in minutes and risk-monitoring variant lineages in near real-time. The system accurately pinpoints the putatively dangerous variants by selecting on average less than 0.3% of the novel variants each week. The EWS flagged all 16 variants designated by the World Health Organisation (WHO) as variants of interest (VOIs) if applicable or variants of concern (VOCs) otherwise with an average lead time of more than one and a half months ahead of their designation as such.

BNT162b vaccines protect rhesus macaques from SARS-CoV-2.

A safe and effective vaccine against COVID-19 is urgently needed in quantities that are sufficient to immunize large populations. Here we report the preclinical development of two vaccine candidates (BNT162b1 and BNT162b2) that contain nucleoside-modified messenger RNA that encodes immunogens derived from the spike glycoprotein (S) of SARS-CoV-2, formulated in lipid nanoparticles. BNT162b1 encodes a soluble, secreted trimerized receptor-binding domain (known as the RBD-foldon). BNT162b2 encodes the full-length transmembrane S glycoprotein, locked in its prefusion conformation by the substitution of two residues with proline (S(K986P/V987P); hereafter, S(P2) (also known as P2 S)). The flexibly tethered RBDs of the RBD-foldon bind to human ACE2 with high avidity. Approximately 20% of the S(P2) trimers are in the two-RBD 'down', one-RBD 'up' state. In mice, one intramuscular dose of either candidate vaccine elicits a dose-dependent antibody response with high virus-entry inhibition titres and strong T-helper-1 CD4+ and IFNγ+CD8+ T cell responses. Prime-boost vaccination of rhesus macaques (Macaca mulatta) with the BNT162b candidates elicits SARS-CoV-2-neutralizing geometric mean titres that are 8.2-18.2× that of a panel of SARS-CoV-2-convalescent human sera. The vaccine candidates protect macaques against challenge with SARS-CoV-2; in particular, BNT162b2 protects the lower respiratory tract against the presence of viral RNA and shows no evidence of disease enhancement. Both candidates are being evaluated in phase I trials in Germany and the USA1-3, and BNT162b2 is being evaluated in an ongoing global phase II/III trial (NCT04380701 and NCT04368728).

Neutralization of SARS-CoV-2 lineage B.1.1.7 pseudovirus by BNT162b2 vaccine-elicited human sera.

Recently, a new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) lineage called B.1.1.7 (variant of concern: VOC 202012/01), which is reported to spread more efficiently and faster than other strains, emerged in the United Kingdom. This variant has an unusually large number of mutations, with 10 amino acid changes in the spike (S) protein, raising concerns that its recognition by neutralizing antibodies may be affected. In this study, we tested SARS-CoV-2-S pseudoviruses bearing either the Wuhan reference strain or the B.1.1.7 lineage spike protein with sera of 40 participants who were vaccinated in a previously reported trial with the messenger RNA-based COVID-19 vaccine BNT162b2. The immune sera had slightly reduced but overall largely preserved neutralizing titers against the B.1.1.7 lineage pseudovirus. These data indicate that the B.1.1.7 lineage will not escape BNT162b2-mediated protection.