RT Journal Article
SR Electronic
T1 Early Computational Detection of Potential High Risk SARS-CoV-2 Variants
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.24.474095
DO 10.1101/2021.12.24.474095
A1 Karim Beguir
A1 Marcin J. Skwark
A1 Yunguan Fu
A1 Thomas Pierrot
A1 Nicolas Lopez Carranza
A1 Alexandre Laterre
A1 Ibtissem Kadri
A1 Abir Korched
A1 Anna U. Lowegard
A1 Bonny Gaby Lui
A1 Bianca Sänger
A1 Yunpeng Liu
A1 Asaf Poran
A1 Alexander Muik
A1 Ugur Sahin
YR 2022
UL http://biorxiv.org/content/early/2022/09/20/2021.12.24.474095.abstract
AB The ongoing COVID-19 pandemic is leading to the discovery of hundreds of novel SARS-CoV-2 variants on a daily basis. While most variants do not impact the course of the pandemic, some variants pose a significantly increased risk when the acquired mutations allow better evasion of antibody neutralisation in previously infected or vaccinated subjects 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. Here we present 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. These metrics can be combined into 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. With only the S protein nucleotide sequence as input, the EWS detects HRVs earlier and with better precision than baseline metrics such as the growth metric (which requires real-world observations) or random sampling. Notably, Omicron BA.1 was flagged by the EWS on the day its sequence was made available. Additionally, our immune escape and fitness metrics were experimentally validated using in vitro pseudovirus-based virus neutralisation test (pVNT) assays and binding assays. The EWS flagged as potentially dangerous all 16 variants (Alpha-Omicron BA.1/2/4/5) designated by the World Health Organisation (WHO) with an average lead time of more than one and a half months ahead of them being designated as such.One-Sentence Summary A COVID-19 Early Warning System combining structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.Competing Interest StatementU.S. is a management board member and employee at BioNTech SE. A.M., B.G.L. and B.S. are employees at BioNTech SE. A.P. and Y. L. are employees at BioNTech US. U.S. and A.M. are inventors on patents and patent applications related to RNA technology and the COVID-19 vaccine. U.S., A.M., B.G.L., and B.S. have securities from BioNTech SE. K.B. is a management board member and employee at InstaDeep Ltd. M.J.S., Y.F., T.P., N.L.C., A.L.,I.K., A.K. and A.U.L. are employees of InstaDeep Ltd or its subsidiaries. K.B., M.J.S., Y.F., T.P., N.L.C., and A.L. are inventors on patents and patent applications related to AI technology. K.B., M.J.S., Y.F., T.P., N.L.C., and A.L. have securities from InstaDeep Ltd.