[Paper Review] Omicron (B.1.1.529): Infectivity, vaccine breakthrough, and antibody resistance

Jiahui Chen, Rui Wang+2 more|arXiv (Cornell University)|Dec 1, 2021

SARS-CoV-2 and COVID-19 ResearchMedicine27 references24 citations

TL;DR

The authors use the TopNetmAb AI model to predict Omicron's higher infectivity and stronger vaccine breakthrough potential, and to assess antibody resistance across 132 antibody-RBD complexes and several FDA-approved mAbs.

ABSTRACT

The latest severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant Omicron (B.1.1.529) has ushered panic responses around the world due to its contagious and vaccine escape mutations. The essential infectivity and antibody resistance of the SARS-CoV-2 variant are determined by its mutations on the spike (S) protein receptor-binding domain (RBD). However, a complete experimental evaluation of Omicron might take weeks or even months. Here, we present a comprehensive quantitative analysis of Omicron's infectivity, vaccine-breakthrough, and antibody resistance. An artificial intelligence (AI) model, which has been trained with tens of thousands of experimental data points and extensively validated by experimental data on SARS-CoV-2, reveals that Omicron may be over ten times more contagious than the original virus or about twice as infectious as the Delta variant. Based on 132 three-dimensional (3D) structures of antibody-RBD complexes, we unveil that Omicron may be twice more likely to escape current vaccines than the Delta variant. The Food and Drug Administration (FDA)-approved monoclonal antibodies (mAbs) from Eli Lilly may be seriously compromised. Omicron may also diminish the efficacy of mAbs from Celltrion and Rockefeller University. However, its impact on Regeneron mAb cocktail appears to be mild.

Motivation & Objective

• Quantify Omicron RBD mutations' impact on ACE2 binding to assess infectivity changes.
• Assess how Omicron RBD mutations affect binding to a large set of antibody-RBD complexes to gauge vaccine breakthrough risk.
• Evaluate the impact of Omicron mutations on FDA-approved monoclonal antibodies and other clinical-stage antibodies.
• Leverage topology-based features and deep learning to provide rapid, data-driven predictions prior to extensive experiments.

Proposed method

• TopNetmAb topology-based AI model trained on tens of thousands of experimental data points.
• Integration of biochemical/biophysical features with algebraic topology (persistent homology) features.
• Use of 3D structures of ACE2-RBD and 132 antibody-RBD complexes to compute mutation-induced binding energy changes (BFE).
• Prediction of BFE changes for 15 Omicron RBD mutations across ACE2 and antibody complexes.
• Validation against existing experimental mutational data and prior predictive performance (Pearson correlations mentioned).

Experimental results

Research questions

• RQ1 How do Omicron RBD mutations alter the binding free energy changes (BFE) of the ACE2-RBD complex and thus infectivity?
• RQ2 To what extent do Omicron mutations disrupt binding between RBD and known antibodies, indicating vaccine breakthrough risk?
• RQ3 Which FDA-approved or clinically relevant mAbs are most impacted by Omicron mutations?
• RQ4 How does Omicron compare to Delta in terms of antibody escape and mAb efficacy?
• RQ5 Can structure-based AI predictions provide timely insights into Omicron’s threat prior to experimental data?

Key findings

• Omicron may be over ten times more contagious than the original SARS-CoV-2 strain and about twice as infectious as Delta based on accumulated BFE changes (2.60 kcal/mol; ~13-fold infectivity increase; Delta comparison ~2.8x).
• Omicron RBD mutations significantly disrupt binding to many known antibodies, suggesting twice the vaccine-escape potential of Delta across 132 antibody-RBD complexes.
• Eli Lilly mAb cocktail efficacy is predicted to be seriously reduced by Omicron mutations (notably K417N, E484A, Q493R, Y505H); Regeneron cocktail shows only mild impact.
• Celltrion’s CT-P59 and Rockefeller University antibodies (C135, C144) may be disrupted by Omicron mutations, though effects vary by antibody.
• Overall, Omicron’s impact on ACE2 binding is limited compared to its impact on antibody escape, signaling a shift toward immune evasion rather than ACE2 affinity alone.