Since the appearance in the late of December 2019, SARS-CoV-2 is rapidly evolving and mutating continuously, giving rise to various variants with variable degrees of infectivity and lethality. The virus that initially appeared in China later mutated several times, wreaking havoc and claiming many lives worldwide amid the ongoing COVID-19 pandemic. After Alpha, Beta, Gamma, and Delta variants, the most recently emerged variant of concern (VOC) is the Omicron (B.1.1.529) that has evolved due to the accumulation of high numbers of mutations especially in the spike protein, raising concerns for its ability to evade from pre-existing immunity acquired through vaccination or natural infection as well as overpowering antibodies-based therapies. Several theories are on the surface to explain how the Omicron has gathered such a high number of mutations within less time. Few of them are higher mutation rates within a subgroup of population and then its introduction to a larger population, long term persistence and evolution of the virus in immune-compromised patients, and epizootic infection in animals from humans, where under different immune pressures the virus mutated and then got reintroduced to humans. Multifaceted approach including rapid diagnosis, genome analysis of emerging variants, ramping up of vaccination drives and receiving booster doses, efficacy testing of vaccines and immunotherapies against newly emerged variants, updating the available vaccines, designing of multivalent vaccines able to generate hybrid immunity, up-gradation of medical facilities and strict implementation of adequate prevention and control measures need to be given high priority to handle the on-going SARS-CoV-2 pandemic successfully. 

https://doi.org/10.1016/j.envres.2022.112816

Emergence of SARS-CoV-2 Omicron (B.1.1.529) variant, salient features, high global health concerns and strategies to counter it amid ongoing COVID-19 pandemic

Abstract Among the 30 nonsynonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (1) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (2) interactions of Spike with ACE2 receptors, and (3) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron overall previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected.

Selection Analysis Identifies Clusters of Unusual Mutational Changes in Omicron Lineage BA.1 That Likely Impact Spike Function

Among the 30 nonsynonymous nucleotide substitutions in the Omicron S-gene are 13 that have only rarely been seen in other SARS-CoV-2 sequences. These mutations cluster within three functionally important regions of the S-gene at sites that will likely impact (1) interactions between subunits of the Spike trimer and the predisposition of subunits to shift from down to up configurations, (2) interactions of Spike with ACE2 receptors, and (3) the priming of Spike for membrane fusion. We show here that, based on both the rarity of these 13 mutations in intrapatient sequencing reads and patterns of selection at the codon sites where the mutations occur in SARS-CoV-2 and related sarbecoviruses, prior to the emergence of Omicron the mutations would have been predicted to decrease the fitness of any virus within which they occurred. We further propose that the mutations in each of the three clusters therefore cooperatively interact to both mitigate their individual fitness costs, and, in combination with other mutations, adaptively alter the function of Spike. Given the evident epidemic growth advantages of Omicron overall previously known SARS-CoV-2 lineages, it is crucial to determine both how such complex and highly adaptive mutation constellations were assembled within the Omicron S-gene, and why, despite unprecedented global genomic surveillance efforts, the early stages of this assembly process went completely undetected. 

OUP accepted manuscript

Monoclonal antibodies (mAbs) offer a treatment option for individuals with severe COVID-19 and are especially important in high-risk individuals where vaccination is not an option. Given the importance of understanding the evolution of resistance to mAbs by SARS-CoV-2, we reviewed the available in vitro neutralization data for mAbs against live variants and viral constructs containing spike mutations of interest. Unfortunately, evasion of mAb-induced protection is being reported with new SARS-CoV-2 variants. The magnitude of neutralization reduction varied greatly among mAb-variant pairs. For example, sotrovimab retained its neutralization capacity against Omicron BA.1 but showed reduced efficacy against BA.2, BA.4 and BA.5, and BA.2.12.1. At present, only bebtelovimab has been reported to retain its efficacy against all SARS-CoV-2 variants considered here. Resistance to mAb neutralization was dominated by the action of epitope single amino acid substitutions in the spike protein. Although not all observed epitope mutations result in increased mAb evasion, amino acid substitutions at non-epitope positions and combinations of mutations also contribute to evasion of neutralization. This Review highlights the implications for the rational design of viral genomic surveillance and factors to consider for the development of novel mAb therapies. 

/pdf/sars-cov-2-variant-evasion-of-monoclonal-antibodies-based-on-2nxh0p1s.pdf

SARS-CoV-2 variant evasion of monoclonal antibodies based on in vitro studies

The omicron (B.1.1.529) variant of SARS-CoV-2 evolved into several sublineages, three of which (BA.1, BA.2, and BA.5) became globally dominant. Currently, the prevalence of omicron subvariants BQ.1 (a subvariant of BA.5), its sublineage BQ.1.1, and XBB (a recombinant of two different BA.2 subvariants) is increasing rapidly in the USA, France, Singapore, India, and elsewhere. BQ.1.1 and XBB possess substitutions relative to BA.5 and BA.2, respectively, in the receptor-binding domain of their spike protein (appendix p 4), which is the major target for vaccines and therapeutic monoclonal antibodies (mAbs) for COVID-19. 

Humoral immune evasion of the omicron subvariants BQ.1.1 and XBB

http://www.cell.com/article/S2666379122000271/pdf

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain

The SARS-CoV-2 Omicron sub-variants BA.1 and BA.2 have become the dominant variants worldwide due to enhanced transmissibility and immune evasion. In response to the rise of BA.1 and BA.2, two recent studies by Liu et al. and Iketani et al. provide a detailed analysis of loss of therapeutic antibody potency through evaluation of escape by pseudotyped viruses harboring BA.1 and BA.2 receptor binding domain (RBD) point mutations. Surprisingly, Liu et al. and Iketani et al. observed a profoundly broad escape effect for the individual mutations S371L and S371F. This result cannot be explained by known escape mechanisms of the SARS-CoV-2 RBD, and conflicts with existing computational and experimental escape measurements for S371 mutations performed on monomeric RBD. Through an examination of these conflicting datasets and a structural analysis of the antibodies assayed by Liu et al. and Iketani et al., we propose a mechanism to explain S371L/F escape according to a perturbation of spike trimer conformational dynamics that has not yet been described for any SARS-CoV-2 escape mutation. The proposed mechanism is relevant to Omicron and future variant surveillance as well as therapeutic antibody design.

/pdf/a-structural-dynamic-explanation-for-observed-escape-of-sars-1b1xruck.pdf

A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F

The dynamic interplay between virus and host plays out across many interacting surfaces as virus and host evolve continually in response to one another. In particular, epitope-paratope interactions (EPIs) between viral antigen and host antibodies drive much of this evolutionary race. In this review, we describe a series of recent studies examining aspects of epitope complexity that go beyond two interacting protein surfaces as EPIs are typically understood. To structure our discussion, we present a framework for understanding epitope complexity as a spectrum along a series of axes, focusing primarily on 1) epitope biochemical complexity (e.g., epitopes involving N-glycans) and 2) antigen conformational/dynamic complexity (e.g., epitopes with differential properties depending on antigen state or fold-axis). We highlight additional epitope complexity factors including epitope tertiary/quaternary structure, which contribute to epistatic relationships between epitope residues within- or adjacent-to a given epitope, as well as epitope overlap resulting from polyclonal antibody responses, which is relevant when assessing antigenic pressure against a given epitope. Finally, we discuss how these different forms of epitope complexity can limit EPI analyses and therapeutic antibody development, as well as recent efforts to overcome these limitations.

/pdf/complexity-of-viral-epitope-surfaces-as-evasive-targets-for-30nf01nj.pdf

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies

Complex glycans decorate viral surface proteins and play a critical role in virus-host interactions. Viral surface glycans shield vulnerable protein epitopes from host immunity yet can also present distinct "glycoepitopes" that can be targeted by host antibodies such as the potent anti-HIV antibody 2G12 that binds high-mannose glycans on gp120. Two recent publications demonstrate 2G12 binding to high mannose glycans on SARS-CoV-2 and select Influenza A (Flu) H3N2 viruses. Previously, our lab observed 2G12 binding and functional inhibition of a range of Flu viruses that include H3N2 and H1N1 lineages. In this manuscript, we present these data alongside structural analyses to offer an expanded picture of 2G12-Flu interactions. Further, based on the remarkable breadth of 2G12 N-glycan recognition and the structural factors promoting glycoprotein oligomannosylation, we hypothesize that 2G12 glycoepitopes can be defined from protein structure alone according to N-glycan site topology. We develop a model describing 2G12 glycoepitopes based on N-glycan site topology, and apply the model to identify viruses within the Protein Data Bank presenting putative 2G12 glycoepitopes for 2G12 repurposing toward analytical, diagnostic, and therapeutic applications. 

/pdf/conserved-topology-of-virus-glycoepitopes-presents-novel-27rrgqg5.pdf

Conserved topology of virus glycoepitopes presents novel targets for repurposing HIV antibody 2G12

The application of Machine Learning (ML) tools to engineer novel antibodies having predictable functional properties is gaining prominence. Herein, we present a platform that employs an ML-guided optimization of the complementarity-determining region (CDR) together with a CDR framework (FR) shuffling method to engineer affinity-enhanced and clinically developable monoclonal antibodies (mAbs) from a limited experimental screen space (order of 10^2 designs) using only two experimental iterations. Although high-complexity deep learning models like graph neural networks (GNNs) and large language models (LLMs) have shown success on protein folding with large dataset sizes, the small and biased nature of the publicly available antibody-antigen interaction datasets is not sufficient to capture the diversity of mutations virtually screened using these models in an affinity enhancement campaign. To address this key gap, we introduced inductive biases learned from extensive domain knowledge on protein-protein interactions through feature engineering and selected model hyper parameters to reduce overfitting of the limited interaction datasets. Notably we show that this platform performs better than GNNs and LLMs on an in-house validation dataset that is enriched in diverse CDR mutations that go beyond alanine-scanning. To illustrate the broad applicability of this platform, we successfully solved a challenging problem of redesigning two different anti-SARS-COV-2 mAbs to enhance affinity (up to 2 orders of magnitude) and neutralizing potency against the dynamically evolving SARS-COV-2 Omicron variants.

/pdf/machine-learning-guided-antibody-engineering-that-leverages-1i1rlsge.pdf

Thomas Clark

Papers

Insights on the mutational landscape of the SARS-CoV-2 Omicron variant receptor-binding domain

A structural dynamic explanation for observed escape of SARS-CoV-2 BA.2 variant mutation S371L/F

Complexity of Viral Epitope Surfaces as Evasive Targets for Vaccines and Therapeutic Antibodies

Conserved topology of virus glycoepitopes presents novel targets for repurposing HIV antibody 2G12

Machine Learning-Guided Antibody Engineering That Leverages Domain Knowledge To Overcome The Small Data Problem