Comparison of the Neutralization Power of Sotrovimab Against SARS-CoV-2 Variants: Development of a Rapid Computational Method.

BACKGROUND: The rapid evolution of SARS-CoV-2 imposed a huge challenge on disease control. Immune evasion caused by genetic variations of the SARS-CoV-2 spike protein's immunogenic epitopes affects the efficiency of monoclonal antibody-based therapy of COVID-19. Therefore, a rapid method is needed to evaluate the efficacy of the available monoclonal antibodies against the new emerging variants or potential novel variants. OBJECTIVE: The aim of this study is to develop a rapid computational method to evaluate the neutralization power of anti-SARS-CoV-2 monoclonal antibodies against new SARS-CoV-2 variants and other potential new mutations. METHODS: The amino acid sequence of the extracellular domain of the spike proteins of the severe acute respiratory syndrome coronavirus (GenBank accession number YP_009825051.1) and SARS-CoV-2 (GenBank accession number YP_009724390.1) were used to create computational 3D models for the native spike proteins. Specific mutations were introduced to the curated sequence to generate the different variant spike models. The neutralization potential of sotrovimab (S309) against these variants was evaluated based on its molecular interactions and Gibbs free energy in comparison to a reference model after molecular replacement of the reference receptor-binding domain with the variant's receptor-binding domain. RESULTS: Our results show a loss in the binding affinity of the neutralizing antibody S309 with both SARS-CoV and SARS-CoV-2. The binding affinity of S309 was greater to the Alpha, Beta, Gamma, and Kappa variants than to the original Wuhan strain of SARS-CoV-2. However, S309 showed a substantially decreased binding affinity to the Delta and Omicron variants. Based on the mutational profile of Omicron subvariants, our data describe the effect of the G339H and G339D mutations and their role in escaping antibody neutralization, which is in line with published clinical reports. CONCLUSIONS: This method is rapid, applicable, and of interest to adapt the use of therapeutic antibodies to the treatment of emerging variants. It could be applied to antibody-based treatment of other viral infections.

How concerning is a SARS-CoV-2 variant of concern? Computational predictions and the variants labeling system.

In this study, we evaluated the use of a predictive computational approach for SARS-CoV-2 genetic variations analysis in improving the current variant labeling system. First, we reviewed the basis of the system developed by the World Health Organization (WHO) for the labeling of SARS-CoV-2 genetic variants and the derivative adapted by the United States Centers for Disease Control and Prevention (CDC). Both labeling systems are based on the virus' major attributes. However, we found that the labeling criteria of the SARS-CoV-2 variants derived from these attributes are not accurately defined and are used differently by the two agencies. Consequently, discrepancies exist between the labels given by WHO and the CDC to the same variants. Our observations suggest that giving the variant of concern (VOC) label to a new variant is premature and might not be appropriate. Therefore, we used a comparative computational approach to predict the effects of the mutations on the virus structure and functions of five VOCs. By linking these data to the criteria used by WHO/CDC for variant labeling, we ascertained that a predictive computational comparative approach of the genetic variations is a good way for rapid and more accurate labeling of SARS-CoV-2 variants. We propose to label all emergent variants, variant under monitoring or variant being monitored (VUM/VBM), and to carry out computational predictive studies with thorough comparison to existing variants, upon which more appropriate and informative labels can be attributed. Furthermore, harmonization of the variant labeling system would be globally beneficial to communicate about and fight the COVID-19 pandemic.

A Computational Approach to Evaluate the Combined Effect of SARS-CoV-2 RBD Mutations and ACE2 Receptor Genetic Variants on Infectivity: The COVID-19 Host-Pathogen Nexus.

SARS-CoV-2 infectivity is largely determined by the virus Spike protein binding to the ACE2 receptor. Meanwhile, marked infection rate differences were reported between populations and individuals. To understand the disease dynamic, we developed a computational approach to study the implications of both SARS-CoV-2 RBD mutations and ACE2 polymorphism on the stability of the virus-receptor complex. We used the 6LZG PDB RBD/ACE2 3D model, the mCSM platform, the LigPlot+ and PyMol software to analyze the data on SARS-CoV-2 mutations and ACE variants retrieved from GISAID and Ensembl/GnomAD repository. We observed that out of 351 RBD point mutations, 83% destabilizes the complex according to free energy (ΔΔG) differences. We also spotted variations in the patterns of polar and hydrophobic interactions between the mutations occurring in 15 out of 18 contact residues. Similarly, comparison of the effect on the complex stability of different ACE2 variants showed that the pattern of molecular interactions and the complex stability varies also according to ACE2 polymorphism. We infer that it is important to consider both ACE2 variants and circulating SARS-CoV-2 RBD mutations to assess the stability of the virus-receptor association and evaluate infectivity. This approach might offers a good molecular ground to mitigate the virus spreading.

Genetic variants of the EGFR ligand-binding domain and their association with structural alterations in Arab cancer patients.

OBJECTIVE: This study aimed to identify novel genetic variants in the CR2 extracellular domain of the epidermal growth factor receptor (EGFR) in healthy individuals and patients with six different types of adenocarcinoma, in Arabian peninsula populations. It also aimed to investigate the effects of these variants on the EGFR structure and their eventual relevance to tumorigenesis. RESULTS: We detected seven new EGFR genetic variants in 168 cancer patients and 114 controls. A SNP rs374670788 was more frequent in bladder cancer but not significantly associated to. However, a missense mutation (V550M) was significantly associated to colon, ovary, lung, bladder and thyroid cancer samples (p < 0.05). Three mutations (H590R, E602K and T605T) were found in the heterozygous form only in colon cancer patients. Genomic analysis of the synonymous mutation (G632G) showed that the T/A genotype could be associated to thyroid cancer in Arab patients (p < 0.05). An additional novel SNP rs571064657 was observed in control individuals. Computational analysis of the genetic variants revealed a reduction in the stabilization of the EGFR tethered form for both V550M and the common R521K variant with low energetic state (- ∆∆G). Molecular interactions analysis suggested that these mutations might affect the receptor's function and promote tumorigenesis.

Engineering of the upper hinge region of human IgG1 Fc enhances the binding affinity to FcγIIIa (CD16a) receptor isoform.

The interaction between antibodies and Immune cells surface FcγRIIIa (CD16a) receptor triggers a variety of immune responses including antibody-dependent cell-mediated cytotoxicity, antibody neutralization, phagocytosis, inflammation and tissue injury. Recent studies showed that IgG1 upper hinge region and FcγRs polymorphism play a major role in the interaction with Fcγ receptors and in the stability of the immune complex hence, in mounting strong inflammatory response. To further investigate this issue, we developed a tool box of IgG1 Fc isoforms to depict the affinity between mutated IgG1 Fc regions and extracellular domain variants (V158F) of CD16a. Our strategy consisted of designing different random upper-hinge mutated variants of IgG1 Fc domain, reproducing the naturally occurring two variants of CD16a and producing all of them as recombinant fusion proteins in Pichia Pastoris. The interactions were assayed using the Surface Plasmon Resonance (Biacore) method along with an in silico analysis to identify the major interaction and key residues that underline the affinity between the Fc region and CD16a variants. Our data showed that the affinity of the Fc region to the CD16a is strongly correlated to polar interactions. This molecular engineering approach yielded an IgG1Fc mutant with enhanced binding affinity to CD16a F158 variant.