Elucidating important structural features for the binding affinity of spike - SARS-CoV-2 neutralizing antibody complexes

Predicting the immune escape of SARS-CoV-2 neutralizing antibodies upon mutation

COVID-19 has resulted in millions of deaths and severe impact on economies worldwide. Moreover, the emergence of SARS-CoV-2 variants presented significant challenges in controlling the pandemic, particularly their potential to avoid the immune system and evade vaccine immunity. This has led to a growing need for research to predict how mutations in SARS-CoV-2 reduces the ability of antibodies to neutralize the virus. In this study, we assembled a set of 1813 mutations from the interface of SARS-CoV-2 spike protein's receptor binding domain (RBD) and neutralizing antibody complexes and developed a machine learning model to classify high or low escape mutations using interaction energy, inter-residue contacts and predicted binding free energy change. Our approach achieved an Area under the Receiver Operating Characteristics (ROC) Curve (AUC) of 0.91 using the Random Forest classifier on the test dataset with 217 mutations. The model was further utilized to predict the escape mutations on a dataset of 29,165 mutations located at the interface of 83 RBD-neutralizing antibody complexes. A small subset of this dataset was also validated based on available experimental data. We found that top 10 % high escape mutations were dominated by charged to nonpolar mutations whereas low escape mutations were dominated by polar to nonpolar mutations. We believe that the present method will allow prioritization of high/low escape mutations in the context of neutralizing antibodies targeting SARS-CoV-2 RBD region and assist antibody design for current and emerging variants.

SARS-CoV-2 variants infectivity prediction and therapeutic peptide design using computational approaches

The outbreak of severe acute respiratory coronavirus 2 (SARS-CoV-2) has created a public health emergency globally. SARS-CoV-2 enters the human cell through the binding of the spike protein to human angiotensin converting enzyme 2 (ACE2) receptor. Significant changes have been reported in the mutational landscape of SARS-CoV-2 in the receptor binding domain (RBD) of S protein, subsequent to evolution of the pandemic. The present study examines the correlation between the binding affinity of mutated S-proteins and the rate of viral infectivity. For this, the binding affinity of SARS-CoV and variants of SARS-CoV-2 towards ACE2 was computationally determined. Subsequently, the RBD mutations were classified on the basis of the number of strains identified with respect to each mutation and the resulting variation in the binding affinity was computationally examined. The molecular docking studies indicated a significant correlation between the Z-Rank score of mutated S proteins and the rate of infectivity, suitable for predicting SARS-CoV-2 infectivity. Accordingly, a 30-mer peptide was designed and the inhibitory properties were computationally analyzed. Single amino acid-wise mutation was performed subsequently to identify the peptide with the highest binding affinity. Molecular dynamics and free energy calculations were then performed to examine the stability of the peptide-protein complexes. Additionally, selected peptides were synthesized and screened using a colorimetric assay. Together, this study developed a model to predict the rate of infectivity of SARS-CoV-2 variants and propose a potential peptide that can be used as an inhibitor for the viral entry to human.Communicated by Ramaswamy H. Sarma.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

The effect of mutations on binding interactions between the SARS-CoV-2 receptor binding domain and neutralizing antibodies B38 and CB6

SARS-CoV-2 is the pathogen responsible for COVID-19 that has claimed over six million lives as of July 2022. The severity of COVID-19 motivates a need to understand how it could evolve to escape potential treatments and to find ways to strengthen existing treatments. Here, we used the molecular modeling methods MD + FoldX and PyRosetta to study the SARS-CoV-2 spike receptor binding domain (S-RBD) bound to two neutralizing antibodies, B38 and CB6 and generated lists of antibody escape and antibody strengthening mutations. Our resulting watchlist contains potential antibody escape mutations against B38/CB6 and consists of 211/186 mutations across 35/22 S-RBD sites. Some of these mutations have been identified in previous studies as being significant in human populations (e.g., N501Y). The list of potential antibody strengthening mutations that are predicted to improve binding of B38/CB6 to S-RBD consists of 116/45 mutations across 29/13 sites. These mutations could be used to improve the therapeutic value of these antibodies.

Probing the mutational landscape of the SARS-CoV-2 spike protein via quantum mechanical modeling of crystallographic structures

We employ a recently developed complexity-reduction quantum mechanical (QM-CR) approach, based on complexity reduction of density functional theory calculations, to characterize the interactions of the SARS-CoV-2 spike receptor binding domain (RBD) with ACE2 host receptors and antibodies. QM-CR operates via ab initio identification of individual amino acid residue's contributions to chemical binding and leads to the identification of the impact of point mutations. Here, we especially focus on the E484K mutation of the viral spike protein. We find that spike residue 484 hinders the spike's binding to the human ACE2 receptor (hACE2). In contrast, the same residue is beneficial in binding to the bat receptor Rhinolophus macrotis ACE2 (macACE2). In agreement with empirical evidence, QM-CR shows that the E484K mutation allows the spike to evade categories of neutralizing antibodies like C121 and C144. The simulation also shows how the Delta variant spike binds more strongly to hACE2 compared to the original Wuhan strain, and predicts that a E484K mutation can further improve its binding. Broad agreement between the QM-CR predictions and experimental evidence supports the notion that ab initio modeling has now reached the maturity required to handle large intermolecular interactions central to biological processes.

Design of Three Residues Peptides against SARS-CoV-2 Infection

The continuous and rapid spread of the COVID-19 pandemic has emphasized the need to seek new therapeutic and prophylactic treatments. Peptide inhibitors are a valid alternative approach for the treatment of emerging viral infections, mainly due to their low toxicity and high efficiency. Recently, two small nucleotide signatures were identified in the genome of some members of the Coronaviridae family and many other human pathogens. In this study, we investigated whether the corresponding amino acid sequences of such nucleotide sequences could have effects on the viral infection of two representative human coronaviruses: HCoV-OC43 and SARS-CoV-2. Our results showed that the synthetic peptides analyzed inhibit the infection of both coronaviruses in a dose-dependent manner by binding the RBD of the Spike protein, as suggested by molecular docking and validated by biochemical studies. The peptides tested do not provide toxicity on cultured cells or human erythrocytes and are resistant to human serum proteases, indicating that they may be very promising antiviral peptides.

Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus-related antibodies

SUMMARY

We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data.

AVAILABILITY AND IMPLEMENTATION

Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Elucidating important structural features for the binding affinity of spike - SARS-CoV-2 neutralizing antibody complexes

The coronavirus disease 2019 (COVID-19) has affected the lives of millions of people around the world. In an effort to develop therapeutic interventions and control the pandemic, scientists have isolated several neutralizing antibodies against SARS-CoV-2 from the vaccinated and convalescent individuals. These antibodies can be explored further to understand SARS-CoV-2 specific antigen-antibody interactions and biophysical parameters related to binding affinity, which can be utilized to engineer more potent antibodies for current and emerging SARS-CoV-2 variants. In the present study, we have analyzed the interface between spike protein of SARS-CoV-2 and neutralizing antibodies in terms of amino acid residue propensity, pair preference, and atomic interaction energy. We observed that Tyr residues containing contacts are highly preferred and energetically favorable at the interface of spike protein-antibody complexes. We have also developed a regression model to relate the experimental binding affinity for antibodies using structural features, which showed a correlation of 0.93. Moreover, several mutations at the spike protein-antibody interface were identified, which may lead to immune escape (epitope residues) and improved affinity (paratope residues) in current/emerging variants. Overall, the work provides insights into spike protein-antibody interactions, structural parameters related to binding affinity and mutational effects on binding affinity change, which can be helpful to develop better therapeutics against COVID-19.

A Deadly Embrace: Hemagglutination Mediated by SARS-CoV-2 Spike Protein at Its 22 N-Glycosylation Sites, Red Blood Cell Surface Sialoglycoproteins, and Antibody

Rouleaux (stacked clumps) of red blood cells (RBCs) observed in the blood of COVID-19 patients in three studies call attention to the properties of several enveloped virus strains dating back to seminal findings of the 1940s. For COVID-19, key such properties are: (1) SARS-CoV-2 binds to RBCs in vitro and also in the blood of COVID-19 patients; (2) although ACE2 is its target for viral fusion and replication, SARS-CoV-2 initially attaches to sialic acid (SA) terminal moieties on host cell membranes via glycans on its spike protein; (3) certain enveloped viruses express hemagglutinin esterase (HE), an enzyme that releases these glycan-mediated bindings to host cells, which is expressed among betacoronaviruses in the common cold strains but not the virulent strains, SARS-CoV, SARS-CoV-2 and MERS. The arrangement and chemical composition of the glycans at the 22 N-glycosylation sites of SARS-CoV-2 spike protein and those at the sialoglycoprotein coating of RBCs allow exploration of specifics as to how virally induced RBC clumping may form. The in vitro and clinical testing of these possibilities can be sharpened by the incorporation of an existing anti-COVID-19 therapeutic that has been found in silico to competitively bind to multiple glycans on SARS-CoV-2 spike protein.

Predictions of the SARS-CoV-2 Omicron Variant (B.1.1.529) Spike Protein Receptor-Binding Domain Structure and Neutralizing Antibody Interactions

The genome of the SARS-CoV-2 Omicron variant (B.1.1.529) was released on November 22, 2021, which has caused a flurry of media attention due the large number of mutations it contains. These raw data have spurred questions around vaccine efficacy. Given that neither the structural information nor the experimentally-derived antibody interaction of this variant are available, we have turned to predictive computational methods to model the mutated structure of the spike protein's receptor binding domain and posit potential changes to vaccine efficacy. In this study, we predict some structural changes in the receptor-binding domain that may reduce antibody interaction without completely evading existing neutralizing antibodies (and therefore current vaccines).

Structural topological analysis of spike proteins of SARS-CoV-2 variants of concern highlight distinctive amino acid substitution patterns

Since the onset of pandemic in 2019, SARS-CoV-2 has diverged into numerous variants driven by antigenic and infectivity-oriented selection. Some variants have accumulated fitness-enhancing mutations, evaded immunity and spread despite global vaccination campaigns. The spike (S) glycoprotein of SARS-CoV-2 demonstrated the greatest immunogenicity and amino acid substitution diversity owing to its importance in the interaction with human angiotensin receptor 2 (hACE2). The S protein consistently emerges as an amino acid substitution (AAS) hotspot in all six lineages, however, in Omicron this enrichment is significantly higher. This study attempts to design and validate a method of mapping S-protein substitution profile across variants to identify the conserved and AAS regions. A substitution matrix was created based on publicly available databases, and the substitution localization was illustrated on a cryo-electron microscopy generated S-protein model. Our analyses indicated that the diversity of N-terminal (NTD) and receptor-binding (RBD) domains exceeded that of any other regions but still contained extended low substitution density regions particularly considering significantly broader substitution profiles of Omicron BA.2 and BA.4/5. Finally, the substitution matrix was compared to a random sample alignment of variant sequences, revealing discrepancies. Therefore, it was suggested to improve matrix accuracy by processing a large number of S-protein sequences using an automated algorithm. Several critical immunogenic and receptor-interacting residues were identified in the conserved regions within NTD and RBD. In conclusion, the structural and topological analysis of S proteins of SARS-CoV-2 variants highlight distinctive amino acid substitution patterns which may be foundational in predicting future variants.

Improving antibody affinity through <i>in vitro</i> mutagenesis in complementarity determining regions

High-affinity antibodies are widely used in diagnostics and for the treatment of human diseases. However, most antibodies are isolated from semi-synthetic libraries by phage display and do not possess in vivo affinity maturation, which is triggered by antigen immunization. It is therefore necessary to engineer the affinity of these antibodies by way of in vitro assaying. In this study, we optimized the affinity of two human monoclonal antibodies which were isolated by phage display in a previous related study. For the 42A1 antibody, which targets the liver cancer antigen glypican-3, the variant T57H in the second complementarity-determining region of the heavy chain (CDR-H2) exhibited a 2.6-fold improvement in affinity, as well as enhanced cell-binding activity. For the I4A3 antibody to severe acute respiratory syndrome coronavirus 2, beneficial single mutations in CDR-H2 and CDR-H3 were randomly combined to select the best synergistic mutations. Among these, the mutation S53P-S98T improved binding affinity (about 3.7 fold) and the neutralizing activity (about 12 fold) compared to the parent antibody. Taken together, single mutations of key residues in antibody CDRs were enough to increase binding affinity with improved antibody functions. The mutagenic combination of key residues in different CDRs creates additive enhancements. Therefore, this study provides a safe and effective in vitro strategy for optimizing antibody affinity.