High resolution cryo-EM structures of two potently SARS-CoV-2 neutralizing monoclonal antibodies of same donor origin that vary in neutralizing Omicron variants (preprint)

While vaccines have by large been found to effective against the evolving SARS-CoV-2 variants, the profound and rapid effectivity of monoclonal antibodies (mAbs) in significantly reducing hospitalization to severe disease outcomes have also been demonstrated. In the present study, by high resolution cryo-electron microscopy (cryo-EM), we examined the structural insights of two trimeric spike (S) protein bound mAbs isolated from an Indian convalescent individual infected with ancestral SARS-CoV-2 which we recently reported to potently neutralize SARS-CoV-2 from its ancestral form through highly virulent Delta form however different in their ability to neutralize Omicron variants. Our findings showed binding and conformational heterogeneities of both the mAbs (THSC20.HVTR04 and THSC20.HVTR26) bound to S trimer in its apo and hACE-2 bound forms. Additionally, cryo-EM resolved structure assisted modeling highlighted key residues associated with the ability of these two mAbs to neutralize Omicron variants. Our findings highlighted key interacting features modulating antigen-antibody interacting that can further aid in structure guided antibody engineering to enhance their breadth and potency.

Ancestral SARS-CoV-2 driven antibody repertoire diversity in an unvaccinated individual correlates with expanded neutralization breadth (preprint)

Understanding the quality of immune repertoire triggered during natural infection can provide vital clues that form the basis for development of humoral immune response in some individuals capable of broadly neutralizing pan SARS-CoV-2 variants. We assessed the diversity of neutralizing antibody responses developed in an unvaccinated individual infected with ancestral SARS-CoV-2 by examining the ability of the distinct B cell germline-derived monoclonal antibodies (mAbs) in neutralizing known and currently circulating Omicron variants by pseudovirus and authentic virus neutralization assays. The ability of the antibodies developed post vaccination in neutralizing Omicron variants was compared to that obtained at baseline of the same individual and to those obtained from Omicron breakthrough infected individuals by pseudovirus neutralization assay. Broadly SARS-CoV-2 neutralizing mAbs representing unique B cell lineages with non-overlapping epitope specificities isolated from a single donor varied in their ability to neutralize Omicron variants. Plasma antibodies developed post vaccination from this individual demonstrated neutralization of Omicron BA.1, BA.2 and BA.4 with increased magnitude and found to be comparable with those obtained from other vaccinated individuals who were infected with ancestral SARS-CoV-2. Development of B cell repertoire capable of producing antibodies with distinct affinity and specificities for the antigen immediately after infection capable of eliciting broadly neutralizing antibodies offers highest probability in protecting against evolving SARS-CoV-2 variants.

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework (preprint)

The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets — the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

Accelerating Inhibitor Discovery for Multiple SARS-CoV-2 Targets with a Single, Sequence-Guided Deep Generative Framework (preprint)

The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets -- the main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit discovery across different emerging drug-targets.

A combination of potently neutralizing monoclonal antibodies isolated from an Indian convalescent donor protects against the SARS-CoV-2 delta variant (preprint)

Although efficacious vaccines have significantly reduced the morbidity and mortality due to COVID-19, there remains an unmet medical need for treatment options, which monoclonal antibodies (mAbs) can potentially fill. This unmet need is exacerbated by the emergence and spread of SARS-CoV-2 variants of concern (VOCs) that have shown some resistance to vaccine responses. Here we report the isolation of two highly potently neutralizing mAbs (THSC20.HVTR04 and THSC20.HVTR26) from an Indian convalescent donor, that neutralize SARS-CoV-2 VOCs at picomolar concentrations including the delta variant (B.1.617.2). These two mAbs target non-overlapping epitopes on the receptor-binding domain (RBD) of the spike protein thereby preventing the virus attachment to its host receptor, human angiotensin converting enzyme-2 (hACE2). Furthermore, the mAb cocktail demonstrated protection against the Delta variant at low antibody doses when passively administered in the K18 hACE2 transgenic mice model, highlighting their potential as cocktail for prophylactic and therapeutic applications. Developing the capacity to rapidly discover and develop mAbs effective against highly transmissible pathogens like coronaviruses at a local level, especially in a low- and middle-income country (LMIC) such as India, will enable prompt responses to future pandemics as an important component of global pandemic preparedness.

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models (preprint)

The novel nature of SARS-CoV-2 calls for the development of efficient de novo drug design approaches. In this study, we propose an end-to-end framework, named CogMol (Controlled Generation of Molecules), for designing new drug-like small molecules targeting novel viral proteins with high affinity and off-target selectivity. CogMol combines adaptive pre-training of a molecular SMILES Variational Autoencoder (VAE) and an efficient multi-attribute controlled sampling scheme that uses guidance from attribute predictors trained on latent features. To generate novel and optimal drug-like molecules for unseen viral targets, CogMol leverages a protein-molecule binding affinity predictor that is trained using SMILES VAE embeddings and protein sequence embeddings learned unsupervised from a large corpus. CogMol framework is applied to three SARS-CoV-2 target proteins: main protease, receptor-binding domain of the spike protein, and non-structural protein 9 replicase. The generated candidates are novel at both molecular and chemical scaffold levels when compared to the training data. CogMol also includes insilico screening for assessing toxicity of parent molecules and their metabolites with a multi-task toxicity classifier, synthetic feasibility with a chemical retrosynthesis predictor, and target structure binding with docking simulations. Docking reveals favorable binding of generated molecules to the target protein structure, where 87-95 % of high affinity molecules showed docking free energy < -6 kcal/mol. When compared to approved drugs, the majority of designed compounds show low parent molecule and metabolite toxicity and high synthetic feasibility. In summary, CogMol handles multi-constraint design of synthesizable, low-toxic, drug-like molecules with high target specificity and selectivity, and does not need target-dependent fine-tuning of the framework or target structure information.