2B04 for COVID-19

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of infections and hundreds of thousands of deaths globally. There are no widely available licensed therapeutics against SARS-CoV-2, highlighting an urgent need for effective interventions. The virus enters host cells through binding of a receptor-binding domain within its trimeric spike glycoprotein to human angiotensin-converting enzyme 2. In this article, we describe the generation and characterization of a panel of murine mAbs directed against the receptor-binding domain. One mAb, 2B04, neutralized wild-type SARS-CoV-2 in vitro with remarkable potency (half-maximal inhibitory concentration of <2 ng/ml). In a murine model of SARS-CoV-2 infection, 2B04 protected challenged animals from weight loss, reduced lung viral load, and blocked systemic dissemination. Thus, 2B04 is a promising candidate for an effective antiviral that can be used to prevent SARS-CoV-2 infection.

Even though COVID-19 is no longer the primary focus of the global scientific community, its high mutation rate (nearly 30 substitutions per year) poses a threat of a potential comeback. Effective vaccines have been developed and administered to the population, ending the pandemic. Nonetheless, reinfection by newly emerging subvariants, particularly the latest JN.1 strain, remains common. The rapid mutation of this virus demands a fast response from the scientific community in case of an emergency. While the immune escape of earlier variants was extensively investigated, one still needs a comprehensive understanding of how specific mutations, especially in the newest subvariants, influence the antigenic escape of the pathogen. Here, we tested comprehensive in silico approaches to identify methods for fast and accurate prediction of antibody neutralization by various mutants. As a benchmark, we modeled the complexes of the murine antibody 2B04, which neutralizes infection by preventing the SARS-CoV-2 spike glycoprotein’s association with angiotensin-converting enzyme (ACE2). Complexes with the wild-type, B.1.1.7 Alpha, and B.1.427/429 Epsilon SARS-CoV-2 variants were used as positive controls, while complexes with the B.1.351 Beta, P.1 Gamma, B.1.617.2 Delta, B.1.617.1 Kappa, BA.1 Omicron, and the newest JN.1 Omicron variants were used as decoys. Three essentially different algorithms were employed: forced placement based on a template, followed by two steps of extended molecular dynamics simulations; protein–protein docking utilizing PIPER (an FFT-based method extended for use with pairwise interaction potentials); and the AlphaFold 3.0 model for complex structure prediction. Homology modeling was used to assess the 3D structure of the newly emerged JN.1 Omicron subvariant, whose crystallographic structure is not yet available in the Protein Database. After a careful comparison of these three approaches, we were able to identify the pros and cons of each method. Protein–protein docking yielded two false-positive results, while manual placement reinforced by molecular dynamics produced one false positive and one false negative. In contrast, AlphaFold resulted in only one doubtful result and a higher overall accuracy-to-time ratio. The reasons for inaccuracies and potential pitfalls of various approaches are carefully explained. In addition to a comparative analysis of methods, some mechanisms of immune escape are elucidated herein. This provides a critical foundation for improving the predictive accuracy of vaccine efficacy against new viral subvariants, introducing accurate methodologies, and pinpointing potential challenges.

Abstract Summary Since the emergence of SARS-CoV-2, numerous studies have investigated antibody interactions with viral variants in vitro, and several datasets have been curated to compile available protein structures and experimental measurements. However, existing data remain fragmented, limiting their utility for the development and validation of machine learning models for antibody–antigen interaction prediction. Here, we present CoV-UniBind, a unified database comprising over 75 000 entries of SARS-CoV-2 antibody–antigen sequence, binding, and structural data, integrated and standardized from three public sources and multiple peer-reviewed publications. To demonstrate its utility, we benchmarked multiple protein folding, inverse folding, and language models across tasks relevant to antibody design and vaccine development. We expect CoV-UniBind to facilitate future computational efforts in antibody and vaccine development against SARS-CoV-2. Availability and implementation The curated datasets, model scores and antibody synonyms are free to download at https://huggingface.co/datasets/InstaDeepAI/cov-unibind. Folded structures are available upon request.