Antigen-Antibody Complex-Guided Exploration of the Hotspots Conferring the Immune-Escaping Ability of the SARS-CoV-2 RBD.

The COVID-19 pandemic resulting from the spread of SARS-CoV-2 spurred devastating health and economic crises around the world. Neutralizing antibodies and licensed vaccines were developed to combat COVID-19, but progress was slow. In addition, variants of the receptor-binding domain (RBD) of the spike protein confer resistance of SARS-CoV-2 to neutralizing antibodies, nullifying the possibility of human immunity. Therefore, investigations into the RBD mutations that disrupt neutralization through convalescent antibodies are urgently required. In this study, we comprehensively and systematically investigated the binding stability of RBD variants targeting convalescent antibodies and revealed that the RBD residues F456, F490, L452, L455, and K417 are immune-escaping hotspots, and E484, F486, and N501 are destabilizing residues. Our study also explored the possible modes of actions of emerging SARS-CoV-2 variants. All results are consistent with experimental observations of attenuated antibody neutralization and clinically emerging SARS-CoV-2 variants. We identified possible immune-escaping hotspots that could further promote resistance to convalescent antibodies. The results provide valuable information for developing and designing novel monoclonal antibody drugs to combat emerging SARS-CoV-2 variants.

Comprehensive Deep Mutational Scanning Reveals the Immune-Escaping Hotspots of SARS-CoV-2 Receptor-Binding Domain Targeting Neutralizing Antibodies.

The rapid spread of SARS-CoV-2 has caused the COVID-19 pandemic, resulting in the collapse of medical care systems and economic depression worldwide. To combat COVID-19, neutralizing antibodies have been investigated and developed. However, the evolutions (mutations) of the receptor-binding domain (RBD) of SARS-CoV-2 enable escape from neutralization by these antibodies, further impairing recognition by the human immune system. Thus, it is critical to investigate and predict the putative mutations of RBD that escape neutralizing immune responses. Here, we employed computational analyses to comprehensively investigate the mutational effects of RBD on binding to neutralizing antibodies and angiotensin-converting enzyme 2 (ACE2) and demonstrated that the RBD residues K417, L452, L455, F456, E484, G485, F486, F490, Q493, and S494 were consistent with clinically emerging variants or experimental observations of attenuated neutralizations. We also revealed common hotspots, Y449, L455, and Y489, that exerted comparable destabilizing effects on binding to both ACE2 and neutralizing antibodies. Our results provide valuable information on the putative effects of RBD variants on interactions with neutralizing antibodies. These findings provide insights into possible evolutionary hotspots that can escape recognition by these antibodies. In addition, our study results will benefit the development and design of vaccines and antibodies to combat the newly emerging variants of SARS-CoV-2.

Markov state modeling reveals alternative unbinding pathways for peptide-MHC complexes.

Peptide binding to major histocompatibility complexes (MHCs) is a central component of the immune system, and understanding the mechanism behind stable peptide-MHC binding will aid the development of immunotherapies. While MHC binding is mostly influenced by the identity of the so-called anchor positions of the peptide, secondary interactions from nonanchor positions are known to play a role in complex stability. However, current MHC-binding prediction methods lack an analysis of the major conformational states and might underestimate the impact of secondary interactions. In this work, we present an atomically detailed analysis of peptide-MHC binding that can reveal the contributions of any interaction toward stability. We propose a simulation framework that uses both umbrella sampling and adaptive sampling to generate a Markov state model (MSM) for a coronavirus-derived peptide (QFKDNVILL), bound to one of the most prevalent MHC receptors in humans (HLA-A24:02). While our model reaffirms the importance of the anchor positions of the peptide in establishing stable interactions, our model also reveals the underestimated importance of position 4 (p4), a nonanchor position. We confirmed our results by simulating the impact of specific peptide mutations and validated these predictions through competitive binding assays. By comparing the MSM of the wild-type system with those of the D4A and D4P mutations, our modeling reveals stark differences in unbinding pathways. The analysis presented here can be applied to any peptide-MHC complex of interest with a structural model as input, representing an important step toward comprehensive modeling of the MHC class I pathway.