RESUMO
Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. These viruses infect human host cells via the viral spike protein binding to the host cell receptor angiotensin-converting enzyme 2 (ACE2). Using over 1300 IgG sequences derived from convalescent patient B cells that bind with spike's receptor binding domain (RBD), we first established protein structural docking models in assessing the RBD-IgG-ACE2 interaction interfaces and predicting the virus-neutralizing activity of each IgG with a confidence score. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the landscape of IgGs' activity profiles against individual coronaviral variants of concern. With functional analyses and experimental validations, we efficiently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) to prevent the infection of host cells in vitro and hACE2 transgenic mice in vivo. Furthermore, the computational analyses enabled rational redesigns of selective IgG clones with single amino acid substitutions at the RBD-binding interface to improve the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screening and re-design even in low-data regimes combining protein language models and Kriging for antibody sequence analysis, activity prediction, and efficacy improvement, in synergy with physics-driven protein docking models for antibody-antigen interface structure analyses and functional optimization.
RESUMO
Over two years into the COVID-19 pandemic, the human immune response to SARS-CoV-2 during the active disease phase has been extensively studied. However, the long-term impact after recovery, which is critical to advance our understanding SARS-CoV-2 and COVID-19-associated long-term complications, remains largely unknown. Herein, we characterized single-cell profiles of circulating immune cells in the peripheral blood of 100 patients, including convalescent COVID-19 and sero-negative controls. Flow cytometry analyses revealed reduced frequencies of both short-lived monocytes and long-lived regulatory T (Treg) cells within the patients who have recovered from severe COVID-19. sc-RNA seq analysis identifies seven heterogeneous clusters of monocytes and nine Treg clusters featuring distinct molecular signatures in association with COVID-19 severity. Asymptomatic patients contain the most abundant clusters of monocytes and Tregs expressing high CD74 or IFN-responsive genes. In contrast, the patients recovered from a severe disease have shown two dominant inflammatory monocyte clusters featuring S100 family genes: one monocyte cluster of S100A8 & A9 coupled with high HLA-I and another cluster of S100A4 & A6 with high HLA-II genes, a specific non-classical monocyte cluster with distinct IFITM family genes, as well as a unique TGF-ß high Treg Cluster. The outpatients and seronegative controls share most of the monocyte and Treg clusters patterns with high expression of HLA genes. Surprisingly, while presumably short-lived monocytes appear to have sustained alterations over 4 months, the decreased frequencies of long-lived Tregs (high HLA-DRA and S100A6) in the outpatients restore over the tested convalescent time (≥ 4 months). Collectively, our study identifies sustained and dynamically altered monocytes and Treg clusters with distinct molecular signatures after recovery, associated with COVID-19 severity.
