Artikel: Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy.
bioRxiv : the preprint server for biology
2024
Abstract: 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- ... ...
Abstract | 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 |
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Sprache | Englisch |
Erscheinungsdatum | 2024-03-04 |
Erscheinungsland | United States |
Dokumenttyp | Preprint |
DOI | 10.1101/2024.03.01.582176 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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