Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy.

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.

Mediator complex subunit 1 architects a tumorigenic Treg cell program independent of inflammation.

While immunotherapy has revolutionized cancer treatment, its safety has been hampered by immunotherapy-related adverse events. Unexpectedly, we show that Mediator complex subunit 1 (MED1) is required for T regulatory (Treg) cell function specifically in the tumor microenvironment. Treg cell-specific MED1 deletion does not predispose mice to autoimmunity or excessive inflammation. In contrast, MED1 is required for Treg cell promotion of tumor growth because MED1 is required for the terminal differentiation of effector Treg cells in the tumor. Suppression of these terminally differentiated Treg cells is sufficient for eliciting antitumor immunity. Both human and murine Treg cells experience divergent paths of differentiation in tumors and matched tissues with non-malignant inflammation. Collectively, we identify a pathway promoting the differentiation of a Treg cell effector subset specific to tumors and demonstrate that suppression of a subset of Treg cells is sufficient for promoting antitumor immunity in the absence of autoimmune consequences.