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

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, 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 coronavirus variants. Using over 1300 IgG sequences derived from patient B cells bound with the viral spike's receptor binding domain (RBD), we first established protein structural docking models in assessing the IgG-RBD-ACE2 interaction interfaces and predicting their viral neutralizing activities with a confidence score. The confidence score is calculated as a fraction of IgG-blocking RBD binding sites versus all ACE2-interacting sites. Additionally, employing Gaussian process regression (also known as Kriging) in a latent space of an antibody language model, we predicted the IgGs' activity profiles against viral strains. Using functional analyses and experimental validations, we subsequently prioritized IgG candidates for neutralizing a broad spectrum of viral variants (wildtype, Delta, and Omicron) and preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. To further improve the blockade efficacy for the Delta strain (B.1.617), we rationally redesigned the IgG clones with single amino acid substitutions at the RBD-binding interface. Our work expedites applications of artificial intelligence in low-data regimes when limited data is available, including protein language models (using unlabeled data) and Kriging (using few labeled data) for antibody sequence analysis, activity prediction, and efficacy improvement, which are aided by physics-driven protein docking models for antibody-antigen interface structure analyses.

Impact of COVID-19 Policies and Misinformation on Social Unrest (preprint)

The novel coronavirus disease (COVID-19) pandemic has impacted every corner of earth, disrupting governments and leading to socioeconomic instability. This crisis has prompted questions surrounding how different sectors of society interact and influence each other during times of change and stress. Given the unprecedented economic and societal impacts of this pandemic, many new data sources have become available, allowing us to quantitatively explore these associations. Understanding these relationships can help us better prepare for future disasters and mitigate the impacts. Here, we focus on the interplay between social unrest (protests), health outcomes, public health orders, and misinformation in eight countries of Western Europe and four regions of the United States. We created 1-3 week forecasts of both a binary protest metric for identifying times of high protest activity and the overall protest counts over time. We found that for all regions, except Belgium, at least one feature from our various data streams was predictive of protests. However, the accuracy of the protest forecasts varied by country, that is, for roughly half of the countries analyzed, our forecasts outperform a na\"ive model. These mixed results demonstrate the potential of diverse data streams to predict a topic as volatile as protests as well as the difficulties of predicting a situation that is as rapidly evolving as a pandemic.