The ISCB COVID-19 webinar collection points to webinars on ongoing research on COVID-19 and Sars-Cov2 organized by ISCB.
May 19, 2020
Efforts to develop antiviral drugs versus COVID-19 or vaccines for its prevention have been hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. This webinar will describe our efforts to address this challenge by expressing 26 of the 29 SARS-CoV-2 proteins in human cells and identifying the human proteins physically associated with each using affinity-purification mass spectrometry. Among 332 high-confidence SARS-CoV-2-human protein-protein interactions, we identified 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Within a subset of these, multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity.
June 26, 2020 at 11:00AM EDT
The urgency of the coronavirus pandemic has motivated investigators world wide to seek approved drugs or investigation new drugs as a way to rapidly advance therapeutics into clinical trials to treat the disease. I will describe a large collaboration, hosted by the UCSF Quantitative Biology Institute, to do that in a mechanistically focused way. Using AP-MS, a host-pathogen network of viral and human proteins was created, and drugs were sought targeting the human partner. From among 322 high confidence human proteins associated with 26 viral proteins emerged 63 that were druggable. Against those, 69 drugs were tested for efficacy, and from these 10 drugs in two broad classes emerged: those targeting protein biogenesis, and those acting against the Sigma1 and Sigma2 receptors. The activities of these drugs, and the chemoinformatics infrastructure that supported their selection, will be discussed. The mechanism-based repurposing strategy will be compared to a complementary effort that targets viral proteins and seeks novel chemical matter, using structure-based ultra-large library docking.
Global surveillance of COVID-19 by mining news media using a multi-source dynamic embedded topic model
By Yue Li and David Buckeridge
June 30, 2020 at 11:00AM EDT
As the COVID-19 pandemic continues to unfold, understanding the global impact of non-pharmacological interventions (NPI) is important for formulating effective intervention strategies, particularly as many countries prepare for future waves. We used a machine learning approach to distill latent topics related to NPI from large-scale international news media. We hypothesize that these topics are informative about the timing and nature of implemented NPI, dependent on the source of the information (e.g., local news versus official government announcements) and the target countries. Given a set of latent topics associated with NPI (e.g., self-quarantine, social distancing, online education, etc), we assume that countries and media sources have different prior distributions over these topics, which are sampled to generate the news articles. To model the source-specific topic priors, we developed a semi-supervised, multi-source, dynamic, embedded topic model. Our model is able to simultaneously infer latent topics and learn a linear classifier to predict NPI labels using the topic mixtures as input for each news article. To learn these models, we developed an efficient end-to-end amortized variational inference algorithm. We applied our models to news data collected and labelled by the World Health Organization (WHO) and the Global Public Health Intelligence Network (GPHIN). Through comprehensive experiments, we observed superior topic quality and intervention prediction accuracy, compared to the baseline embedded topic models, which ignore information on media source and intervention labels. The inferred latent topics reveal distinct policies and media framing in different countries and media sources, and also characterize reaction COVID-19 and NPI in a semantically meaningful manner.