RE2DC: a robust and efficient 2D classifier with visualization for processing massive and heterogeneous cryo-EM data (preprint)

Despite the fact that single particle cryo-EM has become a powerful method of structural biology, processing cryo-EM images are challenging due to the low SNR, high-dimension and un-label nature of the data. Selecting the best subset of particle images relies on 2D classification—a process that involves iterative image alignment and clustering. This process, however, represents a major time sink, particularly when the data is massive or overly heterogeneous. Popular approaches to this process often trade its robustness for efficiency. Here, we introduced a new unsupervised 2D classification method termed RE2DC. It is built upon a highly efficient variant of γ -SUP, a robust statistical cryo-EM clustering algorithm resistant to the attractor effect. To develop this efficient variant, we employed a tree-based approximation to reduce the computation complexity from O ( N 2 ) to O ( N ), with N as the number of images. In addition, we exploited t-SNE visualization to unveil the process of 2D classification. Our tests of RE2DC using various datasets demonstrate it is both robust and efficient, with the potential to reveal subtle structural intermediates. Using RE2DC to curate a dataset of sub-millions of COVID-19 spike particles picked from 3,511 movies only takes 8 hours, suggesting its capability of accelerating cryo-EM structural determination. Currently, RE2DC is available with both CPU and GPU versions, where the implementation only requires modest hardware resources.

Simeprevir suppresses SARS-CoV-2 replication and synergizes with remdesivir (preprint)

The outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a global threat to human health. Using a multidisciplinary approach, we identified and validated the hepatitis C virus (HCV) protease inhibitor simeprevir as an especially promising repurposable drug for treating COVID-19. Simeprevir potently reduces SARS-CoV-2 viral load by multiple orders of magnitude and synergizes with remdesivir in vitro. Mechanistically, we showed that simeprevir inhibits the main protease (Mpro) and unexpectedly the RNA-dependent RNA polymerase (RdRp). Our results thus reveal the viral protein targets of simeprevir, and provide preclinical rationale for the combination of simeprevir and remdesivir for the pharmacological management of COVID-19 patients. One Sentence SummaryDiscovery of simeprevir as a potent suppressor of SARS-CoV-2 viral replication that synergizes with remdesivir.