Large library docking for novel SARS-CoV-2 main protease non-covalent and covalent inhibitors (preprint)

Antiviral therapeutics to treat SARS-CoV-2 are much desired for the on-going pandemic. A well-precedented viral enzyme is the main protease (MPro), which is now targeted by an approved drug and by several investigational drugs. With the inevitable liabilities of these new drugs, and facing viral resistance, there remains a call for new chemical scaffolds against MPro. We virtually docked 1.2 billion non-covalent and a new library of 6.5 million electrophilic molecules against the enzyme structure. From these, 29 non-covalent and 11 covalent inhibitors were identified in 37 series, the most potent having an IC 50 of 29 μM and 20 μM, respectively. Several series were optimized, resulting in inhibitors active in the low micromolar range. Subsequent crystallography confirmed the docking predicted binding modes and may template further optimization. Together, these compounds reveal new chemotypes to aid in further discovery of MPro inhibitors for SARS-CoV-2 and other future coronaviruses.

Identification of a conserved drug binding pocket in TMEM16 proteins (preprint)

The TMEM16 family of calcium-activated membrane proteins includes ten mammalian paralogs (TMEM16A-K) playing distinct physiological roles with some implicated in cancer and airway diseases. Their modulators with therapeutic potential include 1PBC, a potent inhibitor with anti-tumoral properties, and the FDA-approved drug niclosamide that targets TMEM16F to inhibit syncytia formation induced by SARS-CoV-2 infection. Here, we report cryo-EM structures of TMEM16F associated with 1PBC and niclosamide, revealing that both molecules bind the same drug binding pocket. We functionally and computationally validate this binding pocket in TMEM16A as well as TMEM16F, thereby showing that drug modulation also involves residues that are not conserved between TMEM16A and TMEM16F. This study establishes a much-needed structural framework for the development of more potent and more specific drug molecules targeting TMEM16 proteins.

A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.