Structural Genomics Consortium and Department of Pharmacology & Toxicology, Faculty of Medicine, St George Campus, University of Toronto, Toronto, Canada
Prof. Matthieu Schapira
Structural Genomics Consortium and Department of Pharmacology & Toxicology, Faculty of Medicine, St George Campus, University of Toronto, Toronto, Canada
We compiled a list of 11 016 unique structures of small-molecule ligands bound to proteins representing 750 873 protein–ligand atomic interactions, and analyzed the frequency, geometry and the impact of each interaction type. The most frequent ligand–protein atom pairs can be clustered into seven interaction types.
There is currently a lack of effective drugs to treat people infected with SARS-CoV-2, the cause of the global COVID-19 pandemic. The SARS-CoV-2 Non-structural protein 13 (NSP13) has been identified as a target for anti-virals due to its high sequence conservation and essential role in viral replication. Structural analysis reveals two “druggable” pockets on NSP13 that are among the most conserved sites in the entire SARS-CoV-2 proteome. Here we present crystal structures of SARS-CoV-2 NSP13 solved in the APO form and in the presence of both phosphate and a non-hydrolysable ATP analog. Comparisons of these structures reveal details of conformational changes that provide insights into the helicase mechanism and possible modes of inhibition. To identify starting points for drug development we have performed a crystallographic fragment screen against NSP13. The screen reveals 65 fragment hits across 52 datasets opening the way to structure guided development of novel antiviral agents.
Few data have been published on the associations of ferritin with trunk and leg fat depots. We aimed to investigate these associations in a Chinese population. Trunk fat mass and leg fat mass were determined in a cross-sectional sample of 1,150 Chinese (479 men and 671 women) aged 50–70 years by dual-energy X-ray absorptiometry scan. Fasting plasma ferritin was measured. Plasma ferritin was positively correlated with waist circumference, waist-to-hip ratio, total body fat and trunk fat mass, but inversely correlated with leg fat mass in men (r = 0.16, 0.26, 0.19, 0.22 and −0.12, respectively, all P<0.05) and women (r = 0.16, 0.16, 0.08, 0.17 and −0.12, respectively, all P<0.05). Multivariate regression analysis showed that ferritin levels increased with larger trunk fat mass (β = 0.33 ± 0.08 for men and β = 0.21 ± 0.05 for women, both P<0.001) while decreased with larger leg fat mass (β = −0.12 ± 0.09, P = 0.15 for men; and β = −0.14 ± 0.05, P = 0.005 for women). Moreover, plasma ferritin levels decreased with increasing tertile of leg fat mass among each tertile of trunk fat mass. This is the first study to report the opposite associations of trunk and leg fat depots with plasma ferritin levels.