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CELLULOSIC biomass is recycled by a variety of microorganisms occupying different habitats 1 . Studies of their cellulase systems have included the purification of enzyme components, the determination of their enzymological properties 2 and the cloning and characterization of their structural genes 3 . Sequence analysis of more than 70 cellulases permits grouping into seven families corresponding to distinct structural types 4,5 . The three-dimensional structure of the catalytic core of cellobiohydrolase CBHII from the fungus Trichoderma reesei has been reported 6 . Here we show that endoglucanase CelD from Clostridium thermocellum , which is representative of a different family of cellulose-degrading enzymes consisting of at least 11 bacterial, fungal and plant endoglucanases 5,7 , has a globular structure, with an amino-terminal immunoglobulin-like domain tightly packed against a larger catalytic domain. The latter shows a novel protein fold, shaped like an α-barrel of 12 helices connected by loops that form the active site. The structure of a complex CelD with a substrate analogue suggests a mechanism for substrate hydrolysis.

Predictions of the structures of the antigen-binding domains of an antibody, recorded before its experimental structure determination and tested subsequently, were based on comparative analysis of known antibody structures or on conformational energy calculations. The framework, the relative positions of the hypervariable regions, and the folds of four of the hypervariable loops were predicted correctly. This portion includes all residues in contact with the antigen, in this case hen egg white lysozyme, implying that the main chain conformation of the antibody combining site does not change upon ligation. The conformations of three residues in each of the other two hypervariable loops are different in the predicted models and the experimental structure.

The COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of a community effort, the “Billion molecules against Covid-19 challenge”, to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 potentially active molecules, which were subsequently ranked to find ‘consensus compounds’. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (Nsp12 domain), and (alpha) spike protein S. Overall, 27 potential inhibitors were experimentally confirmed by binding-, cleavage-, and/or viral suppression assays and are presented here. All results are freely available and can be taken further downstream without IP restrictions. Overall, we show the effectiveness of computational techniques, community efforts, and communication across research fields (i.e., protein expression and crystallography, in silico modeling, synthesis and biological assays) to accelerate the early phases of drug discovery.

Deep-coverage whole genome sequencing at the population level is now feasible and offers potential advantages for locus discovery, particularly in the analysis rare mutations in non-coding regions. Here, we performed whole genome sequencing in 16,324 participants from four ancestries at mean depth >29X and analyzed correlations of genotypes with four quantitative traits - plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, and triglycerides. We conducted a discovery analysis including common or rare variants in coding as well as non-coding regions and developed a framework to interpret genome sequence for dyslipidemia risk. Common variant association yielded loci previously described with the exception of a few variants not captured earlier by arrays or imputation. In coding sequence, rare variant association yielded known Mendelian dyslipidemia genes and, in non-coding sequence, we detected no rare variant association signals after application of four approaches to aggregate variants in non-coding regions. We developed a new, genome-wide polygenic score for LDL-C and observed that a high polygenic score conferred similar effect size to a monogenic mutation (~30 mg/dl higher LDL-C for each); however, among those with extremely high LDL-C, a high polygenic score was considerably more prevalent than a monogenic mutation (23% versus 2% of participants, respectively).