AaRSs (aminoacyl-tRNA synthetases) group into two ten-member classes throughout evolution, with unique active site architectures defining each class. Most are monomers or homodimers but, for no apparent reason, many bacterial GlyRSs are heterotetramers consisting of two catalytic α-subunits and two tRNA-binding β-subunits. The heterotetrameric GlyRS from Escherichia coli (EcGlyRS) was historically tested whether its α- and β-polypeptides, which are encoded by a single mRNA with a gap of three in-frame codons, are replaceable by a single chain. Here, an unprecedented X-shaped structure of EcGlyRS shows wide separation of the abutting chain termini seen in the coding sequences, suggesting strong pressure to avoid a single polypeptide format. The structure of the five-domain β-subunit is unique across all aaRSs in current databases, and structural analyses suggest these domains play different functions on α-subunit binding, ATP coordination and tRNA recognition. Moreover, the X-shaped architecture of EcGlyRS largely fits with a model for how two classes of tRNA synthetases arose, according to whether enzymes from opposite classes can simultaneously co-dock onto separate faces of the same tRNA acceptor stem. While heterotetrameric GlyRS remains the last structurally uncharacterized member of aaRSs, our study contributes to a better understanding of this ancient and essential enzyme family.

No abstract

To discover new agents active against methicillin-resistant Staphylococcus aureus (MRSA), in silico models derived from 5451 cell-based anti-MRSA assay data were developed using four machine learning methods, including naïve Bayesian, support vector machine (SVM), recursive partitioning (RP), and k-nearest neighbors (kNN). A total of 876 models have been constructed based on physicochemical descriptors and fingerprints. The overall predictive accuracies of the best models exceeded 80% for both training and test sets. The best model was employed for the virtual screening of anti-MRSA compounds, which were then validated by a cell-based assay using the broth microdilution method with three types of highly resistant MRSA strains (ST239, ST5, and 252). A total of 12 new anti-MRSA agents were confirmed, which had MIC values ranging from 4 to 64 mg/L. This work proves the capacity of combined multiple ligand-based approaches for the discovery of new agents active against MRSA with cell-based assays. We think this work may inspire other lead identification processes when cell-based assay data are available.

Indoleamine 2,3-dioxygenase (IDO), an immune checkpoint, is a promising target for cancer immunotherapy. However, current IDO inhibitors are not approved for clinical use yet; therefore, new IDO inhibitors are still demanded. To identify new IDO inhibitors, we have built naive Bayesian (NB) and recursive partitioning (RP) models from a library of known IDO inhibitors derived from recent publications. Thirteen molecular fingerprints were used as descriptors for the models to predict IDO inhibitors. An in-house compound library was virtually screened using the best machine learning model, which resulted in 50 hits for further enzyme-based IDO inhibitory assays. Consequently, we identified three new IDO inhibitors with IC values of 1.30, 4.10, and 4.68 μM. These active compounds also showed IDO inhibitory activities in cell-based assays. The compounds belong to the tanshinone family, a typical scaffold family derived from Danshen (a Chinese herb), the dried root of , which has been widely used in China, Japan, the United States, and other European countries for the treatment of cardiovascular and cerebrovascular diseases. Thus, we discovered a new use for Danshen using machine learning methods. Surface plasmon resonance (SPR) experiments proved that the inhibitors interacted with the IDO target. Molecular dynamic simulations demonstrated the binding modes of the IDO inhibitors.

The polyketide natural product reveromycin A (RM-A) exhibits antifungal, anticancer, anti-bone metastasis, anti-periodontitis and anti-osteoporosis activities by selectively inhibiting eukaryotic cytoplasmic isoleucyl-tRNA synthetase (IleRS). Herein, a co-crystal structure suggests that the RM-A molecule occupies the substrate tRNAIle binding site of Saccharomyces cerevisiae IleRS (ScIleRS), by partially mimicking the binding of tRNAIle. RM-A binding is facilitated by the copurified intermediate product isoleucyl-adenylate (Ile-AMP). The binding assays confirm that RM-A competes with tRNAIle while binding synergistically with l-isoleucine or intermediate analogue Ile-AMS to the aminoacylation pocket of ScIleRS. This study highlights that the vast tRNA binding site of the Rossmann-fold catalytic domain of class I aminoacyl-tRNA synthetases could be targeted by a small molecule. This finding will inform future rational drug design.