Multidrug resistance among Gram-negative bacteria is a major global public health threat. Metallo-β-lactamases (MBLs) target the most widely used antibiotic class, the β-lactams, including the most recent generation of carbapenems. Interspecies spread renders these enzymes a serious clinical threat, and there are no clinically available inhibitors. We present the crystal structures of IMP-13, a structurally uncharacterized MBL from the Gram-negative bacterium Pseudomonas aeruginosa found in clinical outbreaks globally, and characterize the binding using solution nuclear magnetic resonance spectroscopy and molecular dynamics simulations. The crystal structures of apo IMP-13 and IMP-13 bound to four clinically relevant carbapenem antibiotics (doripenem, ertapenem, imipenem, and meropenem) are presented. Active-site plasticity and the active-site loop, where a tryptophan residue stabilizes the antibiotic core scaffold, are essential to the substrate-binding mechanism. The conserved carbapenem scaffold plays the most significant role in IMP-13 binding, explaining the broad substrate specificity. The observed plasticity and substrate-locking mechanism provide opportunities for rational drug design of novel metallo-β-lactamase inhibitors, essential in the fight against antibiotic resistance.

Trypanosoma protists are pathogens leading to a spectrum of devastating infectious diseases. The range of available chemotherapeutics against Trypanosoma is limited, and the existing therapies are partially ineffective and cause serious adverse effects. Formation of the PEX14−PEX5 complex is essential for protein import into the parasites' glycosomes. This transport is critical for parasite metabolism and failure leads to mislocalization of glycosomal enzymes, with fatal consequences for the parasite. Hence, inhibiting the PEX14−PEX5 protein−protein interaction (PPI) is an attractive way to affect multiple metabolic pathways. Herein, we have used structure-guided computational screening and optimization to develop the first line of compounds that inhibit PEX14−PEX5 PPI. The optimization was driven by several X-ray structures, NMR binding data, and molecular dynamics simulations. Importantly, the developed compounds show significant cellular activity against Trypanosoma, including the human pathogen Trypanosoma brucei gambiense and Trypanosoma cruzi parasites.

The replication complex (RC) of SARS-CoV-2 was recently shown to be one of the fastest RNA-dependent RNA polymerases of any known coronavirus. With this rapid elongation, the RC is more prone to incorporate mismatches during elongation, resulting in a highly variable genomic sequence. Such mutations render the design of viral protein targets difficult, as drugs optimized for a given viral protein sequence can quickly become inefficient as the genomic sequence evolves. Here, we use biochemical experiments to characterize features of RNA template recognition and elongation fidelity of the SARS-CoV-2 RdRp, and the role of the exonuclease, nsp14. Our study highlights the 2′OH group of the RNA ribose as a critical component for RdRp template recognition and elongation. We show that RdRp fidelity is reduced in the presence of the 3′ deoxy-terminator nucleotide 3′dATP, which promotes the incorporation of mismatched nucleotides (leading to U:C, U:G, U:U, C:U, and A:C base pairs). We find that the nsp10–nsp14 heterodimer is unable to degrade RNA products lacking free 2′OH or 3′OH ribose groups. Our results suggest the potential use of 3′ deoxy-terminator nucleotides in RNA-derived oligonucleotide inhibitors as antivirals against SARS-CoV-2.

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Molecular docking is a computational method employed to estimate the binding between a small ligand (the drug candidate) and a protein receptor that has become a standard part of workflow in drug discovery. Generally, when the binding site is known and a molecule is similar to known ligands, the most popular docking methods are rather accurate in the prediction of the geometry. Unfortunately, when the binding site is unknown, the blind docking analysis requires large computational resources and the results are often not accurate. Here we present Yada, a new tool for molecular docking that is capable to distribute efficiently calculations onto general purposes computer grid and that combines biological and structural information of the receptor. Yada is available for Windows and Linux and it is free to download at www.yada.unisa.it .

We show that a water envelope network plays a critical role in protein–protein interactions (PPI).

Parametric and non-parametric machine learning potentials have emerged recently as a way to improve the accuracy of bio-molecular simulations. Here, we present NNP/MM, an hybrid method integrating neural network potentials (NNPs) and molecular mechanics (MM). It allows to simulate a part of molecular system with NNP, while

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