A refined picture of the native amine dehydrogenase family revealed by extensive biodiversity screening.

Native amine dehydrogenases offer sustainable access to chiral amines, so the search for scaffolds capable of converting more diverse carbonyl compounds is required to reach the full potential of this alternative to conventional synthetic reductive aminations. Here we report a multidisciplinary strategy combining bioinformatics, chemoinformatics and biocatalysis to extensively screen billions of sequences in silico and to efficiently find native amine dehydrogenases features using computational approaches. In this way, we achieve a comprehensive overview of the initial native amine dehydrogenase family, extending it from 2,011 to 17,959 sequences, and identify native amine dehydrogenases with non-reported substrate spectra, including hindered carbonyls and ethyl ketones, and accepting methylamine and cyclopropylamine as amine donor.

Detection and Characterization of VIM-52, a New Variant of VIM-1 from a Klebsiella pneumoniae Clinical Isolate.

Over the last two decades, antimicrobial resistance has become a global health problem. In Gram-negative bacteria, metallo-β-lactamases (MBLs), which inactivate virtually all β-lactams, increasingly contribute to this phenomenon. The aim of this study is to characterize VIM-52, a His224Arg variant of VIM-1, identified in a Klebsiella pneumoniae clinical isolate.

Azetidinimines as a novel series of non-covalent broad-spectrum inhibitors of β-lactamases with submicromolar activities against carbapenemases KPC-2 (class A), NDM-1 (class B) and OXA-48 (class D).

The occurrence of resistances in Gram negative bacteria is steadily increasing to reach extremely worrying levels and one of the main causes of resistance is the massive spread of very efficient β-lactamases which render most β-lactam antibiotics useless. Herein, we report the development of a series of imino-analogues of β-lactams (namely azetidinimines) as efficient non-covalent inhibitors of β-lactamases. Despite the structural and mechanistic differences between serine-β-lactamases KPC-2 and OXA-48 and metallo-β-lactamase NDM-1, all three enzymes can be inhibited at a submicromolar level by compound 7dfm, which can also repotentiate imipenem against a resistant strain of Escherichia coli expressing NDM-1.

Performance evaluation of molecular docking and free energy calculations protocols using the D3R Grand Challenge 4 dataset.

Using the D3R Grand Challenge 4 dataset containing Beta-secretase 1 (BACE) and Cathepsin S (CatS) inhibitors, we have evaluated the performance of our in-house docking workflow that involves in the first step the selection of the most suitable docking software for the system of interest based on structural and functional information available in public databases, followed by the docking of the dataset to predict the binding modes and ranking of ligands. The macrocyclic nature of the BACE ligands brought additional challenges, which were dealt with by a careful preparation of the three-dimensional input structures for ligands. This provided top-performing predictions for BACE, in contrast with CatS, where the predictions in the absence of guiding constraints provided poor results.

Genetic, Biochemical, and Structural Characterization of CMY-136 β-Lactamase, a Peculiar CMY-2 Variant.

With the widespread use and abuse of antibiotics for the past decades, antimicrobial resistance poses a serious threat to public health nowadays. β-Lactams are the most used antibiotics, and β-lactamases are the most widespread resistance mechanism. Class C β-lactamases, also known as cephalosporinases, usually do not hydrolyze the latest and most potent β-lactams, expanded spectrum cephalosporins and carbapenems.

Blinded evaluation of cathepsin S inhibitors from the D3RGC3 dataset using molecular docking and free energy calculations.

During the last few years, we have developed a docking protocol involving two steps: (i) the choice of the most appropriate docking software and parameters for the system of interest using structural and functional information available in public databases (PDB, ChEMBL, PubChem Assay, BindingDB, etc.); (ii) the docking of ligand dataset to provide a prediction for the binding modes and ranking of ligands. We applied this protocol to the D3R Grand Challenge 3 dataset containing cathepsin S (CatS) inhibitors.

Blinded evaluation of farnesoid X receptor (FXR) ligands binding using molecular docking and free energy calculations.

Our participation to the D3R Grand Challenge 2 involved a protocol in two steps, with an initial analysis of the available structural data from the PDB allowing the selection of the most appropriate combination of docking software and scoring function. Subsequent docking calculations showed that the pose prediction can be carried out with a certain precision, but this is dependent on the specific nature of the ligands. The correct ranking of docking poses is still a problem and cannot be successful in the absence of good pose predictions.