Native amine dehydrogenases can catalyze the direct reduction of carbonyl compounds to alcohols in the absence of ammonia

Native amine dehydrogenases (nat-AmDHs) catalyze the (S)-stereoselective reductive amination of various ketones and aldehydes in the presence of high concentrations of ammonia. Based on the structure of CfusAmDH from Cystobacter fuscus complexed with Nicotinamide adenine dinucleotide phosphate (NADP+) and cyclohexylamine, we previously hypothesized a mechanism involving the attack at the electrophilic carbon of the carbonyl by ammonia followed by delivery of the hydride from the reduced nicotinamide cofactor on the re-face of the prochiral ketone. The direct reduction of carbonyl substrates into the corresponding alcohols requires a similar active site architecture and was previously reported as a minor side reaction of some native amine dehydrogenases and variants. Here we describe the ketoreductase (KRED) activity of a set of native amine dehydrogenases and variants, which proved to be significant in the absence of ammonia in the reaction medium but negligible in its presence. Conducting this study on a large set of substrates revealed the heterogeneity of this secondary ketoreductase activity, which was dependent upon the enzyme/substrate pairs considered. In silico docking experiments permitted the identification of some relationships between ketoreductase activity and the structural features of the enzymes. Kinetic studies of MsmeAmDH highlighted the superior performance of this native amine dehydrogenases as a ketoreductase but also its very low activity towards the reverse reaction of alcohol oxidation

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

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Towards in silico prediction of beta-lactamase-mediated antibiotic resistance using free energy calculations

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

International audienceWith 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. However, the recent emergence of extended-spectrum AmpC cephalosporinases, their resistance to inhibition by classic β-lactamase inhibitors, and the fact that they can contribute to carbapenem resistance when paired with impermeability mechanisms, means that these enzymes may still prove worrisome in the future. Here we report and characterize the CMY-136 β-lactamase, a Y221H point mutant derivative of CMY-2. CMY-136 confers an increased level of resistance to ticarcillin, cefuroxime, cefotaxime, and ceftolozane/tazobactam. It is also capable of hydrolyzing ticarcillin and cloxacillin, which act as inhibitors of CMY-2. X-ray crystallography and modeling experiments suggest that the hydrolytic profile alterations seem to be the result of an increased flexibility and altered conformation of the Ω-loop, caused by the Y221H mutation

An integrative bioinformatics approach to explore the biodiversity of enzyme families

National audienceIntroductionMetagenomics data represent a largely untapped and continuously growing pool of new sequences coming from various worldwide biotopes (soil, human gut, oceans…). This biodiversity, up to several billions of sequences, may be exploited to answer multiple scientific challenges. For instance, biocatalysis (i.e. catalysis with enzymes) needs new relevant biocatalysts with various activities to take up the energy transition challenge and replace some polluting synthesis steps. Hence, bioinformatics approaches to efficiently identify the targeted enzymatic activity from large metagenomic resources are needed.Through the MODAMDH project (ANR JCJC), we focused on one of the key biocatalysts named amine dehydrogenases (AmDHs) which enable the access to amines that are important entities in the chemical industry [1-2]. To do so, we applied a sequence- and structure-based bioinformatics approach to widen the landscape of protein sequences catalyzing reductive amination by searching for remote homologs and active site analogs. In the context of the ALADIN project (ESR / EquipEx+), we want to generalize this approach and develop workflows that could be applied to any enzyme family. MethodsPublicly available and in-house metagenomics databases (>2.5 billion protein sequences) were screened using HMMER software and the SUPERFAMILY database. Structural modeling and active site classification were performed by the ASMC software [3]. Remote homologs were recovered by HMM-HMM comparisons with HHblits software. Active site analogs were searched by screening catalophores (i.e. minimal active site topologies) using the YASARA software. Phylogeny of protein families was done with IQ-TREE program.ResultsThe AmDH family was first enriched with new metagenomic sequences before being classified into subfamilies using an active site classification and a phylogeny. Besides, we generated a pool of NAD(P)-binding protein sequences from which we found, using HMM-HMM comparisons, new AmDH distant homologs. In contrast, no active site analog has yet been found for the AmDH family.Through the ALADIN project, we will extend this strategy to other enzymatic activities by designing generic workflows and applying them first to explore the diversity of the aforementioned NAD(P)-binding protein families.AcknowledgementsThis study was supported by the contracts from the MODAMDH (ANR-19-CE07-0007, ANR JCJC) and ALADIN (IA-21-ESRE-0021, ESR / EquipEx+) projects

An integrative bioinformatics approach to explore the biodiversity of enzyme families

National audienceIntroductionMetagenomics data represent a largely untapped and continuously growing pool of new sequences coming from various worldwide biotopes (soil, human gut, oceans…). This biodiversity, up to several billions of sequences, may be exploited to answer multiple scientific challenges. For instance, biocatalysis (i.e. catalysis with enzymes) needs new relevant biocatalysts with various activities to take up the energy transition challenge and replace some polluting synthesis steps. Hence, bioinformatics approaches to efficiently identify the targeted enzymatic activity from large metagenomic resources are needed.Through the MODAMDH project (ANR JCJC), we focused on one of the key biocatalysts named amine dehydrogenases (AmDHs) which enable the access to amines that are important entities in the chemical industry [1-2]. To do so, we applied a sequence- and structure-based bioinformatics approach to widen the landscape of protein sequences catalyzing reductive amination by searching for remote homologs and active site analogs. In the context of the ALADIN project (ESR / EquipEx+), we want to generalize this approach and develop workflows that could be applied to any enzyme family. MethodsPublicly available and in-house metagenomics databases (>2.5 billion protein sequences) were screened using HMMER software and the SUPERFAMILY database. Structural modeling and active site classification were performed by the ASMC software [3]. Remote homologs were recovered by HMM-HMM comparisons with HHblits software. Active site analogs were searched by screening catalophores (i.e. minimal active site topologies) using the YASARA software. Phylogeny of protein families was done with IQ-TREE program.ResultsThe AmDH family was first enriched with new metagenomic sequences before being classified into subfamilies using an active site classification and a phylogeny. Besides, we generated a pool of NAD(P)-binding protein sequences from which we found, using HMM-HMM comparisons, new AmDH distant homologs. In contrast, no active site analog has yet been found for the AmDH family.Through the ALADIN project, we will extend this strategy to other enzymatic activities by designing generic workflows and applying them first to explore the diversity of the aforementioned NAD(P)-binding protein families.AcknowledgementsThis study was supported by the contracts from the MODAMDH (ANR-19-CE07-0007, ANR JCJC) and ALADIN (IA-21-ESRE-0021, ESR / EquipEx+) projects

In silico prediction of β-lactamase hydrolysis efficiency: Finding the right balance between kinetic and thermodynamic terms

International audienceDuring recent decades, an alarming worldwide spread and diversification of β-lactamases has been obsd. in Gram-neg. species, conferring resistance to β-lactam antibiotics. In this context, the prediction of future β-lactamase mutants becomes an essential issue and we have developed a protocol that allows the evaluation of the energetic cost (thermodn.) assocd. with a mutation in the active site of a β-lactamase, in the presence of the β-lactam substrate. The catalytic efficiency for the β-lactamase-mediated hydrolysis of β-lactam antibiotics is represented by the ratio of a kinetic term (kcat) and a thermodn. term (Km). Here we present an approach based on quantum calcns. and mol. dynamics simulations allowing to est. the kcat term by evaluating β-lactamases from different classes, in the presence of several β-lactam substrates. Therefore, we are now able to predict in silico the overall catalytic efficiency for a large panel of β-lactamases. The precision of this prediction, which depends in turn on the precision for predicting the individual terms kcat and Km, will be discussed and compared with the precision of exptl. values. These results will ultimately provide essential information for the fight against resistance to β-lactam antibiotics

Expanding the substrate scope of Amine Dehydrogenases by protein engineering

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

International audienceUsing 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. These results highlight the importance of previous structural knowledge that is needed for correct predictions on some challenging targets. After the end of the challenge, we also carried out free energy calculations (i.e. in a non-blinded manner) for CatS using the pmx software and several force fields (AMBER, Charmm). Using knowledge-based starting pose construction allowed reaching remarkable accuracy for the CatS free energy estimates. Interestingly, we show that the use of a consensus result, by averaging the results from different force fields, increases the prediction accuracy