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The 5'-hydroxymethyl metabolite of the penicillin based antibiotic flucloxacillin (FLX) is considered to be involved in bile duct damage occurring in a small number of patients. Because 5'-hydroxymethyl FLX is difficult to obtain by organic synthesis, biosynthesis using highly active and regioselective biocatalysts would be an alternative approach. By screening an in-house library of Cytochrome P450 (CYP) BM3 mutants, mutant M11 L437E was identified as a regioselective enzyme with relatively high activity in production of 5'-hydroxymethyl FLX as was confirmed by mass spectrometry and NMR. In contrast, incubation of M11 L437E and other mutants with oxacillin (OX, which differs from FLX by a lack of aromatic halogens) resulted in formation of two metabolites. In addition to 5'-hydroxymethyl OX we identified a product resulting from aromatic hydroxylation. In silico studies of both FLX and OX with three CYP BM3 mutants revealed substrate binding poses allowing for 5'-methyl hydroxylation, as well as binding poses with the aromatic moiety in the vicinity of the heme iron for which the corresponding product of aromatic hydroxylation was not observed for FLX. Supported by the (differences in) experimentally determined ratios of product formation for OX hydroxylation by M11 and its L437A variant and M11 L437E, Molecular Dynamics simulations suggest that the preference of mutant M11 L437E to bind FLX in its catalytically active pose over the other binding orientation contributes to its biocatalytic activity, highlighting the benefit of studying effects of active-site mutations on possible alternative enzyme-substrate binding poses in protein engineering., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Background: Computational methods to predict binding affinities of small ligands toward relevant biological (off-)targets are helpful in prioritizing the screening and synthesis of new drug candidates, thereby speeding up the drug discovery process. However, use of ligand-based approaches can lead to erroneous predictions when structural and dynamic features of the target substantially affect ligand binding. Free energy methods for affinity computation can include steric and electrostatic protein-ligand interactions, solvent effects, and thermal fluctuations, but often they are computationally demanding and require a high level of supervision. As a result their application is typically limited to the screening of small sets of compounds by experts in molecular modeling., Results: We have developed eTOX ALLIES, an open source framework that allows the automated prediction of ligand-binding free energies requiring the ligand structure as only input. eTOX ALLIES is based on the linear interaction energy approach, an efficient end-point free energy method derived from Free Energy Perturbation theory. Upon submission of a ligand or dataset of compounds, the tool performs the multiple steps required for binding free-energy prediction (docking, ligand topology creation, molecular dynamics simulations, data analysis), making use of external open source software where necessary. Moreover, functionalities are also available to enable and assist the creation and calibration of new models. In addition, a web graphical user interface has been developed to allow use of free-energy based models to users that are not an expert in molecular modeling., Conclusions: Because of the user-friendliness, efficiency and free-software licensing, eTOX ALLIES represents a novel extension of the toolbox for computational chemists, pharmaceutical scientists and toxicologists, who are interested in fast affinity predictions of small molecules toward biological (off-)targets for which protein flexibility, solvent and binding site interactions directly affect the strength of ligand-protein binding.

Cytochrome P450 aromatase (CYP19A1) plays a key role in the development of estrogen dependent breast cancer, and aromatase inhibitors have been at the front line of treatment for the past three decades. The development of potent, selective and safer inhibitors is ongoing with in silico screening methods playing a more prominent role in the search for promising lead compounds in bioactivity-relevant chemical space. Here we present a set of comprehensive binding affinity prediction models for CYP19A1 using our automated Linear Interaction Energy (LIE) based workflow on a set of 132 putative and structurally diverse aromatase inhibitors obtained from a typical industrial screening study. We extended the workflow with machine learning methods to automatically cluster training and test compounds in order to maximize the number of explained compounds in one or more predictive LIE models. The method uses protein-ligand interaction profiles obtained from Molecular Dynamics (MD) trajectories to help model search and define the applicability domain of the resolved models. Our method was successful in accounting for 86% of the data set in 3 robust models that show high correlation between calculated and observed values for ligand-binding free energies (RMSE < 2.5 kJ mol -1 ), with good cross-validation statistics.

The free-energy surface (FES) of protein-ligand binding contains information useful for drug design. Here we show how to exploit a free-energy minimum of a protein-ligand complex identified by metadynamics simulations to design a new EphA2 antagonist with improved inhibitory potency., (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Recently an iterative method was proposed to enhance the accuracy and efficiency of ligand-protein binding affinity prediction through linear interaction energy (LIE) theory. For ligand binding to flexible Cytochrome P450s (CYPs), this method was shown to decrease the root-mean-square error and standard deviation of error prediction by combining interaction energies of simulations starting from different conformations. Thereby, different parts of protein-ligand conformational space are sampled in parallel simulations. The iterative LIE framework relies on the assumption that separate simulations explore different local parts of phase space, and do not show transitions to other parts of configurational space that are already covered in parallel simulations. In this work, a method is proposed to (automatically) detect such transitions during the simulations that are performed to construct LIE models and to predict binding affinities. Using noise-canceling techniques and splines to fit time series of the raw data for the interaction energies, transitions during simulation between different parts of phase space are identified. Boolean selection criteria are then applied to determine which parts of the interaction energy trajectories are to be used as input for the LIE calculations. Here we show that this filtering approach benefits the predictive quality of our previous CYP 2D6-aryloxypropanolamine LIE model. In addition, an analysis is performed of the gain in computational efficiency that can be obtained from monitoring simulations using the proposed filtering method and by prematurely terminating simulations accordingly.

Prediction of human Cytochrome P450 (CYP) binding affinities of small ligands, i.e., substrates and inhibitors, represents an important task for predicting drug-drug interactions. A quantitative assessment of the ligand binding affinity towards different CYPs can provide an estimate of inhibitory activity or an indication of isoforms prone to interact with the substrate of inhibitors. However, the accuracy of global quantitative models for CYP substrate binding or inhibition based on traditional molecular descriptors can be limited, because of the lack of information on the structure and flexibility of the catalytic site of CYPs. Here we describe the application of a method that combines protein-ligand docking, Molecular Dynamics (MD) simulations and Linear Interaction Energy (LIE) theory, to allow for quantitative CYP affinity prediction. Using this combined approach, a LIE model for human CYP 1A2 was developed and evaluated, based on a structurally diverse dataset for which the estimated experimental uncertainty was 3.3 kJ mol-1. For the computed CYP 1A2 binding affinities, the model showed a root mean square error (RMSE) of 4.1 kJ mol-1 and a standard error in prediction (SDEP) in cross-validation of 4.3 kJ mol-1. A novel approach that includes information on both structural ligand description and protein-ligand interaction was developed for estimating the reliability of predictions, and was able to identify compounds from an external test set with a SDEP for the predicted affinities of 4.6 kJ mol-1 (corresponding to 0.8 pKi units).

Early prediction of safety issues in drug development is at the same time highly desirable and highly challenging. Recent advances emphasize the importance of understanding the whole chain of causal events leading to observable toxic outcomes. Here we describe an integrative modeling strategy based on these ideas that guided the design of eTOXsys, the prediction system used by the eTOX project. Essentially, eTOXsys consists of a central server that marshals requests to a collection of independent prediction models and offers a single user interface to the whole system. Every of such model lives in a self-contained virtual machine easy to maintain and install. All models produce toxicity-relevant predictions on their own but the results of some can be further integrated and upgrade its scale, yielding in vivo toxicity predictions. Technical aspects related with model implementation, maintenance and documentation are also discussed here. Finally, the kind of models currently implemented in eTOXsys is illustrated presenting three example models making use of diverse methodology (3D-QSAR and decision trees, Molecular Dynamics simulations and Linear Interaction Energy theory, and fingerprint-based QSAR)., (© 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Irreversible epidermal growth factor receptor (EGFR) inhibitors can circumvent resistance to first-generation ATP-competitive inhibitors in the treatment of nonsmall-cell lung cancer. They covalently bind a noncatalytic cysteine (Cys797) at the surface of EGFR active site by an acrylamide warhead. Herein, we used a hybrid quantum mechanics/molecular mechanics (QM/MM) potential in combination with umbrella sampling in the path-collective variable space to investigate the mechanism of alkylation of Cys797 by the prototypical covalent inhibitor N-(4-anilinoquinazolin-6-yl) acrylamide. Calculations show that Cys797 reacts with the acrylamide group of the inhibitor through a direct addition mechanism, with Asp800 acting as a general base/general acid in distinct steps of the reaction. The obtained reaction free energy is negative (ΔA = -12 kcal/mol) consistent with the spontaneous and irreversible alkylation of Cys797 by N-(4-anilinoquinazolin-6-yl) acrylamide. Our calculations identify desolvation of Cys797 thiolate anion as a key step of the alkylation process, indicating that changes in the intrinsic reactivity of the acrylamide would have only a minor impact on the inhibitor potency.

Cytochrome P450 BM3 mutants are promising biocatalysts for the production of drug metabolites. In the present study, the ability of cytochrome P450 BM3 mutants to produce oxidative metabolites of structurally related NSAIDs meclofenamic acid, mefenamic acid and tolfenamic acid was investigated. A library of engineered P450 BM3 mutants was screened with meclofenamic acid (1) to identify catalytically active and selective mutants. Three mono-hydroxylated metabolites were identified for 1. The hydroxylated products were confirmed by NMR analysis to be 3'-OH-methyl-meclofenamic acid (1a), 5-OH-meclofenamic acid (1b) and 4'-OH-meclofenamic acid (1c) which are human relevant metabolites. P450 BM3 variants containing V87I and V87F mutation showed high selectivity for benzylic and aromatic hydroxylation of 1 respectively. The applicability of these mutants to selectively hydroxylate structurally similar drugs such as mefenamic acid (2) and tolfenamic acid (3) was also investigated. The tested variants showed high total turnover numbers in the order of 4000-6000 and can be used as biocatalysts for preparative scale synthesis. Both 1 and 2 could undergo benzylic and aromatic hydroxylation by the P450 BM3 mutants, whereas 3 was hydroxylated only on aromatic rings. The P450 BM3 variant M11 V87F hydroxylated the aromatic ring at 4' position of all three drugs tested with high regioselectivity. Reference metabolites produced by P450 BM3 mutants allowed the characterisation of activity and regioselectivity of metabolism of all three NSAIDs by thirteen recombinant human P450s. In conclusion, engineered P450 BM3 mutants that are capable of benzylic or aromatic hydroxylation of fenamic acid containing NSAIDs, with high selectivity and turnover numbers have been identified. This shows their potential use as a greener alternative for the generation of drug metabolites., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Carbamate and urea derivatives are important classes of fatty acid amide hydrolase (FAAH) inhibitors that carbamoylate the active-site nucleophile Ser241. In the present work, the reactivation mechanism of carbamoylated FAAH is investigated by means of a quantum mechanics/molecular mechanics (QM/MM) approach. The potential energy surfaces for decarbamoylation of FAAH covalent adducts, derived from the O-aryl carbamate URB597 and from the N-piperazinylurea JNJ1661610, were calculated and compared to that for deacylation of FAAH acylated by the substrate oleamide. Calculations show that a carbamic group bound to Ser241 prevents efficient stabilization of transition states of hydrolysis, leading to large increments in the activation barrier. Moreover, the energy barrier for the piperazine carboxylate was significantly lower than that for the cyclohexyl carbamate derived from URB597. This is consistent with experimental data showing slowly reversible FAAH inhibition for the N-piperazinylurea inhibitor and irreversible inhibition for URB597.

The N-terminal nucleophile (Ntn) hydrolases are a superfamily of enzymes specialized in the hydrolytic cleavage of amide bonds. Even though several members of this family are emerging as innovative drug targets for cancer, inflammation, and pain, the processes through which they catalyze amide hydrolysis remains poorly understood. In particular, the catalytic reactions of cysteine Ntn-hydrolases have never been investigated from a mechanistic point of view. In the present study, we used free energy simulations in the quantum mechanics/molecular mechanics framework to determine the reaction mechanism of amide hydrolysis catalyzed by the prototypical cysteine Ntn-hydrolase, conjugated bile acid hydrolase (CBAH). The computational analyses, which were confirmed in water and using different CBAH mutants, revealed the existence of a chair-like transition state, which might be one of the specific features of the catalytic cycle of Ntn-hydrolases. Our results offer new insights on Ntn-mediated hydrolysis and suggest possible strategies for the creation of therapeutically useful inhibitors.

Self-consistent charge density functional tight binding (SCC-DFTB) is a promising method for hybrid quantum mechanics/molecular mechanics (QM/MM) simulations of enzyme-catalyzed reactions. The acylation reaction of fatty acid amide hydrolase (FAAH), a promising drug target, was investigated by applying a SCC-DFTB/CHARMM27 scheme. Calculated potential energy barriers resulted in reasonable agreement with experiments for oleamide (OA) and oleoylmethyl ester (OME) substrates, outperforming previous calculations performed at the PM3/CHARMM22 level. Furthermore, the experimental preference of FAAH in hydrolyzing OA faster than OME was adequately reproduced by calculations. All these findings indicate that the SCC-DFTB/CHARMM27 approach can be successfully applied to mechanistic investigations of FAAH-catalyzed reactions.

QM/MM modelling of FAAH inactivation by O-biphenyl-3-yl carbamates identifies the deprotonation of Ser241 as the key reaction step, explaining why FAAH is insensitive to the electron-donor effect of conjugated substituents; this may aid design of new inhibitors with improved selectivity and in vivo potency.