Luigi Capoferri

A Catalytic Mechanism for Cysteine N-Terminal Nucleophile Hydrolases, as Revealed by Free Energy Simulations

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.

Quantum mechanics/molecular mechanics modeling of fatty acid amide hydrolase reactivation distinguishes substrate from irreversible covalent inhibitors.

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.

Understanding the role of carbamate reactivity in fatty acid amide hydrolase inhibition by QM/MM mechanistic modelling

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.

Application of a SCC-DFTB QM/MM approach to the investigation of the catalytic mechanism of fatty acid amide hydrolase

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.

Linear Interaction Energy Based Prediction of Cytochrome P450 1A2 Binding Affinities with Reliability Estimation

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 pK i units).

Integrative Modeling Strategies for Predicting Drug Toxicities at the eTOX Project

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).

Insights into regioselective metabolism of mefenamic acid by cytochrome P450 BM3 mutants through crystallography, docking, molecular dynamics, and free energy calculations

Cytochrome P450 BM3 (CYP102A1) mutant M11 is able to metabolize a wide range of drugs and drug‐like compounds. Among these, M11 was recently found to be able to catalyze formation of human metabolites of mefenamic acid and other nonsteroidal anti‐inflammatory drugs (NSAIDs). Interestingly, single active‐site mutations such as V87I were reported to invert regioselectivity in NSAID hydroxylation. In this work, we combine crystallography and molecular simulation to study the effect of single mutations on binding and regioselective metabolism of mefenamic acid by M11 mutants. The heme domain of the protein mutant M11 was expressed, purified, and crystallized, and its X‐ray structure was used as template for modeling. A multistep approach was used that combines molecular docking, molecular dynamics (MD) simulation, and binding free‐energy calculations to address protein flexibility. In this way, preferred binding modes that are consistent with oxidation at the experimentally observed sites of metabolism (SOMs) were identified. Whereas docking could not be used to retrospectively predict experimental trends in regioselectivity, we were able to rank binding modes in line with the preferred SOMs of mefenamic acid by M11 and its mutants by including protein flexibility and dynamics in free‐energy computation. In addition, we could obtain structural insights into the change in regioselectivity of mefenamic acid hydroxylation due to single active‐site mutations. Our findings confirm that use of MD and binding free‐energy calculation is useful for studying biocatalysis in those cases in which enzyme binding is a critical event in determining the selective metabolism of a substrate. Proteins 2016; 84:383–396.

Exploiting Free-Energy Minima to Design Novel EphA2 Protein-Protein Antagonists: From Simulation to Experiment and Return.

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.

Quantum mechanics/molecular mechanics modeling of covalent addition between EGFR-cysteine 797 and N-(4-anilinoquinazolin-6-yl) acrylamide.

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.

Comprehensive and Automated Linear Interaction Energy Based Binding-Affinity Prediction for Multifarious Cytochrome P450 Aromatase Inhibitors

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.