Target-Related Applications of First Principles Quantum Chemical Methods in Drug Design

End Group Effects on Anion Binding in Tetraglycine Peptide: A Computational Study

The importance of anions in various processes has led to a search for molecules that can effectively recognize and interact with these anions. This study explores how the tetraglycine [(Gly)4] peptide in its zwitterionic, neutral, and terminally capped forms acts as a receptor for H2PO4 - and HSO4 - anions within the framework of supramolecular host-guest chemistry. Using molecular dynamics (MD) simulations, we obtained the conformations of the receptor-anion complexes. Density functional theory (DFT), quantifies the complexes' interaction energies in both gas and solvent phases. Proton transfer within the zwitterionic complex with H2PO4 - anion alters peptide charge distribution, affecting its conformation and binding site arrangement, as analysed by quantum mechanics/molecular mechanics (QM/MM) methods. Symmetry-adapted perturbation theory (SAPT) and noncovalent interactions analysis highlight the role of electrostatic interactions in these receptor-anion complexes. It emphasizes the key interactions such as N-H⋅⋅⋅⋅O and O-H⋅⋅⋅O=C between the peptide backbone and anions and elucidates the molecular recognition mechanism driven by crucial noncovalent interactions. The termination of the peptide's end groups modulates anion binding sites from the backbone to the charged N-terminal, resulting in distinct binding sites. Our findings provide insights for designing peptides tailored to function as anion receptors in diverse supramolecular chemistry applications.

Multifaceted exploration of acylthiourea compounds: In vitro cytotoxicity, DFT calculations, molecular docking and dynamics simulation studies

This study reports the synthesis and analysis of biologically active acylthiourea compounds (1 and 2) with a cyclohexyl moiety. The compounds were characterized using UV-Visible, FT-IR, 1H/13C NMR, and elemental analysis. The crystal structure of 2 was solved, revealing intra- and inter-molecular hydrogen bonds. Density functional theory (DFT) calculations provided insights into chemical reactivity and non-covalent interactions. Cytotoxicity assays showed the cyclohexyl group enhanced the activity of compound 2 compared to compound 1. Epoxide hydrolase 1 was predicted as the enzyme target for both compounds. We modeled the structure of epoxide hydrolase 1 and performed molecular dynamics simulation and docking studies. Additionally, in silico docking with SARS-CoV-2 main protease, human ACE2, and avian influenza H5N1 hemagglutinin indicated strong binding potential of the compounds. This integrated approach improves our understanding of the biological potential of acylthiourea derivatives.

A computational study of potent series of selective estrogen receptor degraders for breast cancer therapy

A detailed multistep framework combining quantitative structure-activity relationship, global reactivity, absorption, distribution, metabolism and elimination properties, molecular docking and molecular dynamics simulation (MD) on a series of Selective Estrogen Receptor Down-Regulators (SERDs) interacting with Estrogen Receptor α (ERα) has been performed. The partial least squares regression method derived an empirical model with better predictive capability. The results of global reactivity descriptors revealed that all the compounds are considered strong electrophiles, allowing them to participate in polar reactions more easily. The Brain Or IntestinaL EstimateD permeation diagram revealed that compounds 49 and 31 were predicted to be well absorbed by the human gastrointestinal tract and would not enter the brain. The elucidation of the binding mode between the most active compounds that comply with Lipinski's and Veber's rules from the dataset and ERα targets was explored by molecular docking. The MD simulations were performed for 100 ns on the best compounds, which indicated their stability state under dynamics simulations. These findings are expected to help predict the anticancer activities of the studied SERD compounds and better understand their binding mechanism with ERα targets.Communicated by Ramaswamy H. Sarma.

Computational Methods for Molecular Understanding of the Antibiotic-Aminoacyl tRNA Synthetase Interaction

Developing an understanding of the interactions between an antibiotic and its binding site in a pathogen cell is the key to antibiotic design-an important cost-saving methodology compared to the costly and time-consuming random trial-and-error approach. The rapid development of antibiotic resistance provides an impetus for such studies. Recent years have witnessed the beginning of the use of combined computational techniques, including computer simulations and quantum mechanical computations, to understand how antibiotics bind at the active site of aminoacyl tRNA synthetases (aaRSs) from pathogens. Such computational protocols assist the knowledge-based design of antibiotics targeting aaRSs, which are their validated targets. After the ideas behind the protocols and their strategic planning are discussed, the protocols are described along with their major outcomes. This is followed by an integration of results from the different basic protocols. © 2023 Wiley Periodicals LLC. Basic Protocol 1: Analysis of active-site residues from primary sequence of synthetase and transfer RNAs Basic Protocol 2: Molecular dynamics simulation-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex Basic Protocol 3: Quantum mechanical method-based protocol to study the structure and dynamics of the aaRS active site:antibiotic complex.

Catalytic Mechanism of ATP Hydrolysis in the ATPase Domain of Human DNA Topoisomerase IIα

Human DNA topoisomerase IIα is a biological nanomachine that regulates the topological changes of the DNA molecule and is considered a prime target for anticancer drugs. Despite intensive research, many atomic details about its mechanism of action remain unknown. We investigated the ATPase domain, a segment of the human DNA topoisomerase IIα, using all-atom molecular simulations, multiscale quantum mechanics/molecular mechanics (QM/MM) calculations, and a point mutation study. The results suggested that the binding of ATP affects the overall dynamics of the ATPase dimer. Reaction modeling revealed that ATP hydrolysis favors the dissociative substrate-assisted reaction mechanism with the catalytic Glu87 serving to properly position and polarize the lytic water molecule. The point mutation study complemented our computational results, demonstrating that Lys378, part of the important QTK loop, acts as a stabilizing residue. The work aims to pave the way to a deeper understanding of these important molecular motors and to advance the development of new therapeutics.

Molecular docking, molecular dynamics simulations and reactivity, studies on approved drugs library targeting ACE2 and SARS-CoV-2 binding with ACE2

The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensin-converting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and [SARS-CoV-2/ACE2] complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor and [SARS-CoV-2/ACE2] complex better than chloroquine and hydroxychloroquine. This new understanding should help to improve predictions of the impact of such alternatives on COVID-19.Communicated by Ramaswamy H. Sarma.

Molecular mechanics approaches for rational drug design: forcefields and solvation models

The use of molecular mechanics (MM) in understanding the energy and target of a drug, its structures, and properties has increased recently. This is achieved by the formulation of a simple MM energy equation, which represents the sum of the different energy interactions, often referred to as “forcefields” (FFs). The concept of FFs is now widely used as one of the fundamental tools for the in silico prediction of drug-target interactions. To generate more accurate predictions in the in silico drug discovery projects, the solvent effects are often taken into account. This review seeks to present an introductory guide for the reader on the fundamentals of MM with special emphasis on the role of FFs and the solvation models.

SQM/COSMO Scoring Function: Reliable Quantum-Mechanical Tool for Sampling and Ranking in Structure-Based Drug Design

Quantum mechanical (QM) methods have been gaining importance in structure-based drug design where a reliable description of protein-ligand interactions is of utmost significance. However, strategies i. e. QM/MM, fragmentation or semiempirical (SQM) methods had to be pursued to overcome the unfavorable scaling of QM methods. Various SQM-based approaches have significantly contributed to the accuracy of docking and improvement of lead compounds. Parametrizations of SQM and implicit solvent methods in our laboratory have been instrumental to obtain a reliable SQM-based scoring function. The experience gained in its application for activity ranking of ligands binding to tens of protein targets resulted in setting up a faster SQM/COSMO scoring approach, which outperforms standard scoring methods in native pose identification for two dozen protein targets with ten thousand poses. Recently, SQM/COSMO was effectively applied in a proof-of-concept study of enrichment in virtual screening. Due to its superior performance, feasibility and chemical generality, we propose the SQM/COSMO approach as an efficient tool in structure-based drug design.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

BACKGROUND

Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process.

OBJECTIVE

The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design.

METHOD

This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained.

RESULTS

This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations.

CONCLUSION

The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

Fragment-based quantum mechanical approach to biomolecules, molecular clusters, molecular crystals and liquids

To study large molecular systems beyond the system size that the current state-of-the-art ab initio electronic structure methods could handle, fragment-based quantum mechanical (QM) approaches have been developed over the past years, and proved to be efficient in dealing with large molecular systems at various ab initio levels. According to the fragmentation approach, a large molecular system can be divided into subsystems (fragments), and subsequently the property of the whole system can be approximately obtained by taking a proper combination of the corresponding terms of individual fragments. Therefore, the standard QM calculation of a large system could be circumvented by carrying out a series of calculations on small fragments, which significantly promotes computational efficiency. The electrostatically embedded generalized molecular fractionation with conjugate caps (EE-GMFCC) method is one of the fragment-based QM approaches which has been developed by our research group in recent years. This Perspective presents the theoretical framework of this fragmentation method and its applications in biomolecules, molecular clusters, molecular crystals and liquids, including total energy calculation, protein-ligand/protein binding affinity prediction, geometry optimization, vibrational spectrum simulation, ab initio molecular dynamics simulation, and prediction of excited-state properties.

Adiabatic motion and statistical mechanics via mass-zero constrained dynamics

In recent work [Coretti et al., J. Chem. Phys., 2018, 149, 191102], a new algorithm to solve numerically the dynamics of the shell model for polarization was presented. The approach, broadly applicable to systems involving adiabatically separated dynamical variables, employs constrained molecular dynamics to strictly enforce the condition that the force on the fast degrees of freedom, modeled as having zero mass, is null at each time step. The algorithm is symplectic and fully time reversible, and results in stable and efficient propagation. In this paper we complete the discussion of the mechanics of mass-zero constrained dynamics by showing how to adapt it to problems where the fast degrees of freedom must satisfy additional conditions. This extension includes, in particular, the important case of first principles molecular dynamics. We then consider the statistical mechanics of the mass-zero constrained dynamical system demonstrating that the marginal probability sampled by the dynamics in the physical phase space recovers the form of the Born-Oppenheimer probability density. The effectiveness of the approach and the favorable scaling of the algorithm with system size are illustrated in test calculations of solid Na via orbital-free density functional dynamics.

QM Implementation in Drug Design: Does It Really Help?

Computational chemistry allows one to characterize the structure, dynamics, and energetics of protein-ligand interactions, which makes it a valuable tool in drug discovery in both academic research and pharmaceutical industry. Molecular mechanics (MM)-based approaches are widely utilized to assist the discovery of new drug candidates. However, the complexity of protein-ligand interactions challenges the accuracy and efficiency of the commonly used empirical methods. Aiming to provide better accuracy in the description of protein-ligand interactions, quantum mechanics (QM)-based approaches are becoming increasingly explored. In principle, QM calculation includes all contributions to the energy, accounting for terms usually missing in empirical force fields, and provides a greater degree of transferability. The usefulness of QM in drug design cannot be overemphasized. In this chapter, we present recent developments and applications of fragment-based QM method in studying the protein-ligand and protein-protein interactions. We critically discuss the performance of the fragment-based QM method at different ab initio levels while trying to answer a critical question: do QM-based methods really help in drug design?

User-Friendly Quantum Mechanics: Applications for Drug Discovery

Quantum mechanics (QM) methods provide a fine description of receptor-ligand interactions and of chemical reactions. Their use in drug design and drug discovery is increasing, especially for complex systems including metal ions in the binding sites, for the design of highly selective inhibitors, for the optimization of bi-specific compounds, to understand enzymatic reactions, and for the study of covalent ligands and prodrugs. They are also used for generating molecular descriptors for predictive QSAR/QSPR models and for the parameterization of force fields. Thanks to the continuous increase of computational power offered by GPUs and to the development of sophisticated algorithms, QM methods are becoming part of the standard tools used in computer-aided drug design (CADD). We present the most used QM methods and software packages, and we discuss recent representative applications in drug design and drug discovery.

Exploring PfDHFR reaction surface: A combined molecular dynamics and QM/MM analysis

The substrate to the enzyme PfDHFR (Plasmodium falciparum Dihydrofolate Reductase) is a small molecule dihydrofolate (DHF), it gets converted to tetrahydrofolate (THF) in the active site of the enzyme. The PfDHFR reaction surface involves the protonation of DHF to DHFP as an initial step before the catalytic conversion. The binding affinities of all these species (DHF, DHFP and THF) contribute to the mechanism of DHFR catalytic action. Molecular dynamics (MD) simulations and Quantum Mechanics/Molecular Mechanics (QM/MM) analysis were performed to evaluate the binding affinity and molecular recognition interactions of the substrate DHF/DHFP and the product THF, in the active site of wild-type PfDHFR (wtPfDHFR). The binding affinities of the cofactor NADPH/NADP⁺ were also estimated in all the three complexes. The molecular dynamics (MD) simulations of the substrate, product and cofactor in the cavities of wtPfDHFR revealed the variation of the atomic level interactions during the course of the catalytic conversion. It was found that the DHFP binds very strongly to the PfDHFR active site and pulls the cofactor NADPH closer to itself. The QM/MM analysis revealed that the binding energy of DHFP (-59.82 kcal/mol) and NADPH (-100.24 kcal/mol) in DHFP-wtPfDHFR complex, is higher in comparison to the binding energy of DHF (-38.67 kcal/mol) and NADPH (-77.53 kcal/mol) in DHF-wtPfDHFR complex and the binding energy of THF (-30.72 kcal/mol) and NADP⁺ (-73.72 kcal/mol) in THF-wtPfDHFR complex. The hydride ion donor-acceptor distance (DAD) analysis was also carried out. This combined MD and QM/MM analysis revealed that the protonation of DHF increases the proximity between the substrate and the cofactor, thus facilitates the reaction profile of PfDHFR.

Diarylcyclopropane hydroxamic acid inhibitors of histone deacetylase 4 designed by combinatorial approach and QM/MM calculations

Inhibitors of histone deacetylase superfamily (HDAC), which induce cell cycle arrest, trigger cell death and reduce angiogenesis appear as promising anti-cancer drugs targeting the epigenetic regulation of gene expression. Approved HDAC inhibitors were found effective against haematological and solid malignancies, other HDACIs are currently in clinical trials for the treatment of neurological diseases or immune disorders. Among those, diarylcyclopropane hydroxamic acids (DCHA) were found to be potent and selective inhibitors of the class IIa HDACs, specifically HDAC4, a pharmacological target for the treatment of Huntington's disease and muscular atrophy. Crystallographic analysis revealed that one of the aryl groups of the DCHA fills the lower specificity pocket of the HDAC4 catalytic site that is specific for the class IIa HDACs. We have used computer-assisted combinatorial chemistry, hybrid quantum mechanics/molecular mechanics (QM/MM) with implicit solvation and QSAR models to optimize DCHA inhibitors and propose more potent DCHA analogues. The QM/MM approach has been selected since the process of inhibitor binding to the catalytic zinc and polar amino acid residues of the deacetylase active site induces considerable rearrangement of electron density of the inhibitor. Virtual combinatorial library consisting of 12180 DCHA analogues was focused by means of structure-based evaluation to form a small combinatorial subset enriched in potentially interesting inhibitor candidates. Two validated QSAR models making use of computed relative binding affinities of the DCHA inhibitors to the HDAC4 (ΔΔG_com^QM/MM) were utilized to estimate the inhibitory potencies of the new analogues. The predicted half-maximal inhibitory concentrations (IC₅₀^pre) of the designed analogues fall into the low nanomolar concentration range and their predicted ADME properties are also favourable. The best designed DCHA analogues contain indazole, phenylpiperidine, phenyloxazole or hydroxypyridine moieties and stabilize bound inhibitors by hydrogen bonds to the catalytic water molecule and backbone carbonyl groups of the deacetylase active site residues. This makes them more potent and more specific inhibitors towards the HDAC4 isoform than the known diarylcyclopropane hydroxamic acids. The analogues are recommended for synthesis and experimental verification of inhibitory potencies in medicinal chemistry laboratories.

Hydrolysis of cephalexin and meropenem by New Delhi metallo-β-lactamase: the substrate protonation mechanism is drug dependent

Emergence of antibiotic resistance due to New Delhi metallo-β-lactamase (NDM-1) bacterial enzymes is of great concern due to their ability to hydrolyze a wide range of antibiotics. There are ongoing efforts to obtain the atomistic details of the hydrolysis mechanism in order to develop inhibitors for NDM-1. In particular, it remains elusive how drug molecules of different families of antibiotics are hydrolyzed by NDM-1 in an efficient manner. Here we report the detailed molecular mechanism of NDM-1 catalyzed hydrolysis of cephalexin, a cephalosporin family drug, and meropenem, a carbapenem family drug. This study employs molecular dynamics (MD) simulations using hybrid quantum mechanical/molecular mechanical (QM/MM) methods at the density functional theory (DFT) level, based on which reaction pathways and the associated free energies are obtained. We find that the mechanism and the free energy barrier for the ring-opening step are the same for both the drug molecules, while the subsequent protonation step differs. In particular, we observe that the mechanism of the protonation step depends on the R2 group of the drug molecule. Our simulations show that allylic carbon protonation occurs in the case of the cephalexin drug molecule where Lys211 is the proton donor, and the proton transfer occurs via a water chain formed (only) at the ring-opened intermediate structure. Based on the free energy profiles, the overall kinetics of drug hydrolysis is discussed. Finally, we show that the proposed mechanisms and free energy profiles could explain various experimental observations.

A General Route to Include Pauli Repulsion and Quantum Dispersion Effects in QM/MM Approaches

A methodology to account for nonelectrostatic interactions in Quantum Mechanical (QM)/Molecular Mechanics (MM) approaches is developed. Formulations for Pauli repulsion and dispersion energy, explicitly depending on the QM density, are derived. Such expressions are based on the definition of an auxiliary density on the MM portion and the Tkatchenko-Scheffler (TS) approach, respectively. The developed method is general enough to be applied to any QM/MM method and partition, provided an accurate tuning of a small number of parameters is obtained. The coupling of the method with both nonpolarizable and fully polarizable QM/fluctuating charge (FQ) approaches is reported and applied. A suitable parametrization for the aqueous solution, so that its most representative features are well reproduced, is outlined. Then, the obtained parametrization and method are applied to calculate the nonelectrostatic (repulsion and dispersion) interaction energy of nicotine in aqueous solution.

Correct Modeling of Cisplatin: a Paradigmatic Case

Quantum chemistry is a useful tool in modern approaches to drug and material design, but only when the adopted model reflects a correct physical picture. Paradigmatic is the case of cis‐diaminodichloroplatinum(II), cis‐[Pt(NH₃)₂Cl₂], for which the correct simulation of the structural and vibrational properties measured experimentally still remains an open question. By using this molecule as a proof of concept, it is shown that state‐of‐the‐art quantum chemical calculations and a simple model, capturing the basic physical flavors, a cis‐[Pt(NH₃)₂Cl₂] dimer, can provide the accuracy required for interpretative purposes. The present outcomes have fundamental implications for benchmark studies aiming at assessing the accuracy of a given computational protocol.

Implementing QM in docking calculations: is it a waste of computational time?

The greatest challenge in molecular docking (MD) is the deficiency of scoring functions (SFs), which limits their reliability. SFs are too simplified to represent the true features of the complex free energy of protein-ligand interactions. Investigations of docking functions have traded accuracy for speed through the use of approximations and simplifications. Consequently, there has been an increase in the popularity of quantum-mechanical (QM)-based methods as reference points for the development of fast, reliable, valuable, yet inexpensive, tools. As we discuss here, one significant QM-based parameter used to predict docking is the accuracy of atomic partial charges, which is strongly related to the accuracy of the SF prediction.

Binding free energies in the SAMPL5 octa-acid host-guest challenge calculated with DFT-D3 and CCSD(T)

We have tried to calculate the free energy for the binding of six small ligands to two variants of the octa-acid deep cavitand host in the SAMPL5 blind challenge. We employed structures minimised with dispersion-corrected density-functional theory with small basis sets and energies were calculated using large basis sets. Solvation energies were calculated with continuum methods and thermostatistical corrections were obtained from frequencies calculated at the HF-3c level. Care was taken to minimise the effects of the flexibility of the host by keeping the complexes as symmetric and similar as possible. In some calculations, the large net charge of the host was reduced by removing the propionate and benzoate groups. In addition, the effect of a restricted molecular dynamics sampling of structures was tested. Finally, we tried to improve the energies by using the DLPNO-CCSD(T) approach. Unfortunately, results of quite poor quality were obtained, with no correlation to the experimental data, systematically too positive affinities (by ~50 kJ/mol) and a mean absolute error (after removal of the systematic error) of 11-16 kJ/mol. DLPNO-CCSD(T) did not improve the results, so the accuracy is not limited by the energy function. Instead, four likely sources of errors were identified: first, the minimised structures were often incorrect, owing to the omission of explicit solvent. They could be partly improved by performing the minimisations in a continuum solvent with four water molecules around the charged groups of the ligands. Second, some ligands could bind in several different conformations, requiring sampling of reasonable structures. Third, there is an indication the continuum-solvation model has problems to accurately describe the binding of both the negatively and positively charged guest molecules. Fourth, different methods to calculate the thermostatistical corrections gave results that differed by up to 30 kJ/mol and there is an indication that HF-3c overestimates the entropy term. In conclusion, it is a challenge to calculate binding affinities for this octa-acid system with quantum-mechanical methods.

Insight into the Mechanism of Hydrolysis of Meropenem by OXA-23 Serine-β-lactamase Gained by Quantum Mechanics/Molecular Mechanics Calculations

The fast and constant development of drug resistant bacteria represents a serious medical emergency. To overcome this problem, the development of drugs with new structures and modes of action is urgently needed. In this work, we investigated, at the atomistic level, the mechanisms of hydrolysis of Meropenem by OXA-23, a class D β-lactamase, combining unbiased classical molecular dynamics and umbrella sampling simulations with classical force field-based and quantum mechanics/molecular mechanics potentials. Our calculations provide a detailed structural and dynamic picture of the molecular steps leading to the formation of the Meropenem-OXA-23 covalent adduct, the subsequent hydrolysis, and the final release of the inactive antibiotic. In this mechanistic framework, the predicted activation energy is in good agreement with experimental kinetic measurements, validating the expected reaction path.

Deacylation Mechanism and Kinetics of Acyl-Enzyme Complex of Class C β-Lactamase and Cephalothin

Understanding the molecular details of antibiotic resistance by the bacterial enzymes β-lactamases is vital for the development of novel antibiotics and inhibitors. In this spirit, the detailed mechanism of deacylation of the acyl-enzyme complex formed by cephalothin and class C β-lactamase is investigated here using hybrid quantum-mechanical/molecular-mechanical molecular dynamics methods. The roles of various active-site residues and substrate in the deacylation reaction are elucidated. We identify the base that activates the hydrolyzing water molecule and the residue that protonates the catalytic serine (Ser64). Conformational changes in the active sites and proton transfers that potentiate the efficiency of the deacylation reaction are presented. We have also characterized the oxyanion holes and other H-bonding interactions that stabilize the reaction intermediates. Together with the kinetic and mechanistic details of the acylation reaction, we analyze the complete mechanism and the overall kinetics of the drug hydrolysis. Finally, the apparent rate-determining step in the drug hydrolysis is scrutinized.

Analytical gradients for MP2, double hybrid functionals, and TD-DFT with polarizable embedding described by fluctuating charges

A polarizable quantum mechanics (QM)/ molecular mechanics (MM) approach recently developed for Hartree-Fock (HF) and Kohn-Sham (KS) methods has been extended to energies and analytical gradients for MP2, double hybrid functionals, and TD-DFT models, thus allowing the computation of equilibrium structures for excited electronic states together with more accurate results for ground electronic states. After a detailed presentation of the theoretical background and of some implementation details, a number of test cases are analyzed to show that the polarizable embedding model based on fluctuating charges (FQ) is remarkably more accurate than the corresponding electronic embedding based on a fixed charge (FX) description. In particular, a set of electronegativities and hardnesses has been optimized for interactions between QM and FQ regions together with new repulsion-dispersion parameters. After validation of both the numerical implementation and of the new parameters, absorption electronic spectra have been computed for representative model systems including vibronic effects. The results show remarkable agreement with full QM computations and significant improvement with respect to the corresponding FX results. The last part of the article provides some hints about computation of solvatochromic effects on absorption spectra in aqueous solution as a function of the number of FQ water molecules and on the use of FX external shells to improve the convergence of the results. © 2015 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc.

Computational insights into function and inhibition of fatty acid amide hydrolase

The Fatty Acid Amide Hydrolase (FAAH) enzyme is a membrane-bound serine hydrolase responsible for the deactivating hydrolysis of a family of naturally occurring fatty acid amides. FAAH is a critical enzyme of the endocannabinoid system, being mainly responsible for regulating the level of its main cannabinoid substrate anandamide. For this reason, pharmacological inhibition of FAAH, which increases the level of endogenous anandamide, is a promising strategy to cure a variety of diseases including pain, inflammation, and cancer. Much structural, mutagenesis, and kinetic data on FAAH has been generated over the last couple of decades. This has prompted several informative computational investigations to elucidate, at the atomic-level, mechanistic details on catalysis and inhibition of this pharmaceutically relevant enzyme. Here, we review how these computational studies - based on classical molecular dynamics, full quantum mechanics, and hybrid QM/MM methods - have clarified the binding and reactivity of some relevant substrates and inhibitors of FAAH. We also discuss the experimental implications of these computational insights, which have provided a thoughtful elucidation of the complex physical and chemical steps of the enzymatic mechanism of FAAH. Finally, we discuss how computations have been helpful for building structure-activity relationships of potent FAAH inhibitors.

QM/MM investigations of organic chemistry oriented questions

About 35 years after its first suggestion, QM/MM became the standard theoretical approach to investigate enzymatic structures and processes. The success is due to the ability of QM/MM to provide an accurate atomistic picture of enzymes and related processes. This picture can even be turned into a movie if nuclei-dynamics is taken into account to describe enzymatic processes. In the field of organic chemistry, QM/MM methods are used to a much lesser extent although almost all relevant processes happen in condensed matter or are influenced by complicated interactions between substrate and catalyst. There is less importance for theoretical organic chemistry since the influence of nonpolar solvents is rather weak and the effect of polar solvents can often be accurately described by continuum approaches. Catalytic processes (homogeneous and heterogeneous) can often be reduced to truncated model systems, which are so small that pure quantum-mechanical approaches can be employed. However, since QM/MM becomes more and more efficient due to the success in software and hardware developments, it is more and more used in theoretical organic chemistry to study effects which result from the molecular nature of the environment. It is shown by many examples discussed in this review that the influence can be tremendous, even for nonpolar reactions. The importance of environmental effects in theoretical spectroscopy was already known. Due to its benefits, QM/MM can be expected to experience ongoing growth for the next decade.In the present chapter we give an overview of QM/MM developments and their importance in theoretical organic chemistry, and review applications which give impressions of the possibilities and the importance of the relevant effects. Since there is already a bunch of excellent reviews dealing with QM/MM, we will discuss fundamental ingredients and developments of QM/MM very briefly with a focus on very recent progress. For the applications we follow a similar strategy.

Mechanism of acyl-enzyme complex formation from the Henry-Michaelis complex of class C β-lactamases with β-lactam antibiotics

Bacteria that cause most of the hospital-acquired infections make use of class C β-lactamase (CBL) among other enzymes to resist a wide spectrum of modern antibiotics and pose a major public health concern. Other than the general features, details of the defensive mechanism by CBL, leading to the hydrolysis of drug molecules, remain a matter of debate, in particular the identification of the general base and role of the active site residues and substrate. In an attempt to unravel the detailed molecular mechanism, we carried out extensive hybrid quantum mechanical/molecular mechanical Car-Parrinello molecular dynamics simulation of the reaction with the aid of the metadynamics technique. On this basis, we report here the mechanism of the formation of the acyl-enzyme complex from the Henry-Michaelis complex formed by β-lactam antibiotics and CBL. We considered two β-lactam antibiotics, namely, cephalothin and aztreonam, belonging to two different subfamilies. A general mechanism for the formation of a β-lactam antibiotic-CBL acyl-enzyme complex is elicited, and the individual roles of the active site residues and substrate are probed. The general base in the acylation step has been identified as Lys67, while Tyr150 aids the protonation of the β-lactam nitrogen through either the substrate carboxylate group or a water molecule.

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.