Malonate-based inhibitors of mammalian serine racemase: kinetic characterization and structure-based computational study

The Development of a Regulator of Human Serine Racemase for N-Methyl-D-aspartate Function

It is crucial to regulate N-methyl-D-aspartate (NMDA) function bivalently depending on the central nervous system (CNS) conditions. CNS disorders with NMDA hyperfunction are involved in the pathogenesis of neurotoxic and/or neurodegenerative disorders with elevated D-serine, one of the NMDA receptor co-agonists. On the contrary, NMDA-enhancing agents have been demonstrated to improve psychotic symptoms and cognition in CNS disorders with NMDA hypofunction. Serine racemase (SR), the enzyme regulating both D- and L-serine levels through both racemization (catalysis from L-serine to D-serine) and β-elimination (degradation of both D- and L-serine), emerges as a promising target for bidirectional regulation of NMDA function. In this study, we explored using dimethyl malonate (DMM), a pro-drug of the SR inhibitor malonate, to modulate NMDA activity in C57BL/6J male mice via intravenous administration. Unexpectedly, 400 mg/kg DMM significantly elevated, rather than decreased (as a racemization inhibitor), D-serine levels in the cerebral cortex and plasma. This outcome prompted us to investigate the regulatory effects of dodecagalloyl-α-D-xylose (α12G), a synthesized tannic acid analog, on SR activity. Our findings showed that α12G enhanced the racemization activity of human SR by about 8-fold. The simulated and fluorescent assay of binding affinity suggested a noncooperative binding close to the catalytic residues, Lys56 and Ser84. Moreover, α12G treatment can improve behaviors associated with major CNS disorders with NMDA hypofunction including hyperactivity, prepulse inhibition deficit, and memory impairment in animal models of positive symptoms and cognitive impairment of psychosis. In sum, our findings suggested α12G is a potential therapeutic for treating CNS disorders with NMDA hypofunction.

Design and evaluation of substrate-product analog inhibitors for racemases and epimerases utilizing a 1,1-proton transfer mechanism

Racemases and epimerases catalyze the inversion of stereochemistry at asymmetric carbon atoms to generate stereoisomers that often play important roles in normal and pathological physiology. Consequently, there is interest in developing inhibitors of these enzymes for drug discovery. A strategy for the rational design of substrate-product analog (SPA) inhibitors of racemases and epimerases utilizing a direct 1,1-proton transfer mechanism is elaborated. This strategy assumes that two groups on the asymmetric carbon atom remain fixed at active-site binding determinants, while the hydrogen and third, motile group move during catalysis, with the latter potentially traveling between an R- and S-pocket at the active site. SPAs incorporate structural features of the substrate and product, often with geminal disubstitution on the asymmetric carbon atom to simultaneously present the motile group to both the R- and S-pockets. For racemases operating on substrates bearing three polar groups (glutamate, aspartate, and serine racemases) or with compact, hydrophobic binding pockets (proline racemase), substituent motion is limited and the design strategy furnishes inhibitors with poor or modest binding affinities. The approach is most successful when substrates have a large, motile hydrophobic group that binds at a plastic and/or capacious hydrophobic site. Potent inhibitors were developed for mandelate racemase, isoleucine epimerase, and α-methylacyl-CoA racemase using the SPA inhibitor design strategy, exhibiting binding affinities ranging from substrate-like to exceeding that of the substrate by 100-fold. This rational approach for designing inhibitors of racemases and epimerases having the appropriate active-site architectures is a useful strategy for furnishing compounds for drug development.

Information-Rich, Dual-Function 13C/2H-Isotopic Crosstalk NMR Assay for Human Serine Racemase (hSR) Provides a PLP-Enzyme "Partitioning Fingerprint" and Reveals Disparate Chemotypes for hSR Inhibition

The first dual-function assay for human serine racemase (hSR), the only bona fide racemase in human biology, is reported. The hSR racemization function is essential for neuronal signaling, as the product, d-serine (d-Ser), is a potent N-methyl d-aspartate (NMDA) coagonist, important for learning and memory, with dysfunctional d-Ser-signaling being observed in some neuronal disorders. The second hSR function is β-elimination and gives pyruvate; this activity is elevated in colorectal cancer. This new NMR-based assay allows one to monitor both α-proton-exchange chemistry and β-elimination using only the native l-Ser substrate and hSR and is the most sensitive such assay. The assay judiciously employs segregated dual 13C-labeling and 13C/2H crosstalk, exploiting both the splitting and shielding effects of deuterium. The assay is deployed to screen a 1020-compound library and identifies an indolo-chroman-2,4-dione inhibitor family that displays allosteric site binding behavior (noncompetitive inhibition vs l-Ser substrate; competitive inhibition vs adenosine 5'-triphosphate (ATP)). This assay also reveals important mechanistic information for hSR; namely, that H/D exchange is ∼13-fold faster than racemization, implying that K56 protonates the carbanionic intermediate on the si-face much faster than does S84 on the re-face. Moreover, the 13C NMR peak pattern seen is suggestive of internal return, pointing to K56 as the likely enamine-protonating residue for β-elimination. The 13C/2H-isotopic crosstalk assay has also been applied to the enzyme tryptophan synthase and reveals a dramatically different partition ratio in this active site (β-replacement: si-face protonation ∼6:1 vs β-elimination: si-face protonation ∼1:3.6 for hSR), highlighting the value of this approach for fingerprinting the pyridoxal phosphate (PLP) enzyme mechanism.

How well do semiempirical QM methods describe the structure of proteins?

Semiempirical quantum-mechanical (QM) computational methods are an increasingly popular tool for the study of biomolecular systems. They were, however, developed and tested mostly on small model molecules. In this work, we explore one topic fundamental to these applications: the ability of the methods to describe the structure of proteins. In a set of 19 proteins for which a crystal structure with very high resolution is available, we analyze the properties of the protein geometries optimized using several semiempirical QM methods including PM6-D3H4, PM7, and GFN2-xTB. Some of the methods provide a very good description of the general structural features of the protein, yielding results better than or comparable to the AMBER ff03 force field. However, PM7 and PM6-D3H4 optimizations introduce artificial close contacts in the structure, which is partially remediated by reparameterization.

Conjugates of Tacrine with Aminomethylidene-Substituted Malonates: Synthesis and Biological Evaluation

Abstract

The condensation of tacrine aminopolymethylene derivatives with diethyl (ethoxymethylidene)malonate led to the new hybrid compounds—conjugates, which were the effective inhibitors of acetylcholinesterase (AChE) (IC50 up to 0.538 μM) and butyrylcholinesterase (IC50 up to 0.0314 μM). They can displace propidium iodide from peripherical anionic site of AChE at the level of the reference drug donepezil and demonstrate a weak antioxidant activity. Conjugates are of interest for further extended research as potential drugs for the Alzheimer’s disease treatment.

Tyrosine 121 moves revealing a ligandable pocket that couples catalysis to ATP-binding in serine racemase

Human serine racemase (hSR) catalyses racemisation of L-serine to D-serine, the latter of which is a co-agonist of the NMDA subtype of glutamate receptors that are important in synaptic plasticity, learning and memory. In a 'closed' hSR structure containing the allosteric activator ATP, the inhibitor malonate is enclosed between the large and small domains while ATP is distal to the active site, residing at the dimer interface with the Tyr121 hydroxyl group contacting the α-phosphate of ATP. In contrast, in 'open' hSR structures, Tyr121 sits in the core of the small domain with its hydroxyl contacting the key catalytic residue Ser84. The ability to regulate SR activity by flipping Tyr121 from the core of the small domain to the dimer interface appears to have evolved in animals with a CNS. Multiple X-ray crystallographic enzyme-fragment structures show Tyr121 flipped out of its pocket in the core of the small domain. Data suggest that this ligandable pocket could be targeted by molecules that inhibit enzyme activity.

Fast and Accurate Quantum Mechanical Modeling of Large Molecular Systems Using Small Basis Set Hartree-Fock Methods Corrected with Atom-Centered Potentials

There has been significant interest in developing fast and accurate quantum mechanical methods for modeling large molecular systems. In this work, by utilizing a machine learning regression technique, we have developed new low-cost quantum mechanical approaches to model large molecular systems. The developed approaches rely on using one-electron Gaussian-type functions called atom-centered potentials (ACPs) to correct for the basis set incompleteness and the lack of correlation effects in the underlying minimal or small basis set Hartree-Fock (HF) methods. In particular, ACPs are proposed for ten elements common in organic and bioorganic chemistry (H, B, C, N, O, F, Si, P, S, and Cl) and four different base methods: two minimal basis sets (MINIs and MINIX) plus a double-ζ basis set (6-31G*) in combination with dispersion-corrected HF (HF-D3/MINIs, HF-D3/MINIX, HF-D3/6-31G*) and the HF-3c method. The new ACPs are trained on a very large set (73 832 data points) of noncovalent properties (interaction and conformational energies) and validated additionally on a set of 32 048 data points. All reference data are of complete basis set coupled-cluster quality, mostly CCSD(T)/CBS. The proposed ACP-corrected methods are shown to give errors in the tenths of a kcal/mol range for noncovalent interaction energies and up to 2 kcal/mol for molecular conformational energies. More importantly, the average errors are similar in the training and validation sets, confirming the robustness and applicability of these methods outside the boundaries of the training set. In addition, the performance of the new ACP-corrected methods is similar to complete basis set density functional theory (DFT) but at a cost that is orders of magnitude lower, and the proposed ACPs can be used in any computational chemistry program that supports effective-core potentials without modification. It is also shown that ACPs improve the description of covalent and noncovalent bond geometries of the underlying methods and that the improvement brought about by the application of the ACPs is directly related to the number of atoms to which they are applied, allowing the treatment of systems containing some atoms for which ACPs are not available. Overall, the ACP-corrected methods proposed in this work constitute an alternative accurate, economical, and reliable quantum mechanical approach to describe the geometries, interaction energies, and conformational energies of systems with hundreds to thousands of atoms.

NENCI-2021. I. A large benchmark database of non-equilibrium non-covalent interactions emphasizing close intermolecular contacts

In this work, we present NENCI-2021, a benchmark database of ∼8000 Non-Equilibirum Non-Covalent Interaction energies for a large and diverse selection of intermolecular complexes of biological and chemical relevance. To meet the growing demand for large and high-quality quantum mechanical data in the chemical sciences, NENCI-2021 starts with the 101 molecular dimers in the widely used S66 and S101 databases and extends the scope of these works by (i) including 40 cation-π and anion-π complexes, a fundamentally important class of non-covalent interactions that are found throughout nature and pose a substantial challenge to theory, and (ii) systematically sampling all 141 intermolecular potential energy surfaces (PESs) by simultaneously varying the intermolecular distance and intermolecular angle in each dimer. Designed with an emphasis on close contacts, the complexes in NENCI-2021 were generated by sampling seven intermolecular distances along each PES (ranging from 0.7× to 1.1× the equilibrium separation) and nine intermolecular angles per distance (five for each ion-π complex), yielding an extensive database of 7763 benchmark intermolecular interaction energies (E_int) obtained at the coupled-cluster with singles, doubles, and perturbative triples/complete basis set [CCSD(T)/CBS] level of theory. The E_int values in NENCI-2021 span a total of 225.3 kcal/mol, ranging from -38.5 to +186.8 kcal/mol, with a mean (median) E_int value of -1.06 kcal/mol (-2.39 kcal/mol). In addition, a wide range of intermolecular atom-pair distances are also present in NENCI-2021, where close intermolecular contacts involving atoms that are located within the so-called van der Waals envelope are prevalent-these interactions, in particular, pose an enormous challenge for molecular modeling and are observed in many important chemical and biological systems. A detailed symmetry-adapted perturbation theory (SAPT)-based energy decomposition analysis also confirms the diverse and comprehensive nature of the intermolecular binding motifs present in NENCI-2021, which now includes a significant number of primarily induction-bound dimers (e.g., cation-π complexes). NENCI-2021 thus spans all regions of the SAPT ternary diagram, thereby warranting a new four-category classification scheme that includes complexes primarily bound by electrostatics (3499), induction (700), dispersion (1372), or mixtures thereof (2192). A critical error analysis performed on a representative set of intermolecular complexes in NENCI-2021 demonstrates that the E_int values provided herein have an average error of ±0.1 kcal/mol, even for complexes with strongly repulsive E_int values, and maximum errors of ±0.2-0.3 kcal/mol (i.e., ∼±1.0 kJ/mol) for the most challenging cases. For these reasons, we expect that NENCI-2021 will play an important role in the testing, training, and development of next-generation classical and polarizable force fields, density functional theory approximations, wavefunction theory methods, and machine learning based intra- and inter-molecular potentials.

Druggable Hot Spots in the Schistosomiasis Cathepsin B1 Target Identified by Functional and Binding Mode Analysis of Potent Vinyl Sulfone Inhibitors

Schistosomiasis, a parasitic disease caused by blood flukes of the genus Schistosoma, is a global health problem with over 200 million people infected. Treatment relies on just one drug, and new chemotherapies are needed. Schistosoma mansoni cathepsin B1 (SmCB1) is a critical peptidase for the digestion of host blood proteins and a validated drug target. We screened a library of peptidomimetic vinyl sulfones against SmCB1 and identified the most potent SmCB1 inhibitors reported to date that are active in the subnanomolar range with second order rate constants (k_2nd) of ∼2 × 10⁵ M^–1 s^–1. High resolution crystal structures of the two best inhibitors in complex with SmCB1 were determined. Quantum chemical calculations of their respective binding modes identified critical hot spot interactions in the S1′ and S2 subsites. The most potent inhibitor targets the S1′ subsite with an N-hydroxysulfonic amide moiety and displays favorable functional properties, including bioactivity against the pathogen, selectivity for SmCB1 over human cathepsin B, and reasonable metabolic stability. Our results provide structural insights for the rational design of next-generation SmCB1 inhibitors as potential drugs to treat schistosomiasis.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule—along with any bound ligand(s)—within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method—coupled with our XModeScore method for experimental tautomer/protomer state determination—to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

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.

Using the Semiempirical Quantum Mechanics in Improving the Molecular Docking: A Case Study with CDK2

In this study, we use some modified semiempirical quantum mechanics (SQM) methods for improving the molecular docking process. To this end, the three popular SQM Hamiltonians, PM6, PM6-D3H4X, and PM7 are employed for geometry optimization of some binding modes of ligands docked into the human cyclin-dependent kinase 2 (CDK2) by two widely used docking tools, AutoDock and AutoDock Vina. The results were analyzed with two different evaluation metrics: the symmetry-corrected heavy-atom RMSD and the fraction of recovered ligand-protein contacts. It is shown that the evaluation of the fraction of recovered contacts is more useful to measure the similarity between two structures when interacting with a protein. It was also found that AutoDock is more successful than AutoDock Vina in producing the correct ligand poses (RMSD≤2.0 Å) and ranking of the poses. It is also demonstrated that the ligand optimization at the SQM level improves the docking results and the SQM structures have a significantly better fit to the observed crystal structures. Finally, the SQM optimizations reduce the number of close contacts in the docking poses and successfully remove most of the clash or bad contacts between ligand and protein.

DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green's functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives.

Accelerated identification of serine racemase inhibitor from Centella asiatica

Serine racemase (SR) converts the free form of L-serine into D-serine (DS) in the mammalian brain. The DS functions as a co-agonist of N-methyl D-aspartate (NMDA) receptor. The over- activation of NMDA receptor leads to many neurological disorders like stroke, amyotrophic lateral sclerosis, Alzheimer’s disease and an effective inhibitor of SR could be a corrective method for the receptor over-activation. We report for the first time here a rapid way of purifying and identifying an inhibitor from medicinal plants known to have the neuro-protective effect. We have purified SR inhibitor from the methanolic extract of Centella asiatica by affinity method. High resolution mass spectrometry and infrared spectroscopy were used to identify the ligand to be madecassoside. We have shown the madecassoside binding in silico and its inhibition of recombinant human serine racemase in vitro and ex vivo.

Conformational flexibility within the small domain of human serine racemase

Serine racemase (SR) is a pyridoxal 5'-phosphate (PLP)-containing enzyme that converts L-serine to D-serine, an endogenous co-agonist for the N-methyl-D-aspartate receptor (NMDAR) subtype of glutamate ion channels. SR regulates D-serine levels by the reversible racemization of L-serine to D-serine, as well as the catabolism of serine by α,β-elimination to produce pyruvate. The modulation of SR activity is therefore an attractive therapeutic approach to disorders associated with abnormal glutamatergic signalling since it allows an indirect modulation of NMDAR function. In the present study, a 1.89 Å resolution crystal structure of the human SR holoenzyme (including the PLP cofactor) with four subunits in the asymmetric unit is described. Comparison of this new structure with the crystal structure of human SR with malonate (PDB entry 3l6b) shows an interdomain cleft that is open in the holo structure but which disappears when the inhibitor malonate binds and is enclosed. This is owing to a shift of the small domain (residues 78-155) in human SR similar to that previously described for the rat enzyme. This domain movement is accompanied by changes within the twist of the central four-stranded β-sheet of the small domain, including changes in the φ-ψ angles of all three residues in the C-terminal β-strand (residues 149-151). In the malonate-bound structure, Ser84 (a catalytic residue) points its side chain at the malonate and is preceded by a six-residue β-strand (residues 78-83), but in the holoenzyme the β-strand is only four residues (78-81) and His82 has φ-ψ values in the α-helical region of the Ramachandran plot. These data therefore represent a crystallographic platform that enables the structure-guided design of small-molecule modulators for this important but to date undrugged target.

Advances in Docking

BACKGROUND

Design of small molecules which are able to bind to the protein responsible for a disease is the key step of the entire process of the new medicine discovery. Atomistic computer modeling can significantly improve effectiveness of such design. The accurate calculation of the free energy of binding a small molecule (a ligand) to the target protein is the most important problem of such modeling. Docking is one of the most popular molecular modeling methods for finding ligand binding poses in the target protein and calculating the protein-ligand binding energy. This energy is used for finding the most active compounds for the given target protein. This short review aims to give a concise description of distinctive features of docking programs focusing on computation methods and approximations influencing their accuracy.

METHODS

This review is based on the peer-reviewed research literature including author's own publications. The main features of several representative docking programs are briefly described focusing on their characteristics influencing docking accuracy: force fields, energy calculations, solvent models, algorithms of the best ligand pose search, global and local optimizations, ligand and target protein flexibility, and the simplifications made for the docking accelerating. Apart from other recent reviews focused mainly on the performance of different docking programs, in this work, an attempt is made to extract the most important functional characteristics defining the docking accuracy. Also a roadmap for increasing the docking accuracy is proposed. This is based on the new generation of docking programs which have been realized recently. These programs and respective new global optimization algorithms are described shortly.

RESULTS

Several popular conventional docking programs are considered. Their search of the best ligand pose is based explicitly or implicitly on the global optimization problem. Several algorithms are used to solve this problem, and among them, the heuristic genetic algorithm is distinguished by its popularity and an elaborate design. All conventional docking programs for their acceleration use the preliminary calculated grids of protein-ligand interaction potentials or preferable points of protein and ligand conjugation. These approaches and commonly used fitting parameters restrict strongly the docking accuracy. Solvent is considered in exceedingly simplified approaches in the course of the global optimization and the search for the best ligand poses. More accurate approaches on the base of implicit solvent models are used frequently for more careful binding energy calculations after docking. The new generation of docking programs are developed recently. They find the spectrum of low energy minima of a protein-ligand complex including the global minimum. These programs should be more accurate because they do not use a preliminary calculated grid of protein-ligand interaction potentials and other simplifications, the energy of any conformation of the molecular system is calculated in the frame of a given force field and there are no fitting parameters. A new docking algorithm is developed and fulfilled specially for the new docking programs. This algorithm allows docking a flexible ligand into a flexible protein with several dozen mobile atoms on the base of the global energy minimum search. Such docking results in improving the accuracy of ligand positioning in the docking process. The adequate choice of the method of molecular energy calculations also results in the better docking positioning accuracy. An advancement in the application of quantum chemistry methods to docking and scoring is revealed.

CONCLUSION

The findings of this review confirm the great demand in docking programs for discovery of new medicine substances with the help of molecular modeling. New trends in docking programs design are revealed. These trends are focused on the increase of the docking accuracy at the expense of more accurate molecular energy calculations without any fitting parameters, including quantum-chemical methods and implicit solvent models, and by using new global optimization algorithms which make it possible to treat flexibility of ligands and mobility of protein atoms simultaneously. Finally, it is shown that all the necessary prerequisites for increasing the docking accuracy can be accomplished in practice.

Evaluating Transition Metal Barrier Heights with the Latest Density Functional Theory Exchange-Correlation Functionals: The MOBH35 Benchmark Database

A new database of transition metal reaction barrier heights (MOBH35) is presented. Benchmark energies (forward and reverse barriers and reaction energy) are calculated using DLPNO-CCSD(T) extrapolated to the complete basis set limit using a Weizmann-1-like scheme. Using these benchmark energies, the performance of a wide selection of density functional theory (DFT) exchange-correlation functionals, including the latest from the Martin, Truhlar, and Head-Gordon groups, is evaluated. It was found, using the def2-TZVPP basis set, that the ωB97M-V (MAD 1.7 kcal/mol), ωB97M-D3BJ (MAD 1.9 kcal/mol), ωB97X-V (MAD 2.0 kcal/mol), and revTPSS0-D4 (MAD 2.2 kcal/mol) hybrid functionals are recommended. The double-hybrid functionals B2K-PLYP (MAD 1.7 kcal/mol) and revDOD-PBEP86-D4 (MAD 1.8 kcal/mol) also performed well, but this has to be balanced by their increased computational cost.

Semiempirical Quantum-Chemical Methods with Orthogonalization and Dispersion Corrections

We present two new semiempirical quantum-chemical methods with orthogonalization and dispersion corrections: ODM2 and ODM3 (ODM x). They employ the same electronic structure model as the OM2 and OM3 (OM x) methods, respectively. In addition, they include Grimme's dispersion correction D3 with Becke-Johnson damping and three-body corrections E ABC for Axilrod-Teller-Muto dispersion interactions as integral parts. Heats of formation are determined by adding explicitly computed zero-point vibrational energy and thermal corrections, in contrast to standard MNDO-type and OM x methods. We report ODM x parameters for hydrogen, carbon, nitrogen, oxygen, and fluorine that are optimized with regard to a wide range of carefully chosen state-of-the-art reference data. Extensive benchmarks show that the ODM x methods generally perform better than the available MNDO-type and OM x methods for ground-state and excited-state properties, while they describe noncovalent interactions with similar accuracy as OM x methods with a posteriori dispersion corrections.

The Energy Landscape of Human Serine Racemase

Human serine racemase is a pyridoxal 5′-phosphate (PLP)-dependent dimeric enzyme that catalyzes the reversible racemization of L-serine and D-serine and their dehydration to pyruvate and ammonia. As D-serine is the co-agonist of the N-methyl-D-aspartate receptors for glutamate, the most abundant excitatory neurotransmitter in the brain, the structure, dynamics, function, regulation and cellular localization of serine racemase have been investigated in detail. Serine racemase belongs to the fold-type II of the PLP-dependent enzyme family and structural models from several orthologs are available. The comparison of structures of serine racemase co-crystallized with or without ligands indicates the presence of at least one open and one closed conformation, suggesting that conformational flexibility plays a relevant role in enzyme regulation. ATP, Mg²⁺, Ca²⁺, anions, NADH and protein interactors, as well as the post-translational modifications nitrosylation and phosphorylation, finely tune the racemase and dehydratase activities and their relative reaction rates. Further information on serine racemase structure and dynamics resulted from the search for inhibitors with potential therapeutic applications. The cumulative knowledge on human serine racemase allowed obtaining insights into its conformational landscape and into the mechanisms of cross-talk between the effector binding sites and the active site.

Catalytic Space Engineering of Porphyrin Metal-Organic Frameworks for Combined CO2 Capture and Conversion at a Low Concentration

Porous porphyrin metal-organic frameworks (PMOFs) provide promising platforms for studying CO₂ capture and conversion (C3) owing to their versatility in photoelectric, catalytic, and redox activities and porphyrin coordination chemistry. Herein, we report the C3 application of two PMOFs by engineering the coordination space through the introduction of two catalytic metalloporphyrins doped with rhodium or iridium, Rh-PMOF-1 and Ir-PMOF-1, both of which can serve as heterogeneous catalysts for the chemical fixation of CO₂ into cyclic carbonates with yields of up to 99 %. Remarkably, the catalytic reactions can effectively proceed under low CO₂ concentrations and high yields of 83 % and 73 % can be obtained under 5 % CO₂ in the presence of Rh-PMOF-1 and Ir-PMOF-1, respectively. The synergistic effect of the metalloporphyrin ligand and the Zr₆ O₈ cluster, in combination with the CO₂ concentration effect from the pore space, might account for the excellent catalytic performance of Rh-PMOF-1 under low CO₂ concentration. Recycling tests of Rh-PMOF-1 show negligible loss of catalytic activity after 10 runs.

Testing Semiempirical Quantum Mechanical Methods on a Data Set of Interaction Energies Mapping Repulsive Contacts in Organic Molecules

Semiempirical quantum mechanical (QM) methods with corrections for noncovalent interactions provide a favorable combination of accuracy and computational efficiency that makes them a useful tool for a study of large molecular systems. It was, however, noted that the accuracy of these methods deteriorates at intermolecular distances shorter than equilibrium. In this work, we explore this issue systematically using a newly developed data set of benchmark interaction energies named R160×6. This data set maps repulsive contacts in organic molecules, and it consists of 160 model complexes for which six points along the dissociation curve are provided. Testing a wide range of semiempirical QM methods against the CCSD(T)/CBS benchmark revealed that most methods, and all the dispersion-corrected ones, underestimate the repulsion systematically. The worst cases are usually hydrogen-hydrogen contacts. The best results were obtained with PM6-D3H4 and DFTB3-D3H4, as these methods already contain a correction for the H-H repulsion, but the errors are still about twice as large as in equilibrium geometries.

Potential of ionic liquids for inhibiting the growth and β-lactamase production by Bacillus cereus EMB20

Present work reports the inhibition of Bacillus cereus EMB20 β-lactamase by a deep eutectic solvent, maline in an uncompetitive manner. Far-UV CD and intrinsic fluorescence spectroscopy revealed a disrupted secondary as well as tertiary structure as a function of maline concentration. The effect of individual components of maline on β-lactamase inhibition showed that malonic acid was mainly responsible for inhibiting the β-lactamase. Structural and docking studies found that malonic acid led to major perturbations in the secondary and tertiary structure of the enzyme while H-bonding with the active site residues. Further the antibacterial and cytotoxic studies also confirmed the potential of maline as a potent growth inhibitor of β-lactamase producing B. cereus EMB20.

Superior Performance of the SQM/COSMO Scoring Functions in Native Pose Recognition of Diverse Protein-Ligand Complexes in Cognate Docking

General and reliable description of structures and energetics in protein-ligand (PL) binding using the docking/scoring methodology has until now been elusive. We address this urgent deficiency of scoring functions (SFs) by the systematic development of corrected semiempirical quantum mechanical (SQM) methods, which correctly describe all types of noncovalent interactions and are fast enough to treat systems of thousands of atoms. Two most accurate SQM methods, PM6-D3H4X and SCC-DFTB3-D3H4X, are coupled with the conductor-like screening model (COSMO) implicit solvation model in so-called "SQM/COSMO" SFs and have shown unique recognition of native ligand poses in cognate docking in four challenging PL systems, including metalloprotein. Here, we apply the two SQM/COSMO SFs to 17 diverse PL complexes and compare their performance with four widely used classical SFs (Glide XP, AutoDock4, AutoDock Vina, and UCSF Dock). We observe superior performance of the SQM/COSMO SFs and identify challenging systems. This method, due to its generality, comparability across the chemical space, and lack of need for any system-specific parameters, gives promise of becoming, after comprehensive large-scale testing in the near future, a useful computational tool in structure-based drug design and serving as a reference method for the development of other SFs.

Human serine racemase structure/activity relationship studies provide mechanistic insight and point to position 84 as a hot spot for β-elimination function

There is currently great interest in human serine racemase, the enzyme responsible for producing the NMDA co-agonist d-serine. Reported correlation of d-serine levels with disorders including Alzheimer's disease, ALS, and ischemic brain damage (elevated d-serine) and schizophrenia (reduced d-serine) has further piqued this interest. Reported here is a structure/activity relationship study of position Ser⁸⁴, the putative re-face base. In the most extreme case of functional reprogramming, the S84D mutant displays a dramatic reversal of β-elimination substrate specificity in favor of l-serine over the normally preferred l-serine-O-sulfate (∼1200-fold change in k_cat/K_m ratios) and l (l-THA; ∼5000-fold change in k_cat/K_m ratios) alternative substrates. On the other hand, the S84T (which performs l-Ser racemization activity), S84A (good k_cat but high K_m for l-THA elimination), and S84N mutants (nearly WT efficiency for l-Ser elimination) displayed intermediate activity, all showing a preference for the anionic substrates, but generally attenuated compared with the native enzyme. Inhibition studies with l-erythro-β-hydroxyaspartate follow this trend, with both WT serine racemase and the S84N mutant being competitively inhibited, with K_i = 31 ± 1.5 μm and 1.5 ± 0.1 mm, respectively, and the S84D being inert to inhibition. Computational modeling pointed to a key role for residue Arg-135 in binding and properly positioning the l-THA and l-serine-O-sulfate substrates and the l-erythro-β-hydroxyaspartate inhibitor. Examination of available sequence data suggests that Arg-135 may have originated for l-THA-like β-elimination function in earlier evolutionary variants, and examination of available structural data suggests that a Ser⁸⁴-H₂O-Lys¹¹⁴ hydrogen-bonding network in human serine racemase lowers the pK_a of the Ser⁸⁴re-face base.

A novel serine racemase inhibitor suppresses neuronal over-activation in vivo

Serine racemase (SRR) is an enzyme that produces d-serine from l-serine. d-Serine acts as an endogenous coagonist of NMDA-type glutamate receptors (NMDARs), which regulate many physiological functions. Over-activation of NMDARs induces excitotoxicity, which is observed in many neurodegenerative disorders and epilepsy states. In our previous works on the generation of SRR gene knockout (Srr-KO) mice and its protective effects against NMDA- and Aβ peptide-induced neurodegeneration, we hypothesized that the regulation of NMDARs' over-activation by inhibition of SRR activity is one such therapeutic strategy to combat these disease states. In the previous study, we performed in silico screening to identify four compounds with inhibitory activities against recombinant SRR. Here, we synthesized 21 derivatives of candidate 1, one of four hit compounds, and performed screening by in vitro evaluations. The derivative 13J showed a significantly lower IC₅₀ value in vitro, and suppressed neuronal over-activation in vivo.

Magnesium and calcium ions differentially affect human serine racemase activity and modulate its quaternary equilibrium toward a tetrameric form

Serine racemase is the pyridoxal 5'-phosphate dependent enzyme that catalyzes both production and catabolism of d-serine, a co-agonist of the NMDA glutamate receptors. Mg²⁺, or, alternatively, Ca²⁺, activate human serine racemase by binding both at a specific site and - as ATP-metal complexes - at a distinct ATP binding site. We show that Mg²⁺ and Ca²⁺ bind at the metal binding site with a 4.5-fold difference in affinity, producing a similar thermal stabilization and partially shifting the dimer-tetramer equilibrium in favour of the latter. The ATP-Ca²⁺ complex produces a 2-fold lower maximal activation in comparison to the ATP-Mg²⁺ complex and exhibits a 3-fold higher EC₅₀. The co-presence of ATP and metals further stabilizes the tetramer. In consideration of the cellular concentrations of Mg²⁺ and Ca²⁺, even taking into account the fluctuations of the latter, these results point to Mg²⁺ as the sole physiologically relevant ligand both at the metal binding site and at the ATP binding site. The stabilization of the tetramer by both metals and ATP-metal complexes suggests a quaternary activation mechanism mediated by 5'-phosphonucleotides similar to that observed in the distantly related prokaryotic threonine deaminases. This allosteric mechanism has never been observed before in mammalian fold type II pyridoxal 5'-phosphate dependent enzymes.

Explicit treatment of active-site waters enhances quantum mechanical/implicit solvent scoring: Inhibition of CDK2 by new pyrazolo[1,5-a]pyrimidines

We present comprehensive testing of solvent representation in quantum mechanics (QM)-based scoring of protein-ligand affinities. To this aim, we prepared 21 new inhibitors of cyclin-dependent kinase 2 (CDK2) with the pyrazolo[1,5-a]pyrimidine core, whose activities spanned three orders of magnitude. The crystal structure of a potent inhibitor bound to the active CDK2/cyclin A complex revealed that the biphenyl substituent at position 5 of the pyrazolo[1,5-a]pyrimidine scaffold was located in a previously unexplored pocket and that six water molecules resided in the active site. Using molecular dynamics, protein-ligand interactions and active-site water H-bond networks as well as thermodynamics were probed. Thereafter, all the inhibitors were scored by the QM approach utilizing the COSMO implicit solvent model. Such a standard treatment failed to produce a correlation with the experiment (R² = 0.49). However, the addition of the active-site waters resulted in significant improvement (R² = 0.68). The activities of the compounds could thus be interpreted by taking into account their specific noncovalent interactions with CDK2 and the active-site waters. In summary, using a combination of several experimental and theoretical approaches we demonstrate that the inclusion of explicit solvent effects enhance QM/COSMO scoring to produce a reliable structure-activity relationship with physical insights. More generally, this approach is envisioned to contribute to increased accuracy of the computational design of novel inhibitors.

IDD388 Polyhalogenated Derivatives as Probes for an Improved Structure-Based Selectivity of AKR1B10 Inhibitors

Human enzyme aldo-keto reductase family member 1B10 (AKR1B10) has evolved as a tumor marker and promising antineoplastic target. It shares high structural similarity with the diabetes target enzyme aldose reductase (AR). Starting from the potent AR inhibitor IDD388, we have synthesized a series of derivatives bearing the same halophenoxyacetic acid moiety with an increasing number of bromine (Br) atoms on its aryl moiety. Next, by means of IC₅₀ measurements, X-ray crystallography, WaterMap analysis, and advanced binding free energy calculations with a quantum-mechanical (QM) approach, we have studied their structure-activity relationship (SAR) against both enzymes. The introduction of Br substituents decreases AR inhibition potency but improves it in the case of AKR1B10. Indeed, the Br atoms in ortho position may impede these drugs to fit into the AR prototypical specificity pocket. For AKR1B10, the smaller aryl moieties of MK181 and IDD388 can bind into the external loop A subpocket. Instead, the bulkier MK184, MK319, and MK204 open an inner specificity pocket in AKR1B10 characterized by a π-π stacking interaction of their aryl moieties and Trp112 side chain in the native conformation (not possible in AR). Among the three compounds, only MK204 can make a strong halogen bond with the protein (-4.4 kcal/mol, using QM calculations), while presenting the lowest desolvation cost among all the series, translated into the most selective and inhibitory potency AKR1B10 (IC₅₀ = 80 nM). Overall, SAR of these IDD388 polyhalogenated derivatives have unveiled several distinctive AKR1B10 features (shape, flexibility, hydration) that can be exploited to design novel types of AKR1B10 selective drugs.

Human serine racemase is allosterically modulated by NADH and reduced nicotinamide derivatives

Serine racemase catalyzes both the synthesis and the degradation of d-serine, an obligatory co-agonist of the glutamatergic NMDA receptors. It is allosterically controlled by adenosine triphosphate (ATP), which increases its activity around 7-fold through a co-operative binding mechanism. Serine racemase has been proposed as a drug target for the treatment of several neuropathologies but, so far, the search has been directed only toward the active site, with the identification of a few, low-affinity inhibitors. Following the recent observation that nicotinamide adenine dinucleotide (reduced form) (NADH) inhibits serine racemase, here we show that the inhibition is partial, with an IC₅₀ of 246 ± 63 μM, several-fold higher than NADH intracellular concentrations. At saturating concentrations of NADH, ATP binds with a 2-fold lower affinity and without co-operativity, suggesting ligand competition. NADH also reduces the weak activity of human serine racemase in the absence of ATP, indicating an additional ATP-independent inhibition mechanism. By dissecting the NADH molecule, we discovered that the inhibitory determinant is the N-substituted 1,4-dihydronicotinamide ring. Particularly, the NADH precursor 1,4-dihydronicotinamide mononucleotide exhibited a partial mixed-type inhibition, with a K_I of 18 ± 7 μM. Docking simulations suggested that all 1,4-dihydronicotinamide derivatives bind at the interdimeric interface, with the ring positioned in an unoccupied site next to the ATP-binding site. This newly recognized allosteric site might be exploited for the design of high-affinity serine racemase effectors to finely modulate d-serine homeostasis.

Ultimate Translation: Developing Therapeutics Targeting on N-Methyl-d-Aspartate Receptor

N-Methyl-d-aspartate receptors (NMDARs) are broadly distributed in the central nervous system (CNS), where they mediate excitatory signaling. NMDAR-mediated neurotransmission (NMDARMN) is the molecular engine of learning, memory and cognition, which are the basis for high cortical function. NMDARMN is also critically involved in the development and plasticity of CNS. Due to its essential and critical role, either over- or under-activation of NMDARMN can contribute substantially to the development of CNS disorders. The involvement of NMDARMN has been demonstrated in a variety of CNS disorders, including schizophrenia, depression, posttraumatic stress disorder, aging, mild cognitive impairment and Alzheimer's dementia, amyotrophic lateral sclerosis, and anti-NMDAR encephalitis. Several targets to "correct" or "reset" the NMDARMN in these CNS disorders have been identified and confirmed. With analogy to aminergic treatments, these targets include the glycine/d-serine co-agonist site, channel ionophore, glycine transporter-1, and d-amino acid oxidase. It is still early days in terms of developing novel therapeutics targeting the NMDAR. However, agents modulating NMDARMN hold promise as the next generation of CNS therapeutics.

Recent Progress in Treating Protein-Ligand Interactions with Quantum-Mechanical Methods

We review the first successes and failures of a “new wave” of quantum chemistry-based approaches to the treatment of protein/ligand interactions. These approaches share the use of “enhanced”, dispersion (D), and/or hydrogen-bond (H) corrected density functional theory (DFT) or semi-empirical quantum mechanical (SQM) methods, in combination with ensemble weighting techniques of some form to capture entropic effects. Benchmark and model system calculations in comparison to high-level theoretical as well as experimental references have shown that both DFT-D (dispersion-corrected density functional theory) and SQM-DH (dispersion and hydrogen bond-corrected semi-empirical quantum mechanical) perform much more accurately than older DFT and SQM approaches and also standard docking methods. In addition, DFT-D might soon become and SQM-DH already is fast enough to compute a large number of binding modes of comparably large protein/ligand complexes, thus allowing for a more accurate assessment of entropic effects.

Semiempirical Quantum-Chemical Orthogonalization-Corrected Methods: Benchmarks for Ground-State Properties

The semiempirical orthogonalization-corrected OMx methods (OM1, OM2, and OM3) go beyond the standard MNDO model by including additional interactions in the electronic structure calculation. When augmented with empirical dispersion corrections, the resulting OMx-Dn approaches offer a fast and robust treatment of noncovalent interactions. Here we evaluate the performance of the OMx and OMx-Dn methods for a variety of ground-state properties using a large and diverse collection of benchmark sets from the literature, with a total of 13035 original and derived reference data. Extensive comparisons are made with the results from established semiempirical methods (MNDO, AM1, PM3, PM6, and PM7) that also use the NDDO (neglect of diatomic differential overlap) integral approximation. Statistical evaluations show that the OMx and OMx-Dn methods outperform the other methods for most of the benchmark sets.

Cyclopropane derivatives as potential human serine racemase inhibitors: unveiling novel insights into a difficult target

d-Serine is the co-agonist of NMDA receptors and binds to the so-called glycine site. d-Serine is synthesized by human serine racemase (SR). Over activation of NMDA receptors is involved in many neurodegenerative diseases and, therefore, the inhibition of SR might represent a novel strategy for the treatment of these pathologies. SR is a very difficult target, with only few compounds so far identified exhibiting weak inhibitory activity. This study was aimed at the identification of novel SR inhibitor by mimicking malonic acid, the best-known SR inhibitor, with a cyclopropane scaffold. We developed, synthesized, and tested a series of cyclopropane dicarboxylic acid derivatives, complementing the synthetic effort with molecular docking. We identified few compounds that bind SR in high micromolar range with a lack of significant correlation between experimental and predicted binding affinities. The thorough analysis of the results can be exploited for the development of more potent SR inhibitors.

Enhanced semiempirical QM methods for biomolecular interactions

Recent successes and failures of the application of 'enhanced' semiempirical QM (SQM) methods are reviewed in the light of the benefits and backdraws of adding dispersion (D) and hydrogen-bond (H) correction terms. We find that the accuracy of SQM-DH methods for non-covalent interactions is very often reported to be comparable to dispersion-corrected density functional theory (DFT-D), while computation times are about three orders of magnitude lower. SQM-DH methods thus open up a possibility to simulate realistically large model systems for problems both in life and materials science with comparably high accuracy.