Prediction of drug binding affinities by comparative binding energy analysis

Comparative binding energy COMBINE analysis for understanding the binding determinants of type II dehydroquinase inhibitors

Herein we report comparative binding energy (COMBINE) analyses to derive quantitative structure-activity relationship (QSAR) models that help rationalize the determinants of binding affinity for inhibitors of type II dehydroquinase (DHQ2), the third enzyme of the shikimic acid pathway. Independent COMBINE models were derived for Helicobacter pylori and Mycobacterium tuberculosis DHQ2, which is an essential enzyme in both these pathogenic bacteria that has no counterpart in human cells. These studies quantify the importance of the hydrogen bonding interactions between the ligands and the water molecule involved in the DHQ2 reaction mechanism. They also highlight important differences in the ligand interactions with the interface pocket close to the active site that could provide guides for future inhibitor design.

From Hansch-Fujita Analysis to AFMoC: A Road to Structure-Based QSAR

Since the pioneering effort of Hansch and Fujita, quantitative structure-activity relationships (QSAR) have proved valuable in optimizing lead structures. Enriching classical 3D-QSAR analysis, which exploits the three-dimensional structure of ligands, with structural information of the target has helped to improve the interpretability of the derived models and to increase their predictive power. One such method is the Adaption of Fields for Molecular Comparison (AFMoC) approach where protein-specifically adapted knowledge-based pair-potentials are tailored to one particular protein by considering additional structural and energetic information about ligands. Here, we summarize applications of AFMoC, describe recent developments, and provide an outlook on how to improve the method.

Statistical estimation of the protein-ligand binding free energy based on direct protein-ligand interaction obtained by molecular dynamics simulation

We have developed a method for estimating protein-ligand binding free energy (DG) based on the direct protein-ligand interaction obtained by a molecular dynamics simulation. Using this method, we estimated the DG value statistically by the average values of the van der Waals and electrostatic interactions between each amino acid of the target protein and the ligand molecule. In addition, we introduced fluctuations in the accessible surface area (ASA) and dihedral angles of the protein-ligand complex system as the entropy terms of the DG estimation. The present method included the fluctuation term of structural change of the protein and the effective dielectric constant. We applied this method to 34 protein-ligand complex structures. As a result, the correlation coefficient between the experimental and calculated DG values was 0.81, and the average error of DG was 1.2 kcal/mol with the use of the fixed parameters. These results were obtained from a 2 nsec molecular dynamics simulation.

Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE)

BACKGROUND

The inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.

RESULTS

By considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.

CONCLUSIONS

These QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.

Residue-ligand interaction energy (ReLIE) on a receptor-dependent 3D-QSAR analysis of S- and NH-DABOs as non-nucleoside reverse transcriptase inhibitors

A series of 74 dihydroalkoxybenzyloxopyrimidines (DABOs), a class of highly potent non-nucleoside reverse transcriptase inhibitors (NNRTIs), was retrieved from the literature and studied by receptor-dependent (RD) three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis to derive RD-3D-QSAR models. The descriptors in this new method are the steric and electrostatic interaction energies of the protein-ligand complexes (per residue) simulated by molecular dynamics, an approach named Residue-Ligand Interaction Energy (ReLIE). This study was performed using a training set of 59 compounds and the MKC-442/RT complex structure as reference. The ReLIE-3D-QSAR models were constructed and evaluated by genetic algorithm (GA) and partial least squares (PLS). In the best equations, at least one term is related to one of the amino acid residues of the p51 subunit: Asn136, Asn137, Glu138, and Thr139. This fact implies the importance of interchain interaction (p66-p51) in the equations that best describe the structure-activity relationship for this class of compounds. The best equation shows q² = 0.660, SE(cv) = 0.500, r² = 0.930, and SEE = 0.226. The external predictive ability of this best model was evaluated using a test set of 15 compounds. In order to design more potent DABO analogues as anti-HIV/AIDS agents, substituents capable of interactions with residues like Ile94, Lys101, Tyr181, and Tyr188 should be selected. Also, given the importance of the conserved Asn136, this residue could become an attractive target for the design of novel NNRTIs with improved potency and increased ability to avoid the development of drug-resistant viruses.

2-(Alkyl/aryl)amino-6-benzylpyrimidin-4(3H)-ones as inhibitors of wild-type and mutant HIV-1: enantioselectivity studies

The single enantiomers of two pyrimidine-based HIV-1 non-nucleoside reverse transcriptase inhibitors, 1 (MC1501) and 2 (MC2082), were tested in both cellular and enzyme assays. In general, the R forms were more potent than their S counterparts and racemates and (R)-2 was more efficient than (R)-1 and the reference compounds, with some exceptions. Interestingly, (R)-2 displayed a faster binding to K103N RT with respect to WT RT, while (R)-1 showed the opposite behavior.

Computational prediction of metabolism: sites, products, SAR, P450 enzyme dynamics, and mechanisms

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Online chemical modeling environment (OCHEM): web platform for data storage, model development and publishing of chemical information

The Online Chemical Modeling Environment is a web-based platform that aims to automate and simplify the typical steps required for QSAR modeling. The platform consists of two major subsystems: the database of experimental measurements and the modeling framework. A user-contributed database contains a set of tools for easy input, search and modification of thousands of records. The OCHEM database is based on the wiki principle and focuses primarily on the quality and verifiability of the data. The database is tightly integrated with the modeling framework, which supports all the steps required to create a predictive model: data search, calculation and selection of a vast variety of molecular descriptors, application of machine learning methods, validation, analysis of the model and assessment of the applicability domain. As compared to other similar systems, OCHEM is not intended to re-implement the existing tools or models but rather to invite the original authors to contribute their results, make them publicly available, share them with other users and to become members of the growing research community. Our intention is to make OCHEM a widely used platform to perform the QSPR/QSAR studies online and share it with other users on the Web. The ultimate goal of OCHEM is collecting all possible chemoinformatics tools within one simple, reliable and user-friendly resource. The OCHEM is free for web users and it is available online at http://www.ochem.eu.

Structure-based quantitative structure–activity relationship modeling of estrogen receptor β-ligands

Background: A variety of chemotypes have been studied as estrogen receptor (ER) β-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure–activity relationship data and the x-ray structures of the ERβ ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ERβ-selective ligands that robust quantitative structure–activity relationship (QSAR) models be built. Methods/results: We employed a newly developed structure-based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure–activity relationship data and the 3D structural information of ERβ, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (>0.60) training-r2 and test set-R2 statistics. Conclusion: We have obtained an ensemble of predictive models of ERβ ligands that will be useful in the future discovery of novel ERβ-selective molecules.

Rigorous treatment of multispecies multimode ligand-receptor interactions in 3D-QSAR: CoMFA analysis of thyroxine analogs binding to transthyretin

For a rigorous analysis of the receptor-ligand binding, speciation of the ligands caused by ionization, tautomerism, covalent hydration, and dynamic stereoisomerism needs to be considered. Each species may bind in several orientations or conformations (modes), especially for flexible ligands and receptors. A thermodynamic description of the multispecies (MS), multimode (MM) binding events shows that the overall association constant is equal to the weighted sum of the sums of microscopic association constants of individual modes for each species, with the weights given by the unbound fractions of individual species. This expression is a prerequisite for a precise quantitative characterization of the ligand-receptor interactions in both structure-based and ligand-based structure-activity analyses. We have implemented the MS-MM correlation expression into the comparative molecular field analysis (CoMFA), which deduces a map of the binding site from structures and binding affinities of a ligand set, in the absence of experimental structural information on the receptor. The MS-MM CoMFA approach was applied to published data for binding to transthyretin of 28 thyroxine analogs, each forming up to four ionization species under physiological conditions. The published X-ray structures of several analogs, exhibiting multiple binding modes, served as templates for the MS-MM superposition of thyroxine analogs. Additional modes were generated for compounds with flexible alkyl substituents, to identify bound conformations. The results demonstrate that the MS-MM modification improved predictive abilities of the CoMFA models, even for the standard procedure with MS-MM selected species and modes. The predicted prevalences of individual modes and the generated receptor site model are in reasonable agreement with the available X-ray data. The calibrated model can help in the design of inhibitors of transthyretin amyloid fibril formation.

Computational prediction of structure-activity relationships for the binding of aminocyclitols to β-glucocerebrosidase

Glucocerebrosidase (GCase, acid β-Glucosidase) hydrolyzes the sphingolipid glucosylceramide into glucose and ceramide. Mutations in this enzyme lead to a lipid metabolism disorder known as Gaucher disease. The design of competitive inhibitors of GCase is a promising field of research for the design of pharmacological chaperones as new therapeutic agents. Using a series of recently reported molecules with experimental binding affinities for GCase in the nanomolar to micromolar range, we here report an extensive theoretical analysis of their binding mode. On the basis of molecular docking, molecular dynamics, and binding free energy calculations using the linear interaction energy method (LIE), we provide details on the molecular interactions supporting ligand binding in the different families of compounds. The applicability of other computational approaches, such as the COMBINE methodology, is also investigated. The results show the robustness of the standard parametrization of the LIE method, which reproduces the experimental affinities with a mean unsigned error of 0.7 kcal/mol. Several structure-activity relationships are established using the computational models here provided, including the identification of hot spot residues in the binding site. The models derived are envisaged as important tools in ligand-design programs for GCase inhibitors.

Design, synthesis, and biological activity of thiazole derivatives as novel influenza neuraminidase inhibitors

A series of novel influenza neuraminidase (NA) inhibitors based on thiazole core were synthesized and evaluated for their ability to inhibit NA of influenza A virus (H(3)N(2)). All compounds were synthesized in good yields starting from commercially available 2-amino-4-thiazole-acetic ester using a suitable synthetic strategy. These compounds showed moderate inhibitory activity against influenza A NA. The most potent compound of this series is compound 4d (IC(50) = 3.43 μM), which is about 20-fold less potent than oseltamivir, and could be used to design novel influenza NA inhibitors that exhibit increased activity based on thiazole ring.

Evaluation of a Set of C9 N-acyl Neu5Ac2en Mimetics as Viral Sialidase Selective Inhibitors

Identification of selective influenza viral sialidase inhibitors is highly desirable in order to minimize or avoid the adverse effects due to the possible inhibition of endogenous human sialidases. We recently reported the evaluation of C9 N-acyl Neu5Ac2en mimetics as probes for human sialidases. Herein, we describe the in vitro activity of the same set of C9 N-acyl Neu5Ac2en mimetics against sialidases expressed by influenza virus A/PR/8/34 (H1N1), A/Memphis/1/72 (H3N2), and A/Duck/313/78 (H5N3) strains. Compound 8 is identified as a promising starting point for the development of viral sialidase selective inhibitors. Multiple sequence alignment and molecular docking techniques are also performed to explore the plausible interaction of compound 8 with viral sialidases.

Quantum mechanical pairwise decomposition analysis of protein kinase B inhibitors: validating a new tool for guiding drug design

Quantum mechanical semiempirical comparative binding energy analysis calculations have been carried out for a series of protein kinase B (PKB) inhibitors derived from fragment- and structure-based drug design. These protein−ligand complexes were selected because they represent a consistent set of experimental data that includes both crystal structures and affinities. Seven scoring functions were evaluated based on both the PM3 and the AM1 Hamiltonians. The optimal models obtained by partial least-squares analysis of the aligned poses are predictive as measured by a number of standard statistical criteria and by validation with an external data set. An algorithm has been developed that provides residue-based contributions to the overall binding affinity. These residue-based binding contributions can be plotted in heat maps so as to highlight the most important residues for ligand binding. In the case of these PKB inhibitors, the maps show that Met166, Thr97, Gly43, Glu114, Ala116, and Val50, among other residues, play an important role in determining binding affinity. The interaction energy map makes it easy to identify the residues that have the largest absolute effect on ligand binding. The structure−activity relationship (SAR) map highlights residues that are most critical to discriminating between more and less potent ligands. Taken together the interaction energy and the SAR maps provide useful insights into drug design that would be difficult to garner in any other way.

Binding affinity prediction with property-encoded shape distribution signatures

We report the use of the molecular signatures known as "property-encoded shape distributions" (PESD) together with standard support vector machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore, a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (DeltaH/-TDeltaS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach, and further improvement in accuracy would require accounting for these components rigorously.

Rationalizing protein-ligand interactions for PTP1B inhibitors using computational methods

Protein tyrosine phosphatase 1B inhibitors were reported to have anti-diabetic properties and hence this enzyme has become interesting drug target in the recent time. Huge amount of data is available in public domain about the PTP1B inhibitors in the form of X-ray structures. This study is an attempt to transform this data into useful knowledge which can be directly used to design more effective protein tyrosine phosphatase inhibitors. In this study, we have built quantitative models for activity of co-crystallized protein tyrosine phosphatase inhibitors using two new approaches developed in our group, i.e. receptor-ligand interaction and Structure-based compound optimization, prioritization and evolution based on receptor-ligand interaction descriptors and residue-wise interaction energies as descriptors, respectively. These models have given insights into the receptor-ligand interactions essential for modulating the activity of PTP1B inhibitors. An external validation set of 22 molecules was used to test predictive power of these models on external set molecules.

Distinct interactions of 2'- and 3'-O-(N-methyl)anthraniloyl-isomers of ATP and GTP with the adenylyl cyclase toxin of Bacillus anthracis, edema factor

Anthrax disease is caused by the spore-forming bacterium, Bacillus anthracis. B. anthracis produces a calmodulin-activated adenylyl cyclase (AC) toxin, edema factor (EF). Through excessive cAMP accumulation EF disrupts host defence. In a recent study [Taha HM, Schmidt J, Göttle M, Suryanarayana S, Shen Y, Tang WJ, et al. Molecular analysis of the interaction of anthrax adenylyl cyclase toxin, edema factor, with 2'(3')-O-(N-(methyl)anthraniloyl)-substituted purine and pyrimidine nucleotides. Mol Pharmacol 2009;75:693-703] we showed that various 2'(3')-O-N-(methyl)anthraniloyl (MANT)-substituted nucleoside 5'-triphosphates are potent inhibitors (K(i) values in the 0.1-5 microM range) of purified EF. Upon interaction with calmodulin we observed efficient fluorescence resonance energy transfer (FRET) between tryptophan and tyrosine residues of EF and the MANT-group of MANT-ATP. Molecular modelling suggested that both the 2'- and 3'-MANT-isomers can bind to EF. The aim of the present study was to examine the effects of defined 2'- and 3'-MANT-isomers of ATP and GTP on EF. 3'-MANT-2'-deoxy-ATP inhibited EF more potently than 2'-MANT-3'-deoxy-ATP, whereas the opposite was the case for the corresponding GTP analogs. Calmodulin-dependent direct MANT fluorescence and FRET was much larger with 2'-MANT-3'-deoxy-ATP and 2'-MANT-3'-deoxy-GTP compared to the corresponding 3'-MANT-2'-deoxy-isomers and the 2'(3')-racemates. K(i) values of MANT-nucleotides for inhibition of catalysis correlated with K(d) values of MANT-nucleotides in FRET studies. Molecular modelling indicated different positioning of the MANT-group in 2'-MANT-3'-deoxy-ATP/GTP and 3'-MANT-2'-deoxy-ATP/GTP bound to EF. Collectively, EF interacts differentially with 2'- and 3'-MANT-isomers of ATP and GTP, indicative for conformational flexibility of the catalytic site and offering a novel approach for the development of potent and selective EF inhibitors. Moreover, our present study may serve as a general model of how to use MANT-nucleotide isomers for the analysis of the molecular mechanisms of nucleotide/protein interactions.

Key residues controlling binding of diverse ligands to human cytochrome P450 2A enzymes

Although the human lung cytochrome P450 2A13 (CYP2A13) and its liver counterpart cytochrome P450 2A6 (CYP2A6) are 94% identical in amino acid sequence, they metabolize a number of substrates with substantially different efficiencies. To determine differences in binding for a diverse set of cytochrome P450 2A ligands, we have measured the spectral binding affinities (K(D)) for nicotine, phenethyl isothiocyanate (PEITC), coumarin, 2'-methoxyacetophenone (MAP), and 8-methoxypsoralen. The differences in the K(D) values for CYP2A6 versus CYP2A13 ranged from 74-fold for 2'-methoxyacetophenone to 1.1-fold for coumarin, with CYP2A13 demonstrating the higher affinity. To identify active site amino acids responsible for the differences in binding of MAP, PEITC, and coumarin, 10 CYP2A13 mutant proteins were generated in which individual amino acids from the CYP2A6 active site were substituted into CYP2A13 at the corresponding position. Titrations revealed that substitutions at positions 208, 300, and 301 individually had the largest effects on ligand binding. The collective relevance of these amino acids to differential ligand selectivity was verified by evaluating binding to CYP2A6 mutant enzymes that incorporate several of the CYP2A13 amino acids at these positions. Inclusion of four CYP2A13 amino acids resulted in a CYP2A6 mutant protein (I208S/I300F/G301A/S369G) with binding affinities for MAP and PEITC much more similar to those observed for CYP2A13 than to those for CYP2A6 without altering coumarin binding. The structure-based quantitative structure-activity relationship analysis using COMBINE successfully modeled the observed mutant-ligand trends and emphasized steric roles for active site residues including four substituted amino acids and an adjacent conserved Leu(370).

Some novel insights into the binding of oseltamivir and zanamivir to H5N1 and N9 influenza virus neuraminidases: A homology modeling and flexible docking study

In the context of the recent pandemic threat by the worldwide spread of H5N1 avian influenza, novel insights into the mechanism of ligand binding and interaction between various inhibitors (zanamivir - ZMV, oseltamivir - OTV, 2,3-didehydro-2-deoxy-N-acetylneuraminic acid - DANA, peramivir - PMV) and neuraminidases (NA) are of vital importance for the structure-based design of new anti-viral drugs. To address this issue, three-dimensional models of H5N1-NA and N9-NA were generated by homology modeling. Traditional residues within the active site throughout the family of NA protein structures were found to be highly conserved in H5N1-NA. A subtle variation between lipophilic and hydrophilic environments in H5N1-NA with respect to N9-NA was observed, thus shedding more light on the high resistance of some H5N1 strains to various NA inhibitors. Based on these models, an ArgusLab4/AScore flexible docking study was performed. The conformational differences between OTV bound to H5N1-NA and OTV bound to N9-NA were structurally identified and quantified. A slight difference of less than 1 kcal mol-1 between the OTV-N9 and OTV-N1 binding free energies is in agreement with the experimentally predicted free energy difference. The conformational differences between ZMV and OTV bound to either H5N1-NA or N9-NA were structurally identified. The binding free energies of the ZMV complexes, being slightly higher than those of OTV, are not in agreement with what was previously proposed using homology modeling. The differences between ZMV and OTV are suggested to be ascribed to the presence/absence of Asn166 in the active cavity of ZMV/OTV in H5N1-NA, and to the presence/absence of Ser165 in the binding site of ZMV/OTV in N9-NA. The charge distribution was evaluated using the semi-empirical AM1 method. The trends of the AM1 charges of the ZMV and OTV side chains in the complexes deviate from those previously reported.

On the structure-based design of novel inhibitors of H5N1 influenza A virus neuraminidase (NA)

The structure-based design of novel H5N1 neuraminidase inhibitors is currently a research topic of vital importance owing to both a recent pandemic threat by the worldwide spread of H5N1 avian influenza and the high resistance of H5N1 virus to the most widely used commercial drug, oseltamivir-OTV (Tamiflu). A specific criterion used in this work for determining fully acceptable conformations of potential inhibitors is a previous experimental proposal of exploiting potential benefits for drug design offered by the '150-cavity' adjacent to the NA active site. Using the crystal structure of H5N1 NA (PDB ID: 2hty) as the starting point, in a set of 54 inhibitors previously proposed by modifying the side chains of oseltamivir, 4 inhibitors were identified using two different computational strategies (ArgusLab4.0.1, FlexX-E3.0.1) both to lower the binding free energy (BFE) of oseltamivir and to have partially acceptable conformations. These 4 oseltamivr structure-based analogues were found to adopt the most promising conformations by identifying the guanidinium side chain of Arg156 as a prospective partner for making polar contacts, but none of the modified 4-amino groups of oseltamivir in the 4 favorable conformations was found to make polar contacts with the guanidinium side chain of Arg156. Hence, the structures of two additional inhibitors were designed and shown to further lower the binding free energy of OTV relative to the previous 54 inhibitors. These two novel structures clearly suggest that it may be possible for a new substituent to be developed by functional modifications at position of the 4-amino group of oseltamivir in order to make polar contacts with the guanidinium side chain of Arg156, and thereby enhance the binding of a more potent inhibitor. Several standpoints of vital importance for designing novel structures of potentially more effective H5N1 NA inhibitors are established.

A new method for in-silico drug screening and similarity search using molecular dynamics maximum volume overlap (MD-MVO) method

We developed a new molecular dynamics simulation method for molecular overlapping (alignment) and ligand-based in-silico drug screening based on molecular similarity. The molecular system consists of the query compound and the other compound(s) selected from a compound library. The newly introduced intermolecular interaction between compounds is proportional to the molecular overlap instead of the van der Waals and Coulomb interactions between atoms of different molecules. This method was able to achieve both conformer generation of molecules and molecular overlapping (alignment) at the same time. After an energy minimization and following short-time MD simulation, the molecules in the system were overlapped with each other and the similarity between compounds was measured by the volume of the overlap. We applied this MD simulation method to ligand-based in-silico drug screening and found that it worked well for several targets.

Use of 3D QSAR models for database screening: a feasibility study

The applicability and scope of 3D QSAR methods (CoMFA, CoMSIA) to screen databases are examined. A protocol requiring minimal user intervention has been established to align training and test set molecules using FlexS. As model system isozymes of human carbonic anhydrase (hCA) are used, all results are exemplified studying affinity toward hCA II and selectivity between hCA I and II. The predictive power of the obtained models is assessed through prediction of 663 compounds not included in the training set and compared to 2D QSAR models derived from fragment (MACCS) or property (VSA) based descriptors. The predictive power is evaluated with respect to the following criteria: the numerical, concerning the absolute accuracy of prediction, and the categorical, characterizing the ability to assign a compound to the correct activity class.

The role of quantum mechanics in structure-based drug design

Herein we will focus on the use of quantum mechanics (QM) in drug design (DD) to solve disparate problems from scoring protein-ligand poses to building QM QSAR models. Through the variational principle of QM we know that we can obtain a more accurate representation of molecular systems than classical models, and while this is not a matter of debate, it still has not been shown that the expense of QM approaches is offset by improved accuracy in DD applications. Objectively validating the improved applicability and performance of QM over classical-based models in DD will be the focus of research in the coming years along with research on the conformational sampling problem as it relates to protein-ligand complexes.

Mechanism of inhibition of human secretory phospholipase A2 by flavonoids: rationale for lead design

The human secretory phospholipase A2 group IIA (PLA2-IIA) is a lipolytic enzyme. Its inhibition leads to a decrease in eicosanoids levels and, thereby, to reduced inflammation. Therefore, PLA2-IIA is of high pharmacological interest in treatment of chronic diseases such as asthma and rheumatoid arthritis. Quercetin and naringenin, amongst other flavonoids, are known for their anti-inflammatory activity by modulation of enzymes of the arachidonic acid cascade. However, the mechanism by which flavonoids inhibit Phospholipase A2 (PLA2) remained unclear so far. Flavonoids are widely produced in plant tissues and, thereby, suitable targets for pharmaceutical extractions and chemical syntheses. Our work focuses on understanding the binding modes of flavonoids to PLA2, their inhibition mechanism and the rationale to modify them to obtain potent and specific inhibitors. Our computational and experimental studies focused on a set of 24 compounds including natural flavonoids and naringenin-based derivatives. Experimental results on PLA2-inhibition showed good inhibitory activity for quercetin, kaempferol, and galangin, but relatively poor for naringenin. Several naringenin derivatives were synthesized and tested for affinity and inhibitory activity improvement. 6-(1,1-dimethylallyl)naringenin revealed comparable PLA2 inhibition to quercetin-like compounds. We characterized the binding mode of these compounds and the determinants for their affinity, selectivity, and inhibitory potency. Based on our results, we suggest C(6) as the most promising position of the flavonoid scaffold to introduce chemical modifications to improve affinity, selectivity, and inhibition of PLA2-IIA by flavonoids.