Efficient method for high-throughput virtual screening based on flexible docking: discovery of novel acetylcholinesterase inhibitors

A Comprehensive Review of Cholinesterase Modeling and Simulation

The present article reviews published efforts to study acetylcholinesterase and butyrylcholinesterase structure and function using computer-based modeling and simulation techniques. Structures and models of both enzymes from various organisms, including rays, mice, and humans, are discussed to highlight key structural similarities in the active site gorges of the two enzymes, such as flexibility, binding site location, and function, as well as differences, such as gorge volume and binding site residue composition. Catalytic studies are also described, with an emphasis on the mechanism of acetylcholine hydrolysis by each enzyme and novel mutants that increase catalytic efficiency. The inhibitory activities of myriad compounds have been computationally assessed, primarily through Monte Carlo-based docking calculations and molecular dynamics simulations. Pharmaceutical compounds examined herein include FDA-approved therapeutics and their derivatives, as well as several other prescription drug derivatives. Cholinesterase interactions with both narcotics and organophosphate compounds are discussed, with the latter focusing primarily on molecular recognition studies of potential therapeutic value and on improving our understanding of the reactivation of cholinesterases that are bound to toxins. This review also explores the inhibitory properties of several other organic and biological moieties, as well as advancements in virtual screening methodologies with respect to these enzymes.

Machine learning classification can reduce false positives in structure-based virtual screening

With the recent explosion in the size of libraries available for screening, virtual screening is positioned to assume a more prominent role in early drug discovery's search for active chemical matter. In typical virtual screens, however, only about 12% of the top-scoring compounds actually show activity when tested in biochemical assays. We argue that most scoring functions used for this task have been developed with insufficient thoughtfulness into the datasets on which they are trained and tested, leading to overly simplistic models and/or overtraining. These problems are compounded in the literature because studies reporting new scoring methods have not validated their models prospectively within the same study. Here, we report a strategy for building a training dataset (D-COID) that aims to generate highly compelling decoy complexes that are individually matched to available active complexes. Using this dataset, we train a general-purpose classifier for virtual screening (vScreenML) that is built on the XGBoost framework. In retrospective benchmarks, our classifier shows outstanding performance relative to other scoring functions. In a prospective context, nearly all candidate inhibitors from a screen against acetylcholinesterase show detectable activity; beyond this, 10 of 23 compounds have IC₅₀ better than 50 μM. Without any medicinal chemistry optimization, the most potent hit has IC₅₀ 280 nM, corresponding to K_i of 173 nM. These results support using the D-COID strategy for training classifiers in other computational biology tasks, and for vScreenML in virtual screening campaigns against other protein targets. Both D-COID and vScreenML are freely distributed to facilitate such efforts.

QSAR Models towards Cholinesterase Inhibitors for the Treatment of Alzheimer's Disease

Alzheimer's Disease (AD) is a multifactorial neurological syndrome with the combination of aging, genetic, and environmental factors triggering the pathological decline. Interestingly, the importance of the Acetylcholinesterase (AChE) enzyme has increased due to its involvement in the ß-amyloid peptide fibril formation during AD pathogenesis. In silico technique, QSAR has proven its usefulness in pharmaceutical research for the design/optimization of new chemical entities. Further, QSAR method advanced the scope of rational drug design and the search for the mechanism of drug action. It is a well-established fact that the chemical and pharmaceutical effects of a compound are closely related to its physico-chemical properties, which can be calculated by various methods from the compound structure. This chapter focuses on different Quantitative Structure-Activity Relationship (QSAR) studies carried out for a variety of cholinesterase inhibitors for the treatment of AD. These predictive models will be potentially used for further designing better and safer drugs against AD.

3D QSAR, pharmacophore and molecular docking studies of known inhibitors and designing of novel inhibitors for M18 aspartyl aminopeptidase of Plasmodium falciparum

Background

The Plasmodium falciparum M18 Aspartyl Aminopeptidase (PfM18AAP) is only aspartyl aminopeptidase which is found in the genome of P. falciparum and is essential for its survival. The PfM18AAP enzyme performs various functions in the parasite and the erythrocytic host such as hemoglobin digestion, erythrocyte invasion, parasite growth and parasite escape from the host cell. It is a valid target to develop antimalarial drugs. In the present work, we employed 3D QSAR modeling, pharmacophore modeling, and molecular docking to identify novel potent inhibitors that bind with M18AAP of P. falciparum.

Results

The PLSR QSAR model showed highest value for correlation coefficient r² (88 %) and predictive correlation coefficient (pred_r2) =0.6101 for external test set among all QSAR models. The pharmacophore modeling identified DHRR (one hydrogen donor, one hydrophobic group, and two aromatic rings) as an essential feature of PfM18AAP inhibitors. The combined approach of 3D QSAR, pharmacophore, and structure-based molecular docking yielded 10 novel PfM18AAP inhibitors from ChEMBL antimalarial library, 2 novel inhibitors from each derivative of quinine, chloroquine, 8-aminoquinoline and 10 novel inhibitors from WHO antimalarial drugs. Additionally, high throughput virtual screening identified top 10 compounds as antimalarial leads showing G-scores -12.50 to -10.45 (in kcal/mol), compared with control compounds(G-scores -7.80 to -4.70) which are known antimalarial M18AAP inhibitors (AID743024). This result indicates these novel compounds have the best binding affinity for PfM18AAP.

Conclusion

The 3D QSAR models of PfM18AAP inhibitors provided useful information about the structural characteristics of inhibitors which are contributors of the inhibitory potency. Interestingly, In this studies, we extrapolate that the derivatives of quinine, chloroquine, and 8-aminoquinoline, for which there is no specific target has been identified till date, might show the antimalarial effect by interacting with PfM18AAP.

Electronic supplementary material

The online version of this article (doi:10.1186/s12900-016-0063-7) contains supplementary material, which is available to authorized users.

Docking Screens for Novel Ligands Conferring New Biology

It is now plausible to dock libraries of 10 million molecules against targets over several days or weeks. When the molecules screened are commercially available, they may be rapidly tested to find new leads. Although docking retains important liabilities (it cannot calculate affinities accurately nor even reliably rank order high-scoring molecules), it can often can distinguish likely from unlikely ligands, often with hit rates above 10%. Here we summarize the improvements in libraries, target quality, and methods that have supported these advances, and the open access resources that make docking accessible. Recent docking screens for new ligands are sketched, as are the binding, crystallographic, and in vivo assays that support them. Like any technique, controls are crucial, and key experimental ones are reviewed. With such controls, docking campaigns can find ligands with new chemotypes, often revealing the new biology that may be docking's greatest impact over the next few years.

Dual binding site and selective acetylcholinesterase inhibitors derived from integrated pharmacophore models and sequential virtual screening

In this study, we have employed in silico methodology combining double pharmacophore based screening, molecular docking, and ADME/T filtering to identify dual binding site acetylcholinesterase inhibitors that can preferentially inhibit acetylcholinesterase and simultaneously inhibit the butyrylcholinesterase also but in the lesser extent than acetylcholinesterase. 3D-pharmacophore models of AChE and BuChE enzyme inhibitors have been developed from xanthostigmine derivatives through HypoGen and validated using test set, Fischer's randomization technique. The best acetylcholinesterase and butyrylcholinesterase inhibitors pharmacophore hypotheses Hypo1_A and Hypo1_B, with high correlation coefficient of 0.96 and 0.94, respectively, were used as 3D query for screening the Zinc database. The screened hits were then subjected to the ADME/T and molecular docking study to prioritise the compounds. Finally, 18 compounds were identified as potential leads against AChE enzyme, showing good predicted activities and promising ADME/T properties.

Acetylshikonin, a Novel AChE Inhibitor, Inhibits Apoptosis via Upregulation of Heme Oxygenase-1 Expression in SH-SY5Y Cells

Acetylcholinesterase inhibitors are prominent alternative in current clinical treatment for AD patients. Therefore, there is a continued need to search for novel AChEIs with good clinical efficacy and less side effects. By using our in-house natural product database and AutoDock Vina as a tool in docking study, we have identified twelve phytochemicals (emodin, aloe-emodin, chrysophanol, and rhein in Rhei Radix Et Rhizoma; xanthotoxin, phellopterin, alloisoimperatorin, and imperatorin in Angelicae dahuricae Radix; shikonin, acetylshikonin, isovalerylshikonin, and β,β-dimethylacrylshikonin in Arnebiae Radix) as candidates of AChEIs that were not previously reported in the literature. In addition to AChEI activity, a series of cell-based experiments were conducted for the investigation of their neuroprotective activities. We found that acetylshikonin and its derivatives prevented apoptotic cell death induced by hydrogen peroxide in human and rat neuronal SH-SY5Y and PC12 cells at 10 μM. We showed that acetylshikonin exhibited the most potent antiapoptosis activity through the inhibition of the generation of reactive oxygen species as well as protection of the loss of mitochondria membrane potential. Furthermore, we identified for the first time that the upregulation of heme oxygenase 1 by acetylshikonin is a key step mediating its antiapoptotic activity from oxidative stress in SH-SY5Y cells.

Exploring different virtual screening strategies for acetylcholinesterase inhibitors

The virtual screening problems associated with acetylcholinesterase (AChE) inhibitors were explored using multiple shape, and structure-based modeling strategies. The employed strategies include molecular docking, similarity search, and pharmacophore modeling. A subset from directory of useful decoys (DUD) related to AChE inhibitors was considered, which consists of 105 known inhibitors and 3732 decoys. Statistical quality of the models was evaluated by enrichment factor (EF) metrics and receiver operating curve (ROC) analysis. The results revealed that electrostatic similarity search protocol using EON (ET_combo) outperformed all other protocols with outstanding enrichment of >95% in top 1% and 2% of the dataset with an AUC of 0.958. Satisfactory performance was also observed for shape-based similarity search protocol using ROCS and PHASE. In contrast, the molecular docking protocol performed poorly with enrichment factors <30% in all cases. The shape- and electrostatic-based similarity search protocol emerged as a plausible solution for virtual screening of AChE inhibitors.

A docking-based receptor library of antibiotics and its novel application in predicting chronic mixture toxicity for environmental risk assessment

As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure-activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R (2)) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.

Analysis of structure-based virtual screening studies and characterization of identified active compounds

Structure-based virtual screening makes explicit or implicit use of 3D target structure information to detect novel active compounds. Results of nearly 300 currently available original applications have been analyzed to characterize the state-of-the-art in this field. Compound selection from docking calculations is much influenced by subjective criteria. Although submicromolar compounds are identified, the majority of docking hits are only weakly potent. However, only a small percentage of docking hits can be reproduced by ligand-based methods. When docking calculations identify potent hits, they often originate from specialized compound sources (e.g., pharmaceutical compound decks or target-focused libraries) and also display a notable bias towards kinase targets. Structure-based virtual screening is the dominant approach to computational hit identification. Docking calculations frequently identify active compounds. Limited accuracy of compound scoring and ranking currently presents a major caveat of the approach that is often compensated for by chemical intuition and knowledge.

Catechol alkenyls from Semecarpus anacardium: acetylcholinesterase inhibition and binding mode predictions

ETHNOPHARMACOLOGICAL RELEVANCE

The fruits of Semecarpus anacardium L. f. (Anacardiaceae) are used in Ayurvedic medicine and also in Iranian Traditional Medicine for various indications, among those for retarding and treatment of dementia.

AIM OF THE STUDY

The severity of Alzheimer's disease obviously correlates with a cholinergic deficit. In a screening for acetylcholinesterase (AChE) inhibitory activity, an extract from the fruit resin of Semecarpus anacardium was among the most active ones. Thus, the aim of this study was to isolate the active compounds and to investigate them in detail. Their binding mode to the active site of AChE was investigated by in silico docking experiments.

MATERIALS AND METHODS

From a dichloromethane extract in an activity-guided fractionation the active compounds were isolated under use of different chromatographic techniques. Their structures were unambiguously identified by one and two-dimensional (1)H and (13)C NMR spectroscopy and mass spectrometry and their cholinesterase inhibitory activities were determined by a microplate assay. In order to compare the 3D active sites of AChE from Torpedo californica (TcAChE) and from Electrophorus electricus (EeAChE), three files from the Protein Data Bank (PDB) were used and for docking experiments, GOLD 3.1 software was employed. The concentrations of active compounds in the extract and the fruits were determined by HPLC analysis.

RESULTS

The active compounds were determined as 1',2'-dihydroxy-3'-pentadec-8-enylbenzene (A) and 1',2'-dihydroxy-3'-pentadeca-8,11-dienylbenzene (B). Their IC(50) values in an in vitro assay on AChE inhibition were determined as 12 and 34 μg/mL, respectively, while they were not active in the inhibition of butyrylcholinesterase (BChE). In silico docking experiments showed a similar bioactivity for compounds A and B. The concentration of compounds A and B in the fruits was 1.85% and 1.88%, respectively.

CONCLUSION

In the search for the active principle of the fruit resin of Semecarpus anacardium, compounds A and B were identified as two selective inhibitors for AChE versus BChE.

Targeting acetylcholinesterase: identification of chemical leads by high throughput screening, structure determination and molecular modeling

Acetylcholinesterase (AChE) is an essential enzyme that terminates cholinergic transmission by rapid hydrolysis of the neurotransmitter acetylcholine. Compounds inhibiting this enzyme can be used (inter alia) to treat cholinergic deficiencies (e.g. in Alzheimer's disease), but may also act as dangerous toxins (e.g. nerve agents such as sarin). Treatment of nerve agent poisoning involves use of antidotes, small molecules capable of reactivating AChE. We have screened a collection of organic molecules to assess their ability to inhibit the enzymatic activity of AChE, aiming to find lead compounds for further optimization leading to drugs with increased efficacy and/or decreased side effects. 124 inhibitors were discovered, with considerable chemical diversity regarding size, polarity, flexibility and charge distribution. An extensive structure determination campaign resulted in a set of crystal structures of protein-ligand complexes. Overall, the ligands have substantial interactions with the peripheral anionic site of AChE, and the majority form additional interactions with the catalytic site (CAS). Reproduction of the bioactive conformation of six of the ligands using molecular docking simulations required modification of the default parameter settings of the docking software. The results show that docking-assisted structure-based design of AChE inhibitors is challenging and requires crystallographic support to obtain reliable results, at least with currently available software. The complex formed between C5685 and Mus musculus AChE (C5685•mAChE) is a representative structure for the general binding mode of the determined structures. The CAS binding part of C5685 could not be structurally determined due to a disordered electron density map and the developed docking protocol was used to predict the binding modes of this part of the molecule. We believe that chemical modifications of our discovered inhibitors, biochemical and biophysical characterization, crystallography and computational chemistry provide a route to novel AChE inhibitors and reactivators.

Electronic structure and PCA analysis of covalent and non-covalent acetylcholinesterase inhibitors

Hartree-Fock and density functional methods were used to analyze electronic and structural properties of known drugs to evaluate the influence of these data on acetylcholinesterase inhibition. The energies of the frontier orbitals and the distances between the more acidic hydrogen species were investigated to determine their contributions to the activity of a group of acetylcholinesterase inhibitors. Electrostatic potential maps indicated suitable sites for drugs-enzyme interactions. In this study, the structural, electronic and spatial properties of nine drugs with known inhibitory effects on acetylcholinesterase were examined. The data were obtained based on calculations at the B3LYP/6-31 + G(d,p) level. Multivariate principal components analysis was applied to 18 parameters to determine the pharmacophoric profile of acetylcholinesterase inhibitors. Desirable features for acetylcholinesterase inhibitor molecules include aromatic systems or groups that simulate the surface electrostatic potential of aromatic systems and the presence of a sufficient number of hydrogen acceptors and few hydrogen donors. PCA showed that electronic properties, including the HOMO-1 orbital energy, logP and aromatic system quantity, as well as structural data, such as volume, size and H-H distance, are the most significant properties.

Virtual screening of compound libraries

During the last decade, Virtual Screening (VS) has definitively established itself as an important part of the drug discovery and development process. VS involves the selection of likely drug candidates from large libraries of chemical structures by using computational methodologies, but the generic definition of VS encompasses many different methodologies. This chapter provides an introduction to the field by reviewing a variety of important aspects, including the different types of virtual screening methods, and the several steps required for a successful virtual screening campaign within a state-of-the-art approach, from target selection to postfilter application. This analysis is further complemented with a small collection important VS success stories.

Butyrylcholinesterase for protection from organophosphorus poisons: catalytic complexities and hysteretic behavior

Butyrylcholinesterase is a promiscuous enzyme that displays complex kinetic behavior. It is toxicologically important because it detoxifies organophosphorus poisons (OP) by making a covalent bond with the OP. The OP and the butyrylcholinesterase are both inactivated in the process. Inactivation of butyrylcholinesterase has no adverse effects. However, inactivation of acetylcholinesterase in nerve synapses can be lethal. OP-inhibited butyrylcholinesterase and acetylcholinesterase can be reactivated with oximes provided the OP has not aged. Strategies for preventing the toxicity of OP include (a) treatment with an OP scavenger, (b) reaction of non-aged enzyme with oximes, (c) reactivation of aged enzyme, (d) slowing down aging with peripheral site ligands, and (e) design of mutants that rapidly hydrolyze OP. Option (a) has progressed through phase I clinical trials with human butyrylcholinesterase. Option (b) is in routine clinical use. The others are at the basic research level. Butyrylcholinesterase displays complex kinetic behavior including activation by positively charged esters, ability to hydrolyze amides, and a lag time (hysteresis) preceding hydrolysis of benzoylcholine and N-methylindoxyl acetate. Mass spectrometry has identified new OP binding motifs on tyrosine and lysine in proteins that have no active site serine. It is proposed, but not yet proven, that low dose exposure involves OP modification of proteins that have no active site serine.

Modeling effects of oxyanion hole on the ester hydrolysis catalyzed by human cholinesterases

Molecular dynamics (MD) simulations and hydrogen bonding energy (HBE) calculations have been performed on the prereactive enzyme-substrate complexes (ES), transition states (TS1), and intermediates (INT1) for acetylcholinesterase (AChE)-catalyzed hydrolysis of acetylcholine (ACh), butyrylcholinesterase (BChE)-catalyzed hydrolysis of ACh, and BChE-catalyzed hydrolysis of (+)/(-)-cocaine to examine the protein environmental effects on the catalytic reactions. The hydrogen bonding of cocaine with the oxyanion hole of BChE is found to be remarkably different from that of ACh with AChE/BChE. Whereas G121/G116, G122/G117, and A204/A199 of AChE/BChE all can form hydrogen bonds with ACh to stabilize the transition state during the ACh hydrolysis, BChE only uses G117 and A199 to form hydrogen bonds with cocaine. The change of the estimated total HBE from ES to TS1 is ca. -5.4/-4.4 kcal/mol for AChE/BChE-catalyzed hydrolysis of ACh and ca. -1.7/-0.8 kcal/mol for BChE-catalyzed hydrolysis of (+)/(-)-cocaine. The remarkable difference of approximately 3 to 5 kcal/mol reveals that the oxyanion hole of AChE/BChE can lower the energy barrier of the ACh hydrolysis significantly more than that of BChE for the cocaine hydrolysis. These results help to understand why the catalytic activity of AChE against ACh is considerably higher than that of BChE against cocaine and provides valuable clues on how to improve the catalytic activity of BChE against cocaine.

Taspine: bioactivity-guided isolation and molecular ligand-target insight of a potent acetylcholinesterase inhibitor from Magnolia x soulangiana

A bioactivity-guided approach was taken to identify the acetylcholinesterase (AChE, EC 3.1.1.7) inhibitory agent in a Magnolia x soulangiana extract using a microplate enzyme assay with Ellman's reagent. This permitted the isolation of the alkaloids taspine (1) and (-)-asimilobine (2), which were detected for the first time in this species. Compound 1 showed a significantly higher effect on AChE than the positive control galanthamine and selectively inhibited the enzyme in a long-lasting and concentration-dependent fashion with an IC(50) value of 0.33 +/- 0.07 muM. Extensive molecular docking studies were performed with human and Torpedo californica-AChE employing Gold software to rationalize the binding interaction. The results suggested ligand 1 to bind in an alternative binding orientation when compared to galanthamine. While this is located in close vicinity to the catalytic amino acid triad, the 1-AChE complex was found to be stabilized by (i) sandwich-like pi-stacking interactions between the planar aromatic ligand (1) and the Trp84 and Phe330 of the enzyme, (ii) an esteratic site anchoring with the amino side chain, and (iii) a hydrogen-bonding network.

Minimizing false positives in kinase virtual screens

In spite of recent improvements in docking and scoring methods, high false-positive rates remain a common issue in structure-based virtual screening. In this study, the distinctive features of false positives in kinase virtual screens were investigated. A series of retrospective virtual screens on kinase targets was performed on specifically designed test sets, each combining true ligands and experimentally confirmed inactive compounds. A systematic analysis of the docking poses generated for the top-ranking compounds highlighted key aspects differentiating true hits from false positives. The most recurring feature in the poses of false positives was the absence of certain key interactions known to be required for kinase binding. A systematic analysis of 444 crystal structures of ligand-bound kinases showed that at least two hydrogen bonds between the ligand and the backbone protein atoms in the kinase hinge region are present in 90% of the complexes, with very little variability across targets. Closer inspection showed that when the two hydrogen bonds are present, one of three preferred hinge-binding motifs is involved in 96.5% of the cases. Less than 10% of the false positives satisfied these two criteria in the minimized docking poses generated by our standard protocol. Ligand conformational artifacts were also shown to contribute to the occurrence of false positives in a number of cases. Application of this knowledge in the form of docking constraints and post-processing filters provided consistent improvements in virtual screening performance on all systems. The false-positive rates were significantly reduced and the enrichment factors increased by an average of twofold. On the basis of these results, a generalized two-step protocol for virtual screening on kinase targets is suggested.

Collection and preparation of molecular databases for virtual screening

Drug discovery and development research is undergoing a paradigm shift from a linear and sequential nature of the various steps involved in the drug discovery process of the past to the more parallel approach of the present, due to a lack of sufficient correlation between activities estimated by in vitro and in vivo assays. This is attributed to the non-drug-likeness of the lead molecules, which has often been detected at advanced drug development stages. Thus a striking aspect of this paradigm shift has been early/parallel in silico prioritization of drug-like molecular databases (also database pre-processing), in addition to prioritizing compounds with high affinity and selectivity for a protein target. In view of this, a drug-like database useful for virtual screening has been created by prioritizing molecules from 36 catalog suppliers, using our recently derived binary QSAR based drug-likeness model as a filter. The performance of this model was assessed by a comparative evaluation with respect to commonly used filters implemented by the ZINC database. Since the model was derived considering all the limitations that have plagued the existing rules and models, it performs better than the existing filters and thus the molecules prioritized by this filter represent a better subset of drug-like compounds. The application of this model on exhaustive subsets of 4,972,123 molecules, many of which have passed the ZINC database filters for drug-likeness, led to a further prioritization of 2,920,551 drug-like molecules. This database may have a great potential for in silico virtual screening for discovering molecules, which may survive the later stages of the drug development research.

Effective handling of induced-fit motion in flexible docking

For structure-based drug design, where various ligand structures need to be docked to a target protein structure, a docking method that can handle conformational flexibility of not only the ligand, but also the protein, is indispensable. We have developed a simple and effective approach for dealing with the local induced-fit motion of the target protein, and implemented it in our docking tool, ADAM. Our approach efficiently combines the following two strategies: a vdW-offset grid in which the protein cavity is enlarged uniformly, and structure optimization allowing the motion of ligand and protein atoms. To examine the effectiveness of our approach, we performed docking validation studies, including redocking in 18 test cases and foreign-docking, in which various ligands from foreign crystal structures of complexes are docked into a target protein structure, in 22 cases (on five target proteins). With the original ADAM, the correct docking modes (RMSD < 2.0 A) were not present among the top 20 models in one case of redocking and four cases of foreign-docking. When the handling of induced-fit motion was implemented, the correct solutions were acquired in all 40 test cases. In foreign-docking on thymidine kinase, the correct docking modes were obtained as the top-ranked solutions for all 10 test ligands by our combinatorial approach, and this appears to be the best result ever reported with any docking tool. The results of docking validation have thus confirmed the effectiveness of our approach, which can provide reliable docking models even in the case of foreign-docking, where conformational change of the target protein cannot be ignored. We expect that this approach will contribute substantially to actual drug design, including virtual screening.

A convenient preparation of xanthene dyes

A facile synthetic route utilizing readily available reagents affords a series of regioisomerically pure xanthene dye derivatives. Advantages include relatively mild conditions and good to excellent yields. Nonpolar, highly crystalline intermediates are isolable by standard chromatographic techniques. The intermediates are in the requisite xanthene oxidation state, thus avoiding the need for relatively inefficient oxidation chemistry and/or harsh conditions. During the course of this work, a new boron-mediated 1,2-aryl migration reaction was discovered.

Evaluation of binary QSAR models derived from LUDI and MOE scoring functions for structure based virtual screening

In today's world of high-throughput in silico screening, the development of virtual screening methodologies to prioritize small molecules as new chemical entities (NCEs) for synthesis is of current interest. Among several approaches to virtual screening, structure-based virtual screening has been considered the most effective. However the problems associated with the ranking of potential solutions in terms of scoring functions remains one of the major bottlenecks in structure-based virtual screening technology. It has been suggested that scoring functions may be used as filters for distinguishing binders from nonbinders instead of accurately predicting their binding free energies. Subsequently, several improvements have been made in this area, which include the use of multiple rather than single scoring functions and application of either consensus or multivariate statistical methods or both to improve the discrimination between binders and nonbinders. In view of it, the discriminative ability (distinguishing binders from nonbinders) of binary QSAR models derived using LUDI and MOE scoring functions has been compared with the models derived by Jacobbsson et al. on five data sets viz. estrogen receptor alphamimics (ERalpha_mimics), estrogen receptor alphatoxins (ERalpha_toxins), matrix metalloprotease 3 inhibitors (MMP-3), factor Xa inhibitors (fXa), and acetylcholine esterase inhibitors (AChE). The overall analyses reveal that binary QSAR is comparable to the PLS discriminant analysis, rule-based, and Bayesian classification methods used by Jacobsson et al. Further the scoring functions implemented in LUDI and MOE can score a wide range of protein-ligand interactions and are comparable to the scoring functions implemented in ICM and Cscore. Thus the binary QSAR models derived using LUDI and MOE scoring functions may be useful as a preliminary screening layer in a multilayered virtual screening paradigm.

Discovery of acetylcholinesterase peripheral anionic site ligands through computational refinement of a directed library

The formation of beta-amyloid plaques in the brain is a key neurodegenerative event in Alzheimer's disease. Small molecules capable of binding to the peripheral anionic site of acetylcholinesterase (AChE) have been shown to inhibit the AChE-induced aggregation of the beta-amyloid peptide. Using the combination of a computational docking model and experimental screening, five compounds that completely blocked the amyloidogenic effect of AChE were rapidly identified from an approximately 200-member library of compounds designed to disrupt protein-protein interactions. Critical to this docking model was the inclusion of two explicit water molecules that are tightly bound to the enzyme. Interestingly, none of the tested compounds inhibited the related enzyme butyrylcholinesterase (BuChE) up to their aqueous solubility limits. These compounds are among the most potent inhibitors of amyloid beta-peptide aggregation and are equivalent only to propidium, a well-characterized AChE peripheral anionic site binder and aggregation inhibitor.

Starting Point to Molecular Design: Efficient Automated 3D Model Builder Key3D

Obtaining three-dimensional (3D) structures from structural formulae is a crucial process in molecular design. We have developed a new 3D model builder, Key3D, in which the simplified distance geometry technique and structure optimization based on the MMFF force field are combined. In an evaluation study using 598 crystal structures, the high performance and accuracy of Key3D were demonstrated. In the "flexible-fitting" test, which is focused on practical usefulness in the molecular design process, 88% of the Key3D structures acceptably reproduced the reference crystal structures (root-mean-square deviation <0.6 A) upon rotation of acyclic bonds. These results indicate that Key3D will be very effective in providing starting points for practical molecular design.