Discovery of Novel GSK-3β Inhibitors Using Pharmacophore and Virtual Screening Studies

Glycogen synthase kinase-3β (GSK-3β) is a kinase family enzyme and an emerged target for the treatment of various diseases. A total of 23 structurally diverse flavonoid inhibitors were used to generate pharmacophore models using HypoGen algorithm. The hypotheses Hypo1 was considered as a best model which consists of three features: one hydrophobic and two aromatic ring features. The Hypo1 pharmacophore model was employed as a query to screen NCI and natural compound databases to discover novel potential lead compounds. In addition, molecular docking studies were carried out with 596 compounds from screening studies. NSC230353, NSC66454, NSC159593, and NSC156759 from NCI database and STOCK1N-81808, ZINC02159818, ZINC04042470, and ZINC72326235 from natural compound database were identified as potential GSK-3β inhibitors.

Targeting Heat Shock Proteins 60 and 70 of Toxoplasma gondii as a Potential Drug Target: In Silico Approach

Heat shock proteins (Hsps) 60 and 70 are postulated as a potential drug target for toxoplasmosis due to its importance in the developmental and survival of Toxoplasma gondii (T. gondii). As of today, there have been no reports on three-dimensional (3D) structure of Hsp60 and Hsp70 deposited in the Brookhaven Protein Data Bank. Hence, this study was conducted to predict 3D structures for Hsp60 and Hsp70 in T. gondii by homology modeling. Selection of the best predicted model was done based on multiple scoring functions. In addition, virtual screening was performed to short-list chemical compounds from the National Cancer Institute (NCI) Diversity Set III in search of potential inhibitor against Hsp60 and Hsp70 in T. gondii. Prior to virtual screening, binding sites of Hsp60 and Hsp70 were predicted using various servers and were used as the center in docking studies. The Hsps were docked against known natural ligands to validate the method used in estimating free energy of binding (FEB) and possible interactions between ligand and protein. Virtual screening was performed with a total of 1560 compounds from the NCI Diversity Set III. The compounds were ranked subsequently according to their FEB. Molecular basis of interactions of the top five ranked compounds was investigated using Ligplot⁺. The major interactions exhibited were hydrogen bonding and hydrophobic interactions in binding to Hsp60 and Hsp70. The results obtained provided information and guidelines for the development of inhibitors for Hsp60 and Hsp70 in T. gondii.

Performance evaluation of structure based and ligand based virtual screening methods on ten selected anti-cancer targets

Virtual screening has become an important tool in drug discovery process. Structure based and ligand based approaches are generally used in virtual screening process. To date, several benchmark sets for evaluating the performance of the virtual screening tool are available. In this study, our aim is to compare the performance of both structure based and ligand based virtual screening methods. Ten anti-cancer targets and their corresponding benchmark sets from 'Demanding Evaluation Kits for Objective In silico Screening' (DEKOIS) library were selected. X-ray crystal structures of protein-ligand complexes were selected based on their resolution. Openeye tools such as FRED, vROCS were used and the results were carefully analyzed. At EF1%, vROCS produced better results but at EF5% and EF10%, both FRED and ROCS produced almost similar results. It was noticed that the enrichment factor values were decreased while going from EF1% to EF5% and EF10% in many cases.

Discovery of novel nonpeptide allosteric inhibitors interrupting the interaction of CDK2/cyclin A3 by virtual screening and bioassays

Serine/threonine-specific cyclin-dependent kinases (CDKs) are key regulatory elements in eukaryotic cell cycle progression, and the dysregulation of CDKs has been implicated in cancers. Therefore, CDKs have been identified as anti-cancer targets for the development of small-molecule drugs. In this Letter, virtual screening and biological evaluation were performed to identify novel lead structures that allosterically disrupt the interaction between CDK2 and cyclin A3, which are directed toward a noncatalytic binding pocket of CDK2. Ultimately, B2 was identified as exhibiting superior CDK2/cyclin A3 inhibition activity. In addition, our results indicated that B2 exhibited antiproliferative activities against a broad spectrum of human cancer cell lines. Significantly, B2 certainly interrupted the interaction between CDK2 and cyclin A3 and exhibited a concentration-dependent trend. In summary, our results suggest that B2 is the first effective allosteric chemical small-molecule CDK2 inhibitor to be discovered, and further lead optimization may result in a series of novel anti-CDK2 agents.

Flavonoids as Multi-target Inhibitors for Proteins Associated with Ebola Virus: In Silico Discovery Using Virtual Screening and Molecular Docking Studies

Ebola virus is a single-stranded, negative-sense RNA virus that causes severe hemorrhagic fever in humans and non-human primates. This virus is unreceptive to a large portion of the known antiviral drugs, and there is no valid treatment as on date for disease created by this pathogen. Looking into its ability to create a pandemic scenario across globe, there is an utmost need for new drugs and therapy to combat this life-threatening infection. The current study deals with the evaluation of the inhibitory activity of flavonoids against the four selected Ebola virus receptor proteins, using in silico studies. The viral proteins VP40, VP35, VP30 and VP24 were docked with small molecules obtained from flavonoid class and its derivatives and evaluated on the basis of energetics, stereochemical considerations and pharmacokinetic properties to identify potential lead compounds. The results showed that both top-ranking screened flavonoids, i.e., Gossypetin and Taxifolin, showed better docking scores and binding energies in all the EBOV receptors when compared to those of the reported compound. All the screened flavonoids have known antiviral activity, acceptable pharmacokinetic properties and are being used on human and thus can be taken as anti-Ebola therapy without the time lag for clinical trial.

Virtual screening of a milk peptide database for the identification of food-derived antimicrobial peptides

SCOPE

Milk provides a wide range of bioactive substances, such as antimicrobial peptides and proteins. Our study aimed to identify novel antimicrobial peptides naturally present in milk.

METHODS AND RESULTS

The components of an endogenous bovine milk peptide database were virtually screened for charge, amphipathy, and predicted secondary structure. Thus, 23 of 248 screened peptides were identified as candidates for antimicrobial effects. After commercial synthesis, their antimicrobial activities were determined against Escherichia coli NEB5α, E. coli ATCC25922, and Bacillus subtilis ATCC6051. In the tested concentration range (<2 mM), bacteriostatic activity of 14 peptides was detected including nine peptides inhibiting both Gram-positive and Gram-negative bacteria. The most effective fragment was TKLTEEEKNRLNFLKKISQRYQKFΑLPQYLK corresponding to αS2 -casein151-181 , with minimum inhibitory concentration (MIC) of 4.0 μM against B. subtilis ATCC6051, and minimum inhibitory concentrations of 16.2 μM against both E. coli strains. Circular dichroism spectroscopy revealed conformational changes of most active peptides in a membrane-mimic environment, transitioning from an unordered to α-helical structure.

CONCLUSION

Screening of food peptide databases by prediction tools is an efficient method to identify novel antimicrobial food-derived peptides. Milk-derived antimicrobial peptides may have potential use as functional food ingredients and help to understand the molecular mechanisms of anti-infective milk effects.

Robust optimization of SVM hyperparameters in the classification of bioactive compounds

Background

Support Vector Machine has become one of the most popular machine learning tools used in virtual screening campaigns aimed at finding new drug candidates. Although it can be extremely effective in finding new potentially active compounds, its application requires the optimization of the hyperparameters with which the assessment is being run, particularly the C and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ values. The optimization requirement in turn, establishes the need to develop fast and effective approaches to the optimization procedure, providing the best predictive power of the constructed model.

Results

In this study, we investigated the Bayesian and random search optimization of Support Vector Machine hyperparameters for classifying bioactive compounds. The effectiveness of these strategies was compared with the most popular optimization procedures—grid search and heuristic choice. We demonstrated that Bayesian optimization not only provides better, more efficient classification but is also much faster—the number of iterations it required for reaching optimal predictive performance was the lowest out of the all tested optimization methods. Moreover, for the Bayesian approach, the choice of parameters in subsequent iterations is directed and justified; therefore, the results obtained by using it are constantly improved and the range of hyperparameters tested provides the best overall performance of Support Vector Machine. Additionally, we showed that a random search optimization of hyperparameters leads to significantly better performance than grid search and heuristic-based approaches.

Conclusions

The Bayesian approach to the optimization of Support Vector Machine parameters was demonstrated to outperform other optimization methods for tasks concerned with the bioactivity assessment of chemical compounds. This strategy not only provides a higher accuracy of classification, but is also much faster and more directed than other approaches for optimization. It appears that, despite its simplicity, random search optimization strategy should be used as a second choice if Bayesian approach application is not feasible.Graphical abstract

Constructing an atomic-resolution model of human P2X7 receptor followed by pharmacophore modeling to identify potential inhibitors

The P2X purinoceptor 7 (P2X7R) is a trimeric ATP-activated ion channel gated by extracellular ATP. P2X7R has important role in numerous diseases including pain, neurodegeneration, and inflammatory diseases such as rheumatoid arthritis and osteoarthritis. In this prospective, the discovery of small-molecule inhibitors for P2X7R as a novel therapeutic target has received considerable attention in recent years. At first, 3D structure of P2X7R was built by using homology modeling (HM) and a 50ns molecular dynamics simulation (MDS). Ligand-based quantitative pharmacophore modeling methodology of P2X7R antagonists were developed based on training set of 49 compounds. The best four-feature pharmacophore model, includes two hydrophobic aromatic, one hydrophobic and one aromatic ring features, has the highest correlation coefficient (0.874), cost difference (368.677), low RMSD (2.876), as well as it shows a high goodness of fit and enrichment factor. Consequently, some hit compounds were introduced as final candidates by employing virtual screening and molecular docking procedure simultaneously. Among these compounds, six potential molecule were identified as potential virtual leads which, as such or upon further optimization, can be used to design novel P2X7R inhibitors.

Prediction of dual agents as an activator of mutant p53 and inhibitor of Hsp90 by docking, molecular dynamic simulation and virtual screening

Heat shock protein90s (Hsp90s) play a crucial role in the development of cancer, and their inhibitors are a main target for tumor suppression. P53 also is a tumor suppressor, but in cancer cells, mutations in the p53 gene lead to the inactivation and accumulation of protein. For instance, the ninth p53 cancer mutation, Y220C, destabilizes the p53 core domain. Small molecules have been assumed to bind to Y220C DNA-binding domain and reactivate cellular mutant p53 functions. In this study, one of the mutant p53 activators is suggested as an Hsp90 inhibitor according to a pyrazole scaffold. To confirm a new ligand as a dual agent, molecular docking and molecular dynamic simulations were performed on both proteins (p53 and Hsp90). Molecular dynamic simulations were also conducted to evaluate the obtained results on the other two pyrazole structures, one known as Hsp90 inhibitor and the other as the reported mutant p53 activator. The findings indicate that the new ligand was stable in the active site of both proteins. Finally, a virtual screening was performed on ZINC database, and a set of new dual agents was proposed according to the new ligand scaffold.

Discovery of novel, non-acidic mPGES-1 inhibitors by virtual screening with a multistep protocol

Microsomal prostaglandin E2 synthase-1 (mPGES-1) inhibitors are considered as potential therapeutic agents for the treatment of inflammatory pain and certain types of cancer. So far, several series of acidic as well as non-acidic inhibitors of mPGES-1 have been discovered. Acidic inhibitors, however, may have issues, such as loss of potency in human whole blood and in vivo, stressing the importance of the design and identification of novel, non-acidic chemical scaffolds of mPGES-1 inhibitors. Using a multistep virtual screening protocol, the Vitas-M compound library (∼1.3 million entries) was filtered and 16 predicted compounds were experimentally evaluated in a biological assay in vitro. This approach yielded two molecules active in the low micromolar range (IC50 values: 4.5 and 3.8 μM, respectively).

Role of computer-aided drug design in modern drug discovery

Drug discovery utilizes chemical biology and computational drug design approaches for the efficient identification and optimization of lead compounds. Chemical biology is mostly involved in the elucidation of the biological function of a target and the mechanism of action of a chemical modulator. On the other hand, computer-aided drug design makes use of the structural knowledge of either the target (structure-based) or known ligands with bioactivity (ligand-based) to facilitate the determination of promising candidate drugs. Various virtual screening techniques are now being used by both pharmaceutical companies and academic research groups to reduce the cost and time required for the discovery of a potent drug. Despite the rapid advances in these methods, continuous improvements are critical for future drug discovery tools. Advantages presented by structure-based and ligand-based drug design suggest that their complementary use, as well as their integration with experimental routines, has a powerful impact on rational drug design. In this article, we give an overview of the current computational drug design and their application in integrated rational drug development to aid in the progress of drug discovery research.

Computer-aided scaffold hopping to identify a novel series of casein kinase 1 delta (CK1d) inhibitors for amyotrophic lateral sclerosis

In this study as the first attempt; comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA) and AutoGPA-based 3D-QSAR methods were applied on a set of 47 recently reported Ck1d inhibitors, in order to gain an insight into the structural requirements which providing guidelines for the design of next generation compounds with enhanced bioactivity. The results of 3D-QSAR analyses indicated that hydrophobic and negatively charged groups at 6th position of benzothiazole ring and positively charged and bulky groups at ortho position of phenyl ring are favorable for high activity. Moreover, molecular docking studies with GOLD protocol revealed that this chemical series has two different orientations in CK1d active site: orientation 1, in which the benzothiazole ring of the compounds is the closet to the hydrophobic area created by Ile23 and 37, Ala36 Lys 38, Met80, 82 and Val81, and orientation 2, in which the benzene ring of the compounds is directed toward the hydrophobic center. Molecular docking result of the riluzole, the only drug approved by FDA for amyotrophic lateral sclerosis (ALS), indicated that the orientation 2 is preferred due to the presence of OCF3 group in R(1) situation at 6th position of benzothiazole ring, while with replacement of OCF3 group by CF3, the orientation 1 is observed. At the end, to find similar analogs by virtual screening, a two-stage approach: pharmacophore-based screening using generated AutoGPA-based 3D-QSAR model followed by structure-based virtual screening using molecular docking was employed. Visual inspection of the docking results of virtually obtained hits revealed two different binding orientations, in which compounds with high GOLD fitness scores produced binding modes, which were the same as the one observed in compounds with orientation 1, whereas the binding modes of the structures with low GOLD fitness scores were in agreement with orientation 2. Further, the drug-like properties of the obtained seven hits with the highest GOLD scores were investigated as a tool to optimize the selection of the most suitable candidates for drug development.

Discovery of potent and selective nonplanar tankyrase inhibiting nicotinamide mimics

Diphtheria toxin-like ADP-ribosyltransferases catalyse a posttranslational modification, ADP-ribosylation and form a protein family of 17 members in humans. Two of the family members, tankyrases 1 and 2, are involved in several cellular processes including mitosis and Wnt/β-catenin signalling pathway. They are often over-expressed in cancer cells and have been linked with the survival of cancer cells making them potential therapeutic targets. In this study, we identified nine tankyrase inhibitors through virtual and in vitro screening. Crystal structures of tankyrase 2 with the compounds showed that they bind to the nicotinamide binding site of the catalytic domain. Based on the co-crystal structures we designed and synthesized a series of tetrahydroquinazolin-4-one and pyridopyrimidin-4-one analogs and were subsequently able to improve the potency of a hit compound almost 100-fold (from 11 μM to 150 nM). The most potent compounds were selective towards tankyrases over a panel of other human ARTD enzymes. They also inhibited Wnt/β-catenin pathway in a cell-based reporter assay demonstrating the potential usefulness of the identified new scaffolds for further development.

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein-ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.

Designing of potential inhibitors against Staphylococcus aureus sortase A: Combined analogue and structure based approach with in vitro validation

Staphylococcus aureus sortase A is an attractive target of Gram-positive bacteria that plays a crucial role in anchoring of surface proteins to peptidoglycan present in bacterial cell wall. Inhibiting sortase A is an elementary and essential effort in preventing the pathogenesis. In this context, in silico virtual screening of in-house database was performed using ligand based pharmacophore model as a filter. The developed pharmacophore model AAHR 11 consists of two acceptors, one hydrophobic and one ring aromatic feature. Top ranked molecule KKR1 was docked into the active site of the target. After profound analysis, it was analyzed and optimized based on the observations from its binding pose orientation. Upgraded version of KKR1 was KKR2 and has improved docking score, binding interactions and best fit in the binding pocket. KKR1 along with KKR2 were further validated using 100 ns molecular dynamic studies. Both KKR1 and KKR2 contain Indole-thiazolidine moiety and were synthesized. The disk diffusion assay has good initial results (ZI of KKR1, KKR2 were 24, 38 mm at 10 μg/mL and ZI of Ampicillin was 22 at 10 μg/mL) and calculated MICs of the molecules (KKR1 5.56±0.28 μg/mL, KKR2 1.32±0.12 μg/mL, Ampicillin 8±1.1 μg/mL) were in good agreement with standard drug Ampicillin. KKR1 has shown IC50 of 1.23±0.14 μM whereas the optimized lead molecule KKR2 show IC50 of 0.008±0.07 μM. Results from in silico were validated by in vitro studies and proved that indole-thiazolidine molecules would be useful for future development as lead molecules against S. aureus sortase A.

Virtual screening studies to identify novel inhibitors for Sigma F protein of Mycobacterium tuberculosis

Tuberculosis (TB) is one of the oldest threats to public health. TB is caused by the pathogen Mycobacterium tuberculosis (MTB). The Sigma factors are essential for the survival of MTB. The Sigma factor Sigma F (SigF) regulates genes expression under stress conditions. The SigF binds to RNA polymerase and forms a holoenzyme, which initiates the transcription of various genes. The Usfx, an anti-SigF protein, binds to SigF and alters the transcription initiation and gene expression. In the present work, virtual screening studies are taken up to identify the interactions between SigF and small molecular inhibitors which can inhibit the formation of holoenzyme. The studies reveal that ARG 104 and ARG 224 amino acid residues of SigF protein are forming important binding interactions with the ligands. The in silico ADME properties for the ligand data set are calculated to check the druggability of the molecules.

The application of in silico drug-likeness predictions in pharmaceutical research

The concept of drug-likeness, established from the analyses of the physiochemical properties or/and structural features of existing small organic drugs or/and drug candidates, has been widely used to filter out compounds with undesirable properties, especially poor ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiles. Here, we summarize various approaches for drug-likeness evaluations, including simple rules/filters based on molecular properties/structures and quantitative prediction models based on sophisticated machine learning methods, and provide a comprehensive review of recent advances in this field. Moreover, the strengths and weaknesses of these approaches are briefly outlined. Finally, the drug-likeness analyses of natural products and traditional Chinese medicines (TCM) are discussed.

Virtual Screening for HIV Protease Inhibitors Using a Novel Database Filtering Procedure

A virtual screening to find novel inhibitors for HIV protease was performed on the ZINC database.1 A critical part in virtual screening and associated techniques is preliminary database filtering and size reduction and for that purpose a novel feature matrix matching procedure was used. The reduction of ∼14 million available ligands to a subset of 14299 ligands was achieved with a structure based approach where the analysis of the 3D structure of the active site of the protease produced a graph with hydrogen bond donor, hydrogen bond acceptor and hydrophobic subsites represented as graph nodes. A similar treatment was also applied to the compound database content and the comparison of binding site and ligand graphs was used to preselect potentially active ligands. The resulting set was further subjected to docking. The algorithm used was able to find several novel as well as previously known and experimentally tested ligands, demonstrating the validity of the approach.

Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

Background

There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software.

Results

The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery.

Conclusion

Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT’s source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0078-2) contains supplementary material, which is available to authorized users.

Pharmacophore-Based Virtual Screening to Discover New Active Compounds for Human Choline Kinase α1

Choline kinase (CK) catalyses the transfer of the ATP γ-phosphate to choline to generate phosphocholine and ADP in the presence of magnesium leading to the synthesis of phosphatidylcholine. Of the three isoforms of CK described in humans, only the α isoforms (HsCKα) are strongly associated with cancer and have been validated as drug targets to treat this disease. Over the years, a large number of Hemicholinium-3 (HC-3)-based HsCKα biscationic inhibitors have been developed though the relevant common features important for the biological function have not been defined. Here, selecting a large number of previous HC-3-based inhibitors, we discover through computational studies a pharmacophore model formed by five moieties that are included in the 1-benzyl-4-(N-methylaniline)pyridinium fragment. Using a pharmacophore-guided virtual screening, we then identified 6 molecules that showed binding affinities in the low μM range to HsCKα1. Finally, protein crystallization studies suggested that one of these molecules is bound to the choline and ATP-binding sites. In conclusion, we have developed a pharmacophore model that not only allowed us to dissect the structural important features of the previous HC-3 derivatives, but also enabled the identification of novel chemical tools with good ligand efficiencies to investigate the biological functions of HsCKα1.

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines, as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites, such as polysaccharides, or secondary metabolites, such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e., chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, the focus was laid on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level.

Receptor-based virtual screening protocol for drug discovery

Computational aided drug design (CADD) is presently a key component in the process of drug discovery and development as it offers great promise to drastically reduce cost and time requirements. In the pharmaceutical arena, virtual screening is normally regarded as the top CADD tool to screen large libraries of chemical structures and reduce them to a key set of likely drug candidates regarding a specific protein target. This chapter provides a comprehensive overview of the receptor-based virtual screening process and of its importance in the present drug discovery and development paradigm. Following a focused contextualization on the subject, the main stages of a virtual screening campaign, including its strengths and limitations, are the subject of particular attention in this review. In all of these stages special consideration will be given to practical issues that are normally the Achilles heel of the virtual screening process.

Molecular docking based screening of predicted potential inhibitors for VP40 from Ebola virus

Ebola virus is a member of Filoviridae and cause severe human disease with 90 percent mortality. The life cycle of Ebola contains an assembly stage which is mediated by VP40 proteins. VP40 subunits oligomerize and form ring-structures which are either octamers or hexamers. Prevention of VP40 matrix protein assembly prevents virus particle formation as well as virus budding. In the present study we simulated the biological condition for a single VP40 subunit. Then a library containing 120.000 drugs like chemicals was used as the virtual screening database. Top 10 successive hits were then analyzed regarding absorption, distribution, metabolism, and excretion properties. Moreover probable accessorial human protein targets and toxicity properties of successive hits were analyzed by in silico tools. We found 4 chemicals that could bind VP40 subunits in a manner that by making an interfering steric condense prevents matrix protein oligomerization. The pharmacokinetic and toxicity studies also validated the potential of 4 finlay successive hits to be considered as a new anti-Ebola drugs.

Effective virtual screening strategy focusing on the identification of novel Bruton's tyrosine kinase inhibitors

Dysregulation of the B-cell receptor (BCR) signaling pathway plays a vital role in the pathogenesis and development of B-cell malignancies. Bruton's tyrosine kinase (BTK), a key component in the BCR signaling, has been validated as a valuable target for the treatment of B-cell malignancies. In an attempt to find novel and potent BTK inhibitors, both ligand- and structure-based pharmacophore models were generated using Discovery Studio 2.5 and Ligandscout 3.11 with the aim of screening the ChemBridge database. The resulting hits were then subjected to sequential docking experiments using two independent docking programs, CDOCKER and Glide. Molecules displaying high glide scores and H-bond interactions with the key residue Met477 in both of the docking programs were retained. Drug-like criteria including Lipinski's rule of five and ADMET properties filters were employed for further refinement of the retrieved hits. By clustering, eight promising compounds with novel chemical scaffolds were finally selected and the top two ranking compounds were evaluated by molecular dynamics simulation. We believe that these compounds are of great potential in BTK inhibition and will be used for further investigation.

Opioid receptors: Structural and mechanistic insights into pharmacology and signaling

Opioid receptors are important drug targets for pain management, addiction, and mood disorders. Although substantial research on these important subtypes of G protein-coupled receptors has been conducted over the past two decades to discover ligands with higher specificity and diminished side effects, currently used opioid therapeutics remain suboptimal. Luckily, recent advances in structural biology of opioid receptors provide unprecedented insights into opioid receptor pharmacology and signaling. We review here a few recent studies that have used the crystal structures of opioid receptors as a basis for revealing mechanistic details of signal transduction mediated by these receptors, and for the purpose of drug discovery.

Discovery of a new chemical series of BRD4(1) inhibitors using protein-ligand docking and structure-guided design

Bromodomains are key transcriptional regulators that are thought to be druggable epigenetic targets for cancer, inflammation, diabetes and cardiovascular therapeutics. Of particular importance is the first of two bromodomains in bromodomain containing 4 protein (BRD4(1)). Protein-ligand docking in BRD4(1) was used to purchase a small, focused screening set of compounds possessing a large variety of core structures. Within this set, a small number of weak hits each contained a dihydroquinoxalinone ring system. We purchased other analogs with this ring system and further validated the new hit series and obtained improvement in binding inhibition. Limited exploration by new analog synthesis showed that the binding inhibition in a FRET assay could be improved to the low μM level making this new core a potential hit-to-lead series. Additionally, the predicted geometries of the initial hit and an improved analog were confirmed by X-ray co-crystallography with BRD4(1).

Small molecule inhibitors of HIVgp41 N-heptad repeat trimer formation

Identification of mechanistically novel anti-HIV fusion inhibitors was accomplished using a computer-aided structure-based design approach with the goal of blocking the formation of the N-heptad repeat (NHR) trimer of the viral protein gp41. A virtual screening strategy that included per-residue interaction patterns (footprints) was employed to identify small molecules compatible with putative binding pockets at the internal interface of the NHR helices at the core native viral six-helix bundle. From a screen of ∼2.8 million compounds using the DOCK program, 120 with favorable energetic and footprint overlap characteristics were purchased and experimentally tested leading to two compounds with favorable cell-cell fusion (IC50) and cytotoxicity profiles. Importantly, both hits were identified on the basis of scores containing footprint overlap terms and would not have been identified using the standard DOCK energy function alone. To our knowledge, these compounds represent the first reported small molecules that inhibit viral entry via the proposed NHR-trimer obstruction mechanism.

Virtual screening, ADMET profiling, molecular docking and dynamics approaches to search for potent selective natural molecules based inhibitors against metallothionein-III to study Alzheimer's disease

MOTIVATION

Metallothionein-III (MT-III) displays neuro-inhibitory activity and is involved in the repair of neuronal damage. An altered expression level of MT-III suggests that it could be a mitigating factor in Alzheimer's disease (AD) neuronal dysfunction. Currently there are limited marketed drugs available against MT-III. The inhibitors are mostly pseudo-peptide based with limited ADMET. In our present study, available database InterBioScreen (natural compounds) was screened out for MT-III. Pharmacodynamics and pharmacokinetic studies were performed. Molecular docking and simulations of top hit molecules were performed to study complex stability.

RESULTS

Study reveals potent selective molecules that interact and form hydrogen bonds with amino acids Ser-6 and Lys-22 are common to established melatonin inhibitors for MT-III. These include DMHMIO, MCA B and s27533 derivatives. The ADMET profiling was better with comparable interaction energy values. It includes properties like blood brain barrier, hepatotoxicity, druggability, mutagenicity and carcinogenicity. Molecular dynamics studies were performed to validate our findings.

Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds

Repetitive exposure to a chemical agent can induce an immune reaction in inherently susceptible individuals that leads to skin sensitization. Although many chemicals have been reported as skin sensitizers, there have been very few rigorously validated QSAR models with defined applicability domains (AD) that were developed using a large group of chemically diverse compounds. In this study, we have aimed to compile, curate, and integrate the largest publicly available dataset related to chemically-induced skin sensitization, use this data to generate rigorously validated and QSAR models for skin sensitization, and employ these models as a virtual screening tool for identifying putative sensitizers among environmental chemicals. We followed best practices for model building and validation implemented with our predictive QSAR workflow using Random Forest modeling technique in combination with SiRMS and Dragon descriptors. The Correct Classification Rate (CCR) for QSAR models discriminating sensitizers from non-sensitizers was 71-88% when evaluated on several external validation sets, within a broad AD, with positive (for sensitizers) and negative (for non-sensitizers) predicted rates of 85% and 79% respectively. When compared to the skin sensitization module included in the OECD QSAR Toolbox as well as to the skin sensitization model in publicly available VEGA software, our models showed a significantly higher prediction accuracy for the same sets of external compounds as evaluated by Positive Predicted Rate, Negative Predicted Rate, and CCR. These models were applied to identify putative chemical hazards in the Scorecard database of possible skin or sense organ toxicants as primary candidates for experimental validation.

crystal structures: beta-2 adrenergic receptor case study

Background

Distinguishing active from inactive compounds is one of the crucial problems of molecular docking, especially in the context of virtual screening experiments. The randomization of poses and the natural flexibility of the protein make this discrimination even harder. Some of the recent approaches to post-docking analysis use an ensemble of receptor models to mimic this naturally occurring conformational diversity. However, the optimal number of receptor conformations is yet to be determined.

In this study, we compare the results of a retrospective screening of beta-2 adrenergic receptor ligands performed on both the ensemble of receptor conformations extracted from ten available crystal structures and an equal number of homology models. Additional analysis was also performed for homology models with up to 20 receptor conformations considered.

Results

The docking results were encoded into the Structural Interaction Fingerprints and were automatically analyzed by support vector machine. The use of homology models in such virtual screening application was proved to be superior in comparison to crystal structures. Additionally, increasing the number of receptor conformational states led to enhanced effectiveness of active vs. inactive compounds discrimination.

Conclusions

For virtual screening purposes, the use of homology models was found to be most beneficial, even in the presence of crystallographic data regarding the conformational space of the receptor. The results also showed that increasing the number of receptors considered improves the effectiveness of identifying active compounds by machine learning methods.

Graphical abstract

Comparison of machine learning results obtained for various number of beta-2 AR homology models and crystal structures.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-015-0062-x) contains supplementary material, which is available to authorized users.

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Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening

Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.

Advances in the development of SUMO specific protease (SENP) inhibitors

Sumoylation is a reversible post-translational modification that involves the covalent attachment of small ubiquitin-like modifier (SUMO) proteins to their substrate proteins. Prior to their conjugation, SUMO proteins need to be proteolytically processed from its precursor form to mature or active form. SUMO specific proteases (SENPs) are cysteine proteases that cleave the pro or inactive form of SUMO at C-terminus using its hydrolase activity to expose two glycine residues. SENPs also catalyze the de-conjugation of SUMO proteins using their isopeptidase activity, which is crucial for recycling of SUMO from substrate proteins. SENPs are important for maintaining the balance between sumoylated and unsumoylated proteins required for normal cellular physiology. Several studies reported the overexpression of SENPs in disease conditions and highlighted their role in the development of various diseases, especially cancer. In this review, we will address the current biological understanding of various SENP isoforms and their role in the pathogenesis of different cancers and other diseases. We will then discuss the advances in the development of protein-based, peptidyl and small molecule inhibitors of various SENP isoforms. Finally, we will summarize successful examples of computational screening that allowed the identification of SENP inhibitors with therapeutic potential.

Enhancing the Reliability of GPCR Models by Accounting for Flexibility of Their Pro-Containing Helices: the Case of the Human mAChR1 Receptor

To better investigate the GPCR structures, we have recently proposed to explore their flexibility by simulating the bending of their Pro-containing TM helices so generating a set of models (the so-called chimeras) which exhaustively combine the two conformations (bent and straight) of these helices. The primary objective of the study is to investigate whether such an approach can be exploited to enhance the reliability of the GPCR models generated by distant templates. The study was focused on the human mAChR1 receptor for which a presumably reliable model was generated using the congener mAChR3 as the template along with a second less reliable model based on the distant β2-AR template. The second model was then utilized to produce the chimeras by combining the conformations of its Pro-containing helices (i.e., TM4, TM5, TM6 and TM7 with 16 modeled chimeras). The reliability of such chimeras was assessed by virtual screening campaigns as evaluated using a novel skewness metric where they surpassed the predictive power of the more reliable mAChR1 model. Finally, the virtual screening campaigns emphasize the opportunity of synergistically combining the scores of more chimeras using a specially developed tool which generates highly predictive consensus functions by maximizing the corresponding enrichment factors.

In silico design of low molecular weight protein-protein interaction inhibitors: Overall concept and recent advances

Protein-protein interactions (PPIs) are carrying out diverse functions in living systems and are playing a major role in the health and disease states. Low molecular weight (LMW) "drug-like" inhibitors of PPIs would be very valuable not only to enhance our understanding over physiological processes but also for drug discovery endeavors. However, PPIs were deemed intractable by LMW chemicals during many years. But today, with the new experimental and in silico technologies that have been developed, about 50 PPIs have already been inhibited by LMW molecules. Here, we first focus on general concepts about protein-protein interactions, present a consensual view about ligandable pockets at the protein interfaces and the possibilities of using fast and cost effective structure-based virtual screening methods to identify PPI hits. We then discuss the design of compound collections dedicated to PPIs. Recent financial analyses of the field suggest that LMW PPI modulators could be gaining momentum over biologics in the coming years supporting further research in this area.

Docking and binding free energy calculations of sirtuin inhibitors

Sirtuins form a unique and highly conserved class of NAD(+)-dependent lysine deacylases. Among these the human subtypes Sirt1-3 has been implicated in the pathogenesis of numerous diseases such as cancer, metabolic syndromes, viral diseases and neurological disorders. Most of the sirtuin inhibitors that have been identified so far show limited potency and/or isoform selectivity. Here, we introduce a promising method to generate protein-inhibitor complexes of human Sirt1, Sirt2 and Sirt3 by means of ligand docking and molecular dynamics simulations. This method highly reduces the complexity of such applications and can be applied to other protein targets beside sirtuins. To the best of our knowledge, we present the first binding free energy method developed by using a validated data set of sirtuin inhibitors, where both a fair number of compounds (33 thieno[3,2-d]pyrimidine-6-carboxamide derivatives) was developed and tested in the same laboratory and also crystal structures in complex with the enzyme have been reported. A significant correlation between binding free energies derived from MM-GBSA calculations and in vitro data was found for all three sirtuin subtypes. The developed MM-GBSA protocol is computationally inexpensive and can be applied as a post-docking filter in virtual screening to find novel Sirt1-3 inhibitors as well as to prioritize compounds with similar chemical structures for further biological characterization.

Design of Novel Mycobacterium tuberculosis Pantothenate Synthetase Inhibitors: Virtual Screening, Synthesis and In Vitro Biological Activities

Pantothenate synthetase (PS) enzyme involved in the pantothenate biosynthetic pathway is essential for the virulence and persistent growth of Mycobacterium tuberculosis (MTB). It is encoded by the panC gene, and has become an appropriate target for developing new therapeutics for tuberculosis. Here we report new inhibitors active against MTB PS developed using energy based pharmacophore modelling of the available proteininhibitor complex (3IVX) and virtual screening of a large commercial library. The e-pharmacophore model consisted of a ring aromatic (R), negative ionizable (N) and acceptor (A) sites. Compounds 5 and 10 emerged as promising hits with IC50 s 2.18 µM and 6.63 µM respectively. Further structural optimization was attempted to optimize lead 10 using medicinal chemistry approach and six compounds were found to exhibit better enzyme inhibition compared to parent compound lead 10 (<6 µM).

When drug discovery meets web search: Learning to Rank for ligand-based virtual screening

Background

The rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms.

Results

A standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration.

Conclusions

To the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html.

Graphical Abstract

The analogy between web search and ligand-based drug discovery

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Stereoselective virtual screening of the ZINC database using atom pair 3D-fingerprints

BACKGROUND

Tools to explore large compound databases in search for analogs of query molecules provide a strategically important support in drug discovery to help identify available analogs of any given reference or hit compound by ligand based virtual screening (LBVS). We recently showed that large databases can be formatted for very fast searching with various 2D-fingerprints using the city-block distance as similarity measure, in particular a 2D-atom pair fingerprint (APfp) and the related category extended atom pair fingerprint (Xfp) which efficiently encode molecular shape and pharmacophores, but do not perceive stereochemistry. Here we investigated related 3D-atom pair fingerprints to enable rapid stereoselective searches in the ZINC database (23.2 million 3D structures).

RESULTS

Molecular fingerprints counting atom pairs at increasing through-space distance intervals were designed using either all atoms (16-bit 3DAPfp) or different atom categories (80-bit 3DXfp). These 3D-fingerprints retrieved molecular shape and pharmacophore analogs (defined by OpenEye ROCS scoring functions) of 110,000 compounds from the Cambridge Structural Database with equal or better accuracy than the 2D-fingerprints APfp and Xfp, and showed comparable performance in recovering actives from decoys in the DUD database. LBVS by 3DXfp or 3DAPfp similarity was stereoselective and gave very different analogs when starting from different diastereomers of the same chiral drug. Results were also different from LBVS with the parent 2D-fingerprints Xfp or APfp. 3D- and 2D-fingerprints also gave very different results in LBVS of folded molecules where through-space distances between atom pairs are much shorter than topological distances.

CONCLUSIONS

3DAPfp and 3DXfp are suitable for stereoselective searches for shape and pharmacophore analogs of query molecules in large databases. Web-browsers for searching ZINC by 3DAPfp and 3DXfp similarity are accessible at www.gdb.unibe.ch and should provide useful assistance to drug discovery projects. Graphical abstractAtom pair fingerprints based on through-space distances (3DAPfp) provide better shape encoding than atom pair fingerprints based on topological distances (APfp) as measured by the recovery of ROCS shape analogs by fp similarity.

Structure based virtual screening to identify selective phosphodiesterase 4B inhibitors

Phosphodiesterase 4 (PDE4), is a hydrolytic enzyme, is proposed as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B selective inhibitors are desirable to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. To achieve this goal, ligand based pharmacophore modeling and molecular docking approach is employed. Pharmacophore hypotheses for PDE4B and PDE4D are generated using HypoGen algorithm. The best PDE4B pharmacophore hypothesis (Hypo1_PDE4B) consist of one hydrogen-bond acceptor and two ring aromatic features, whereas PDE4D pharmacophore hypothesis (Hypo1_PDE4D) consist of one hydrogen-bond acceptor, one hydrophobic aliphatic, and two ring aromatic features. The validated pharmacophore hypotheses are used in virtual screening to identify selective PDE4B inhibitors. The hits were screened for their estimated activity, FitValue, and quantitative estimation of drug likeness. After molecular docking analysis, ten hits were purchased for in vitro analysis. Out of these, six hits have shown potent and selective inhibitory activity against PDE4B with IC50 values ranging from 2 to 378nM.

A ranking method for the concurrent learning of compounds with various activity profiles

Background

In this study, we present a SVM-based ranking algorithm for the concurrent learning of compounds with different activity profiles and their varying prioritization. To this end, a specific labeling of each compound was elaborated in order to infer virtual screening models against multiple targets. We compared the method with several state-of-the-art SVM classification techniques that are capable of inferring multi-target screening models on three chemical data sets (cytochrome P450s, dehydrogenases, and a trypsin-like protease data set) containing three different biological targets each.

Results

The experiments show that ranking-based algorithms show an increased performance for single- and multi-target virtual screening. Moreover, compounds that do not completely fulfill the desired activity profile are still ranked higher than decoys or compounds with an entirely undesired profile, compared to other multi-target SVM methods.

Conclusions

SVM-based ranking methods constitute a valuable approach for virtual screening in multi-target drug design. The utilization of such methods is most helpful when dealing with compounds with various activity profiles and the finding of many ligands with an already perfectly matching activity profile is not to be expected.

Electronic supplementary material

The online version of this article (doi:10.1186/s13321-014-0050-6) contains supplementary material, which is available to authorized users.

Cell-based and virtual fragment screening for adrenergic α2C receptor agonists

Fragment-based drug discovery has emerged as an alternative to conventional lead identification and optimization strategies generally supported by biophysical detection techniques. Membrane targets like G protein-coupled receptors (GPCRs), however, offer challenges in lack of generic immobilization or stabilization methods for the dynamic, membrane-bound supramolecular complexes. Also modeling of different functional states of GPCRs proved to be a challenging task. Here we report a functional cell-based high concentration screening campaign for the identification of adrenergic α2C receptor agonists compared with the virtual screening of the same ligand set against an active-like homology model of the α2C receptor. The conventional calcium mobilization-based assay identified active fragments with a similar incidence to several other reported fragment screens on GPCRs. 16 out of 3071 screened fragments turned out as specific ligands of α2C, two of which were identified by virtual screening as well and several of the hits possessed surprisingly high affinity and ligand efficiency. Our results indicate that in vitro biological assays can be utilized in the fragment hit identification process for GPCR targets.

Ligand and structure-based approaches for the identification of SIRT1 activators

SIRT1 is a NAD(+)-dependent deacetylase that involved in various important metabolic pathways. Combined ligand and structure-based approach was utilized for identification of SIRT1 activators. Pharmacophore models were developed using DISCOtech and refined with GASP module of Sybyl X software. Pharmacophore models were composed of two hydrogen bond acceptor (HBA) atoms, two hydrogen bond donor (HBD) sites and one hydrophobic (HY) feature. The pharmacophore models were validated through receiver operating characteristic (ROC) and Güner-Henry (GH) scoring methods. Model-2 was selected as best model among the model 1-3, based on ROC and GH score value, and found reliable in identification of SIRT1 activators. Model-2 (3D search query) was searched against Zinc database. Several compounds with different chemical scaffold were retrieved as hits. Currently, there is no experimental SIRT1 3D structure available, therefore, we modeled SIRT1 protein structure using homology modeling. Compounds with Qfit value of more than 86 were selected for docking study into the SIRT1 homology model to explore the binding mode of retrieved hits in the active allosteric site. Finally, in silico ADMET prediction study was performed with two best docked compounds. Combination of ligand and structure-based modeling methods identified active hits, which may be good lead compounds to develop novel SIRT1 activators.

Discovery of Mer kinase inhibitors by virtual screening using Structural Protein-Ligand Interaction Fingerprints

Mer is a receptor tyrosine kinase implicated in acute lymphoblastic leukemia (ALL), the most common malignancy in children. The currently available data provide a rationale for development of Mer kinase inhibitors as cancer therapeutics that can target both cell autologous and immune-modulatory anti-tumor effects. We have previously reported several series of potent Mer inhibitors and the objective of the current report is to identify a chemically dissimilar back-up series that might circumvent potential, but currently unknown, flaws inherent to the lead series. To this end, we virtually screened a database of ∼3.8million commercially available compounds using high-throughput docking followed by a filter involving Structural Protein-Ligand Interaction Fingerprints (SPLIF). SPLIF permits a quantitative assessment of whether a docking pose interacts with the protein target similarly to an endogenous or known synthetic ligand, and therefore helps to improve both sensitivity and specificity with respect to the docking score alone. Of the total of 62 experimentally tested compounds, 15 demonstrated reliable dose-dependent responses in the Mer in vitro kinase activity assay with inhibitory potencies ranging from 0.46μM to 9.9μM.

Identification of chikungunya virus nsP2 protease inhibitors using structure-base approaches

The nsP2 protease of chikungunya virus (CHIKV) is one of the essential components of viral replication and it plays a crucial role in the cleavage of polyprotein precursors for the viral replication process. Therefore, it is gaining attention as a potential drug design target against CHIKV. Based on the recently determined crystal structure of the nsP2 protease of CHIKV, this study identified potential inhibitors of the virus using structure-based approaches with a combination of molecular docking, virtual screening and molecular dynamics (MD) simulations. The top hit compounds from database searching, using the NCI Diversity Set II, with targeting at five potential binding sites of the nsP2 protease, were identified by blind dockings and focused dockings. These complexes were then subjected to MD simulations to investigate the stability and flexibility of the complexes and to gain a more detailed insight into the interactions between the compounds and the enzyme. The hydrogen bonds and hydrophobic contacts were characterized for the complexes. Through structural alignment, the catalytic residues Cys1013 and His1083 were identified in the N-terminal region of the nsP2 protease. The absolute binding free energies were estimated by the linear interaction energy approach and compared with the binding affinities predicted with docking. The results provide valuable information for the development of inhibitors for CHIKV.

Virtual screening for the identification of novel inhibitors of Mycobacterium tuberculosis cell wall synthesis: inhibitors targeting RmlB and RmlC

BACKGROUND

Tuberculosis remains one of the deadliest infectious diseases in humans. It has caused more than 100 million deaths since its discovery in 1882. Currently, more than 5 million people are infected with TB bacterium each year. The cell wall of Mycobacterium tuberculosis plays an important role in maintaining the ability of mycobacteria to survive in a hostile environment. Therefore, we report a virtual screening (VS) study aiming to identify novel inhibitors that simultaneously target RmlB and RmlC, which are two essential enzymes for the synthesis of the cell wall of M. tuberculosis.

METHODS

A hybrid VS method that combines drug-likeness prediction, pharmacophore modeling and molecular docking studies was used to indentify inhibitors targeting RmlB and RmlC.

RESULTS

The pharmacophore models HypoB and HypoC of RmlB inhibitors and RmlC inhibitors, respectively, were developed based on ligands complexing with their corresponding receptors. In total, 20 compounds with good absorption, distribution, metabolism, excretion, and toxicity properties were carefully selected using the hybird VS method.

DISCUSSION

We have established a hybrid VS method to discover novel inhibitors with new scaffolds. The molecular interactions of the selected potential inhibitors with the active-site residues are discussed in detail. These compounds will be further evaluated using biological activity assays and deserve consideration for further structure-activity relationship studies.

Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization

Skin permeability is widely considered to be mechanistically implicated in chemically-induced skin sensitization. Although many chemicals have been identified as skin sensitizers, there have been very few reports analyzing the relationships between molecular structure and skin permeability of sensitizers and non-sensitizers. The goals of this study were to: (i) compile, curate, and integrate the largest publicly available dataset of chemicals studied for their skin permeability; (ii) develop and rigorously validate QSAR models to predict skin permeability; and (iii) explore the complex relationships between skin sensitization and skin permeability. Based on the largest publicly available dataset compiled in this study, we found no overall correlation between skin permeability and skin sensitization. In addition, cross-species correlation coefficient between human and rodent permeability data was found to be as low as R²=0.44. Human skin permeability models based on the random forest method have been developed and validated using OECD-compliant QSAR modeling workflow. Their external accuracy was high (Q²_ext = 0.73 for 63% of external compounds inside the applicability domain). The extended analysis using both experimentally-measured and QSAR-imputed data still confirmed the absence of any overall concordance between skin permeability and skin sensitization. This observation suggests that chemical modifications that affect skin permeability should not be presumed a priori to modulate the sensitization potential of chemicals. The models reported herein as well as those developed in the companion paper on skin sensitization suggest that it may be possible to rationally design compounds with the desired high skin permeability but low sensitization potential.

Protein structure prediction provides comparable performance to crystallographic structures in docking-based virtual screening

Structure based virtual screening has largely been limited to protein targets for which either an experimental structure is available or a strongly homologous template exists so that a high-resolution model can be constructed. The performance of state of the art protein structure predictions in virtual screening in systems where only weakly homologous templates are available is largely untested. Using the challenging DUD database of structural decoys, we show here that even using templates with only weak sequence homology (<30% sequence identity) structural models can be constructed by I-TASSER which achieve comparable enrichment rates to using the experimental bound crystal structure in the majority of the cases studied. For 65% of the targets, the I-TASSER models, which are constructed essentially in the apo conformations, reached 70% of the virtual screening performance of using the holo-crystal structures. A correlation was observed between the success of I-TASSER in modeling the global fold and local structures in the binding pockets of the proteins versus the relative success in virtual screening. The virtual screening performance can be further improved by the recognition of chemical features of the ligand compounds. These results suggest that the combination of structure-based docking and advanced protein structure modeling methods should be a valuable approach to the large-scale drug screening and discovery studies, especially for the proteins lacking crystallographic structures.

Establishment of a screening protocol for identification of aminopeptidase N inhibitors

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Two pharmacophore models have been developed.

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Virtual screening was performed by the pharmacophore models and docking.

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Six selected hits were discovered to have inhibitory activities.

Inhibitors of aminopeptidase N (APN) have been thought as potential drugs for the treatment of tumor angiogenesis, invasion and metastasis and a considerable number of APN inhibitors have been reported recently. To clarify the essential structure–activity relationship for the APN inhibitors as well as identify new potent leads against APN, pharmacophore models were established using structure- and common feature-based approaches and validated with a database of active and inactive compounds. These validated pharmacophores were then used in database screening for novel virtual leads. The hit compounds were further subjected to molecular docking studies to refine the retrieved hits. Finally, six structurally diverse compounds that showed strong interactions with the key amino acids and the zinc ion were selected for biological evaluation, where two hits showed more than 70% inhibition against APN at 60 μM concentration. The evaluation results show the potential of our screening approach in identifying APN inhibitors.

Construction of a genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum provides new strategies for bactericide discovery

We reconstructed the first genome-scale metabolic network of the plant pathogen Pectobacterium carotovorum subsp. carotovorum PC1 based on its genomic sequence, annotation, and physiological data. Metabolic characteristics were analyzed using flux balance analysis (FBA), and the results were afterwards validated by phenotype microarray (PM) experiments. The reconstructed genome-scale metabolic model, iPC1209, contains 2235 reactions, 1113 metabolites and 1209 genes. We identified 19 potential bactericide targets through a comprehensive in silico gene-deletion study. Next, we performed virtual screening to identify candidate inhibitors for an important potential drug target, alkaline phosphatase, and experimentally verified that three lead compounds were able to inhibit both bacterial cell viability and the activity of alkaline phosphatase in vitro. This study illustrates a new strategy for the discovery of agricultural bactericides.

De novo design based pharmacophore query generation and virtual screening for the discovery of Hsp-47 inhibitors

Heat shock protein-47 (Hsp-47) is exclusive collagen specific molecular chaperone involved in the maturation, processing and secretion of procollagen. Hsp-47 is consistently upregulated in several fibrotic diseases. Till date there is no potential antifibrotic small molecule drug available and Hsp-47 is known to be potential therapeutic target for fibrotic disorder and drug designing. We used the de novo drug design approach followed by pharmacophore generation and virtual screening to propose Hsp-47 based antifibrotic molecules. We used e-LEAD server for de novo drug design and ZINCPharmer for 3D pharmacophore generation and virtual screening. The virtually screened molecule may inhibit direct recruitment of collagen triple helix to interact with Hsp-47 and act as antifibrotic drug.