Evaluation of potential inhibitors of squalene synthase based on virtual screening and in vitro studies

Squalene synthase (SQS) is a potential target for hyperlipidemia treatment. To identify novel chemical scaffolds of SQS inhibitors, we generated 3D-QSAR pharmacophore models using HypoGen. The best quantitative pharmacophore model, Hypo 1, was selected for virtual screening using two chemical databases, Specs and Traditional Chinese Medicine database (TCM). The best-mapped hit compounds were then subjected to filtering by Lipinski^’s rule of five and docking studies to refine the hits. Finally, five compounds were selected from the top-ranked hit compounds for SQS inhibitory assay in vitro. Three of these compounds could inhibit SQS in vitro, and should be further evaluated pre-clinically as a treatment for hyperlipidemia.     

Development of energetic pharmacophore for the designing of 1,2,3,4-tetrahydropyrimidine derivatives as selective cyclooxygenase-2 inhibitors

We present here the Energetic pharmacophore model representing complementary features of the 1,2,3,4-tetrahydropyrimidine for selective cyclooxygenase-2 (COX-2) inhibition. For the development of pharmacophore hypothesis, a total of 43 previously reported compounds were docked on active site of COX-2 enzyme. The generated pharmacophore features were ranked using energetic terms of Glide XP docking for 1,2,3,4-tetrahydropyrimidine scaffold to optimize its structure requirement for COX-2 inhibition. The thirty new 4,5,6-triphenyl-1,2,3,4-tetrahydropyrimidine derivatives were synthesized and assessed for selective COX-2 inhibitory activity. Two compounds 4B1 and 4B11 were found to be potent and selective COX-2 inhibitors. The molecular docking studies revealed that the newly synthesized compounds can be docked into COX-2 binding site and also provide the molecular basis for their activity.     

Predicting hiCE inhibitors based upon pharmacophore models derived from the receptor and its ligands

Human intestinal carboxyl esterase (hiCE) is a drug target for ameliorating irinotecan-induced diarrhea. By reducing irinotecan-induced diarrhea, hiCE inhibitors can improve the anti-cancer efficacy of irinotecan. To find effective hiCE inhibitors, a new virtual screening protocol that combines pharmacophore models derived from the hiCE structure and its ligands has been proposed. The hiCE structure has been constructed through homology techniques using hCES1’s crystal structure. The hiCE structure was optimized via molecular dynamics simulations with the most known active hiCE inhibitors docked into the structure. An optimized pharmacophore, derived from the receptor, was then generated. A ligand-based pharmacophore was also generated from a larger set of known hiCE inhibitors. The final hiCE inhibitor predictions were based upon the virtual screening hits from both ligand-based and receptor-based pharmacophore models. The hit rates from the ligand-based and receptor-based pharmacophore models are 88% and 86%, respectively. The final hit rate is 94%. The two models are highly consistent with one another (85%). This proves that both models are reliable.     

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC₅₀ values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere–Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a Q_FIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.     

Modeling ligand-receptor interaction for some MHC class II HLA-DR4 peptide mimetic inhibitors using several molecular docking and 3D QSAR techniques

The ligand-receptor interaction between some peptidomimetic inhibitors and a class II MHC peptide presenting molecule, the HLA-DR4 receptor, was modeled using some three-dimensional (3D) quantitative structure-activity relationship (QSAR) methods such as the Comparative Molecular Field Analysis (CoMFA), Comparative Molecular Similarity Indices Analysis (CoMSIA), and a pharmacophore building method, the Catalyst program. The structures of these peptidomimetic inhibitors were generated theoretically, and the conformations used in the 3D QSAR studies were defined by docking them into the known structure of HLA-DR4 receptor through the GOLD, GLIDE Rigidly, GLIDE Flexible, and Xscore programs. Some of the parameters used in these docking programs were selected by docking an X-ray ligand into the receptor and comparing the root-means-square difference (RMSD) computed between the coordinates of the X-ray and docked structure. However, the goodness of a docking result for docking a series of peptidomimetic inhibitors into the HLA-DR4 receptor was judged by comparing the Spearman's rank correlation coefficient computed between each docking result and the activity data taken from the literature. The best CoMFA and CoMSIA models were constructed using the aligned structures of the best docking result. The CoMSIA was conducted in a stepwise manner to identify some important molecular features that were further employed in a pharmacophore building process by the Catalyst program. It was found that most inhibitors of the training set were accurately predicted by the best pharmacophore model, the Hypo1 hypothesis constructed. The deviation or conflict found between the actual and predicted activities of some inhibitors of both the training and the test sets were also investigated by mapping the Hypo1 hypothesis onto the corresponding structures of the inhibitors.     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores

The design of biologically active compounds from ligand-free protein structures using a structure-based approach is still a major challenge. In this paper, we present a fast knowledge-based approach (HS-Pharm) that allows the prioritization of cavity atoms that should be targeted for ligand binding, by training machine learning algorithms with atom-based fingerprints of known ligand-binding pockets. The knowledge of hot spots for ligand binding is here used for focusing structure-based pharmacophore models. Three targets of pharmacological interest (neuraminidase, beta2 adrenergic receptor, and cyclooxygenase-2) were used to test the evaluated methodology, and the derived structure-based pharmacophores were used in retrospective virtual screening studies. The current study shows that structure-based pharmacophore screening is a powerful technique for the fast identification of potential hits in a chemical library, and that it is a valid alternative to virtual screening by molecular docking.     

Use of catalyst pharmacophore models for screening of large combinatorial libraries

Using a data set comprised of literature compounds and structure-activity data for cyclin dependent kinase 2, several pharmacophore hypotheses were generated using Catalyst and evaluated using several criteria. The two best were used in retrospective searches of 10 three-dimensional databases containing over 1,000,000 proprietary compounds. The results were then analyzed for the efficiency with which the hypotheses performed in the areas of compound prioritization, library prioritization, and library design. First as a test of their compound prioritization capabilities, the pharmacophore models were used to search combinatorial libraries that were known to contain CDK active compounds to see if the pharmacophore models could selectively choose the active compounds over the inactive compounds. Second as a test of their utility in library design again the pharmacophore models were used to search the active combinatorial libraries to see if the key synthons were over represented in the hits from the pharmacophore searches. Finally as a test of their ability to prioritize combinatorial libraries, several inactive libraries were searched in addition to the active libraries in order to see if the active libraries produced significantly more hits than the inactive libraries. For this study the pharmacophore models showed potential in all three areas. For compound prioritization, one of the models selected active compounds at a rate nearly 11 times that of random compound selection though in other cases models missed the active compounds entirely. For library design, most of the key fragments were over represented in the hits from at least one of the searches though again some key fragments were missed. Finally, for library prioritization, the two active libraries both produced a significant number of hits with both pharmacophore models, whereas none of the eight inactive libraries produced a significant number of hits for both models.     

Structure-based QSAR analysis of a set of 4-hydroxy-5,6-dihydropyrones as inhibitors of HIV-1 protease: an application of the receptor-dependent (RD) 4D-QSAR formalism

Receptor-dependent (RD) 4D-QSAR models were constructed for a set of 39 4-hydroxy-5,6-dihydropyrone analogue HIV-1 protease inhibitors. The receptor model used in this QSAR analysis was derived from the HIV-1 protease (PDB ID ) crystal structure. The bound ligand in the active site of the enzyme, also a 4-hydroxy-5,6-dihydropyrone analogue, was used as the reference ligand for docking the data set compounds. The optimized RD 4D-QSAR models are not only statistically significant (r(2) = 0.86, q(2) = 0.80 for four- and greater-term models) but also possess reasonable predictivity based on test set predictions. The proposed "active" conformations of the docked analogues in the active site of the enzyme are consistent in overall molecular shape with those suggested from crystallographic studies. Moreover, the RD 4D-QSAR models also "capture" the existence of specific induced-fit interactions between the enzyme active site and each specific inhibitor. Hydrophobic interactions, steric shape requirements, and hydrogen bonding of the 4-hydroxy-5,6-dihydropyrone analogues with the HIV-1 protease binding site model dominate the RD 4D-QSAR models in a manner again consistent with experimental conclusions. Some possible hypotheses for the development of new lead HIV-1 protease inhibitors can be inferred from the RD 4D-QSAR models.     

Structure-based virtual screening of dipeptidyl peptidase 4 inhibitors and their in vitro analysis

Type 2 diabetes mellitus (T2DM) is one of the most widely prevalent metabolic disorders with no cure to date thus remains the most challenging task in the current drug discovery. Therefore, the only strategy to control diabetes prevalence is to develop novel efficacious therapeutics. Dipeptidyl Peptidase 4 (DPP-4) inhibitors are currently used as anti-diabetic drugs for the inhibition of incretins. This study aims to construct the chemical feature based on pharmacophore models for dipeptidyl peptidase IV. The structure-based pharmacophore modeling has been employed to evaluate new inhibitors of DPP-4. A four-featured pharmacophore model was developed from crystal structure of DPP-4 enzyme with 4-(2-aminoethyl) benzenesulfonyl fluoride in its active site via pharmacophore constructing tool of Molecular Operating Environment (MOE) consisting F1 Hyd (hydrophobic region), F2 Hyd|Cat|Don (hydrophobic cationic and donor region), F3 Acc (acceptor region) and F4 Hyd (hydrophobic region). The generated pharmacophore model was used for virtual screening of in-house compound library (the available compounds which were used for initial screening to get the few compounds for the current studies). The resultant selected compounds, after virtual screening were further validated using in vitro assay. Furthermore, structure-activity relationship was carried out for the compounds possessing significant inhibition potential after docking studies. The binding free energy of analogs was evaluated via molecular mechanics generalized Born surface area (MM-GBSA) and Poisson-Boltzmann surface area (MM-PBSA) methods using AMBER 16 as a molecular dynamics (MD) simulation package. Based on potential findings, we report that selected candidates are more likely to be used as DPP-4 inhibitors or as starting leads for the development of novel and potent DPP-4 inhibitors.     

Ligand- and protein-based modeling studies of the inhibitors of human cytochrome P450 2D6 and a virtual screening for potential inhibitors from the Chinese herbal medicine, Scutellaria baicalensis (Huangqin,Baikal Skullcap)

We have previously examined the binding patterns of various substrates to human cytochrome P450 2D6 (CYP2D6) using a series of molecular modeling methods. In this study, we further explored the binding modes of various types of inhibitors to CYP2D6 using a combination of ligand- and protein-based modeling approaches. Firstly, we developed and validated a pharmacophore model for CYP2D6 inhibitors, which consisted of two hydrophobic features and one hydrogen bond acceptor feature. Secondly, we constructed and validated a quantitative structure-activity relationship (QSAR) model for CYP2D6 inhibitors which gave a poor to moderate prediction accuracy. Thirdly, a panel of CYP2D6 inhibitors were subject to molecular docking into the active site of wild-type and mutated CYP2D6 enzyme. We demonstrated that 8 residues in the active site (Leu213, Glu216, Ser217, Gln244, Asp301, Ser304, Ala305, and Phe483) played an important role in the binding to the inhibitors via hydrogen bond formation and/or π-π stacking interaction. Apparent changes in the binding modes of the inhibitors have been observed with Phe120Ile, Glu216Asp, Asp301Glu mutations in CYP2D6. Finally, we screened for potential binders/inhibitors from the Chinese herbal medicine Scutellaria baicalensis (Huangqin, Baikal Skullcap) using the established pharmacophore model for CYP2D6 inhibitors and molecular docking approach. Overall, 18 out of 40 compounds from S. baicalensis were mapped to the pharmacophore model of CYP2D6 inhibitors and most herbal compounds from S. baicalensis could be docked into the active site of CYP2D6. Our study has provided insights into the molecular mechanisms of interaction of synthetic and herbal compounds with human CYP2D6 and further benchmarking studies are needed to validate our modeling and virtual screening results.     

Exploring EZH2-Proteasome Dual-Targeting Drug Discovery through a Computational Strategy to Fight Multiple Myeloma

Multiple myeloma is an incurable plasma cell neoplastic disease representing about 10–15% of all haematological malignancies diagnosed in developed countries. Proteasome is a key player in multiple myeloma and proteasome inhibitors are the current first-line of treatment. However, these are associated with limited clinical efficacy due to acquired resistance. One of the solutions to overcome this problem is a polypharmacology approach, namely combination therapy and multitargeting drugs. Several polypharmacology avenues are currently being explored. The simultaneous inhibition of EZH2 and Proteasome 20S remains to be investigated, despite the encouraging evidence of therapeutic synergy between the two. Therefore, we sought to bridge this gap by proposing a holistic in silico strategy to find new dual-target inhibitors. First, we assessed the characteristics of both pockets and compared the chemical space of EZH2 and Proteasome 20S inhibitors, to establish the feasibility of dual targeting. This was followed by molecular docking calculations performed on EZH2 and Proteasome 20S inhibitors from ChEMBL 25, from which we derived a predictive model to propose new EZH2 inhibitors among Proteasome 20S compounds, and vice versa, which yielded two dual-inhibitor hits. Complementarily, we built a machine learning QSAR model for each target but realised their application to our data is very limited as each dataset occupies a different region of chemical space. We finally proceeded with molecular dynamics simulations of the two docking hits against the two targets. Overall, we concluded that one of the hit compounds is particularly promising as a dual-inhibitor candidate exhibiting extensive hydrogen bonding with both targets. Furthermore, this work serves as a framework for how to rationally approach a dual-targeting drug discovery project, from the selection of the targets to the prediction of new hit compounds.     

Pharmacophore modeling,virtual screening and molecular docking of ATPase inhibitors of HSP70

Heat shock protein 70 is an effective anticancer target as it influences many signaling pathways. Hence the study investigated the important pharmacophore feature required for ATPase inhibitors of HSP70 by generating a ligand based pharmacophore model followed by virtual based screening and subsequent validation by molecular docking in Discovery studio V4.0. The most extrapolative pharmacophore model (hypotheses 8) consisted of four hydrogen bond acceptors. Further validation by external test set prediction identified 200 hits from Mini Maybridge, Drug Diverse, SCPDB compounds and Phytochemicals. Consequently, the screened compounds were refined by rule of five, ADMET and molecular docking to retain the best competitive hits. Finally Phytochemical compounds Muricatetrocin B, Diacetylphiladelphicalactone C, Eleutheroside B and 5-(3-{[1-(benzylsulfonyl)piperidin-4-yl]amino}phenyl)- 4-bromo-3-(carboxymethoxy)thiophene-2-carboxylic acid were obtained as leads to inhibit the ATPase activity of HSP70 in our findings and thus can be proposed for further in vitro and in vivo evaluation.     

Pharmacophore anchor models of ATAT1 to discover potential inhibitors and lead optimization

Post-translation modification of microtubules is associated with many diseases like cancer. Alpha Tubulin Acetyltransferase 1 (ATAT1) is a major enzyme that acetylates ‘Lys-40’ in alpha-tubulin on the luminal side of microtubules and is a drug target that lacks inhibitors. Here, we developed pharmacophore anchor models of ATAT1 which were constructed statistically using thousands of docked compounds, for drug design and investigating binding mechanisms. Our models infer the compound moiety preferences with the physico-chemical properties for the ATAT1 binding site. The results from the pharmacophore anchor models show the three main sub-pockets, including S1 acetyl site, S2 adenine site, and S3 diphosphate site with anchors, where conserved moieties interact with respective sub-pocket residues in each site and help in guiding inhibitor discovery. We validated these key anchors by analyzing 162 homologous protein sequences (>99 species) and over 10 structures with various bound ligands and mutations. Our results were consistent with previous works also providing new interesting insights. Our models applied in virtual screening predicted several ATAT1 potential inhibitors. We believe that our model is useful for future inhibitor discovery and for guiding lead optimization.     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

Selective inhibition of 6-phosphogluconate dehydrogenase from Trypanosoma brucei

A number of triphenylmethane derivatives have been screened against 6-phosphogluconate dehydrogenase from Trypanosoma brucei and sheep liver. Some of these compounds show good inhibition of the enzymes and also selectivity towards the parasite enzyme. Modelling was undertaken to dock the compounds into the active sites of both enzymes. Using a combination of DOCK 3.5 and FLEXIDOCK a correlation was obtained between docking score and both activity for the enzymes and selectivity. Visualisation of the docked structures of the inhibitors in the active sites of the enzymes yielded a possible explanation of the selectivity for the parasite enzyme.     

Pharmacophore and docking-based virtual screening approach for the design of new dual inhibitors of Janus kinase 1 and Janus kinase 2

Janus kinase 1 and 2, non-receptor protein tyrosine kinases, are implicated in various cancerous diseases. Involvement of these two enzymes in the pathways that stimulate cell proliferation in cancerous conditions makes them potential therapeutic targets for designing new dual-targeted agents for the treatment of cancer. In the present study, two separate pharmacophore models were developed and the best models for JAK1 (AAADH.25) and JAK2 (ADRR.92) were selected on the basis of their external predictive ability. Both models were subjected to a systematic virtual screening (VS) protocol using a PHASE database of 1.5 million molecules. The hits retrieved in VS were investigated for ADME properties to avoid selection of molecules with a poor pharmacokinetic profile. The molecules considered to be within the range of acceptable limits of ADME properties were further employed for docking simulations with JAK1 and JAK2 proteins to explore the final hits that possess structural features of both pharmacophore models as well as display essential interactions with both of them. Thus, the new molecules obtained in this way should show inhibitory activity against JAK1 and JAK2 and may serve as novel therapeutic agents for the treatment of cancerous disease conditions.     

Dihydropyrazole and dihydropyrrole structures based design of Kif15 inhibitors as novel therapeutic agents for cancer

Mitotic Kinesin motors, Eg5 and Kif15, have recently emerged as good targets for cancer as they play an inevitable role during mitosis. But, most of the Eg5 inhibitors were found ineffective when the cancer cells develop resistance to them by escalating the expression of Kif15 as alternative to Eg5. Therefore, the drugs that target Kif15 became necessary to be used either as a single or in combination with Eg5 inhibitors. The present study used 39 dihydropyrazole and 13 dihydropyrrole derivatives that were having in vitro inhibitory potential against kinesin motors to develop a common pharmacophore hypothesis AHRR and atom-based QSAR model. The model was used for virtual screening of ZINC database and the resultant hits were docked against Kif15. The four drug candidates with high docking score were examined for their activity and pharmacokinetic behaviour. Based on the results these drugs could be considered as lead candidates in further drug development for cancer.     

Ranking targets in structure-based virtual screening of three-dimensional protein libraries: methods and problems

Structure-based virtual screening is a promising tool to identify putative targets for a specific ligand. Instead of docking multiple ligands into a single protein cavity, a single ligand is docked in a collection of binding sites. In inverse screening, hits are in fact targets which have been prioritized within the pool of best ranked proteins. The target rate depends on specificity and promiscuity in protein-ligand interactions and, to a considerable extent, on the effectiveness of the scoring function, which still is the Achilles' heel of molecular docking. In the present retrospective study, virtual screening of the sc-PDB target library by GOLD docking was carried out for four compounds (biotin, 4-hydroxy-tamoxifen, 6-hydroxy-1,6-dihydropurine ribonucleoside, and methotrexate) of known sc-PDB targets and, several ranking protocols based on GOLD fitness score and topological molecular interaction fingerprint (IFP) comparison were evaluated. For the four investigated ligands, the fusion of GOLD fitness and two IFP scores allowed the recovery of most targets, including the rare proteins which are not readily suitable for statistical analysis, while significantly filtering out most false positive entries. The current survey suggests that selecting a small number of targets (<20) for experimental evaluation is achievable with a pure structure-based approach.