Virtual screening to enrich a compound collection with CDK2 inhibitors using docking, scoring, and composite scoring models

Discovery of dual kinase inhibitors targeting VEGFR2 and FAK: structure-based pharmacophore modeling, virtual screening, and molecular docking studies

VEGFR2 and FAK signaling pathways are interconnected and have synergistic effects on tumor angiogenesis, growth, and metastasis. Thus, instead of the conventional targeting of each of these proteins individually with a specific inhibitor, the present work aimed to discover novel dual inhibitors targeting both VEGFR2 and FAK exploiting their association. To this end, receptor-based pharmacophore modeling technique was opted to generate 3D pharmacophore models for VEGFR2 and FAK type II kinase inhibitors. The generated pharmacophore models were validated by assessing their ability to discriminate between active and decoy compounds in a pre-compiled test set of VEGFR2 and FAK active compounds and decoys. ZINCPharmer web tool was then used to screen the ZINC database purchasable subset using the validated pharmacophore models retrieving 42,616 hits for VEGFR2 and 28,475 hits for FAK. Subsequently, they were filtered using various filters leaving 13,023 and 6,832 survived compounds for VEGFR2 and FAK, respectively, with 124 common compounds. Based on molecular docking simulations, thirteen compounds were found to satisfy all necessary interactions with VEGFR2 and FAK kinase domains. Thus, they are predicted to have a possible dual VEGFR2/FAK inhibitory activity. Finally, SwissADME web tool showed that compound ZINC09875266 is not only promising in terms of binding pattern to our target kinases, but also in terms of pharmacokinetic properties.

Consensus Analyses in Molecular Docking Studies Applied to Medicinal Chemistry

The increasing number of computational studies in medicinal chemistry involving molecular docking has put the technique forward as promising in Computer-Aided Drug Design. Considering the main method in the virtual screening based on the structure, consensus analysis of docking has been applied in several studies to overcome limitations of algorithms of different programs and mainly to increase the reliability of the results and reduce the number of false positives. However, some consensus scoring strategies are difficult to apply and, in some cases, are not reliable due to the small number of datasets tested. Thus, for such a methodology to be successful, it is necessary to understand why, when and how to use consensus docking. Therefore, the present study aims to present different approaches to docking consensus, applications, and several scoring strategies that have been successful and can be applied in future studies.

Identification of novel CDK2 inhibitors by a multistage virtual screening method based on SVM, pharmacophore and docking model

Cyclin-dependent kinase 2 (CDK2) is the family of Ser/Thr protein kinases that has emerged as a highly selective with low toxic cancer therapy target. A multistage virtual screening method combined by SVM, protein-ligand interaction fingerprints (PLIF) pharmacophore and docking was utilised for screening the CDK2 inhibitors. The evaluation of the validation set indicated that this method can be used to screen large chemical databases because it has a high hit-rate and enrichment factor (80.1% and 332.83 respectively). Six compounds were screened out from NCI, Enamine and Pubchem database. After molecular dynamics and binding free energy calculation, two compounds had great potential as novel CDK2 inhibitors and they also showed selective inhibition against CDK2 in the kinase activity assay.

In Silico Drug-Target Profiling

Pharmacological science is trying to establish the link between chemicals, targets, and disease-related phenotypes. A plethora of chemical proteomics and structural data have been generated, thanks to the target-based approach that has dominated drug discovery at the turn of the century. There is an invaluable source of information for in silico target profiling. Prediction is based on the principle of chemical similarity (similar drugs bind similar targets) or on first principles from the biophysics of molecular interactions. In the first case, compound comparison is made through ligand-based chemical similarity search or through classifier-based machine learning approach. The 3D techniques are based on 3D structural descriptors or energy-based scoring scheme to infer a binding affinity of a compound with its putative target. More recently, a new approach based on compound set metric has been proposed in which a query compound is compared with a whole of compounds associated with a target or a family of targets. This chapter reviews the different techniques of in silico target profiling and their main applications such as inference of unwanted targets, drug repurposing, or compound prioritization after phenotypic-based screening campaigns.

Bayesian methods in virtual screening and chemical biology

The Naïve Bayesian Classifier, as well as related classification and regression approaches based on Bayes' theorem, has experienced increased attention in the cheminformatics world in recent years. In this contribution, we first review the mathematical framework on which Bayes' methods are built, and then continue to discuss implications of this framework as well as practical experience under which conditions Bayes' methods give the best performance in virtual screening settings. Finally, we present an overview of applications of Bayes' methods to both virtual screening and the chemical biology arena, where applications range from bridging phenotypic and mechanistic space of drug action to the prediction of ligand-target interactions.

Binding Mode Prediction of PDE4 Inhibitors: A Comparison of Modelling Methods

Molecular modelling is widely used in support of medicinal chemistry programs, with several theoretical approaches used in attempts to expedite drug discovery. In this study, three methods – molecular docking (Glide), shape similarity (ROCS), and pharmacophore modelling (Phase) – were evaluated for their ability to reproduce experimentally determined binding modes of 25 PDE4 inhibitors, identified by X-ray crystallography. Molecular docking was able to provide a good approximation (RMSD less than 2 Å) in 59% of cases, when considering the top binding pose. The pairwise comparisons, using molecular shape similarity, gave good matches in 42% of cases. Pharmacophore models were unable to predict good binding modes for a series of PDE4 inhibitors.

Binding mode analysis of topoisomerase inhibitors, 6-arylamino-7-chloro-quinazoline-5,8-diones, within the cleavable complex of human topoisomerase I and DNA

A series of 6-arylamino-7-chloro-quinazoline-5,8-diones have been evaluated as novel human topoisomerase I (TOP1) inhibitors based on the antitumor activity of 1,4-naphthoquinone. Besides their in vitro cytotoxicity, their ability to inhibit human TOP1-DNA in vitro was tested with human TOP1 and a supercoiled (Form I) plasmid substrate DNA (Park et al., 2004). Using the flexible docking program, QXP, we have developed ternary complex models by docking camptothecin and ten 6-arylamino-7-chloro-quinazoline-5,8-dione analogs into the X-ray crystal structure of the human TOP1-DNA binary complex. The compound binding modes substantiated their potential inhibitory activities against TOP1 in the relaxation assay. Compounds whose templates the 6-arylamino-7-chloro-quinazoline-5,8-dione moiety intercalated between the -1 and +1 base pairs of the scissile strand showed good inhibitory activities. The template of compounds with poor inhibitory activities intercalated between the DNA base pairs of the nonscissile strand. The interaction of the compounds and the human TOP1-DNA binary complex were stabilized by an array of hydrogen bonds and hydrophobic interactions with the TOP1 residues, DNA bases, and water molecules. Docking results from the QXP program suggested potential binding modes of each non-CPT type compound in the human TOP1-DNA cleavable complex, which could provide a rational basis for future TOP1 inhibitor development.

Can we use docking and scoring for hit-to-lead optimization?

Docking and scoring is currently one of the tools used for hit finding and hit-to-lead optimization when structural information about the target is known. Docking scores have been found useful for optimizing ligand binding to reproduce experimentally observed binding modes. The question is, can docking and scoring be used reliably for hit-to-lead optimization? To illustrate the challenges of scoring for hit-to-lead optimization, the relationship of docking scores with experimentally determined IC(50) values measured in-house were tested. The influences of the particular target, crystal structure, and the precision of the scoring function on the ability to differentiate between actives and inactives were analyzed by calculating the area under the curve of receiver operator characteristic curves for docking scores. It was found that for the test sets considered, MW and sometimes ClogP were as useful as GlideScores and no significant difference was observed between SP and XP scores for differentiating between actives and inactives. Interpretation by an expert is still required to successfully utilize docking and scoring in hit-to-lead optimization.

Molecular docking

Molecular docking is a key tool in structural molecular biology and computer-assisted drug design. The goal of ligand-protein docking is to predict the predominant binding mode(s) of a ligand with a protein of known three-dimensional structure. Successful docking methods search high-dimensional spaces effectively and use a scoring function that correctly ranks candidate dockings. Docking can be used to perform virtual screening on large libraries of compounds, rank the results, and propose structural hypotheses of how the ligands inhibit the target, which is invaluable in lead optimization. The setting up of the input structures for the docking is just as important as the docking itself, and analyzing the results of stochastic search methods can sometimes be unclear. This chapter discusses the background and theory of molecular docking software, and covers the usage of some of the most-cited docking software.

Finding ligands for G protein-coupled receptors based on the protein–compound affinity matrix

We developed a novel method of identifying new active ligands based on information related to known active compounds using protein-compound docking simulations, even when the tertiary structure of the actual target receptor protein is unknown. This method was used to find ligands of G protein-coupled receptors (GPCRs), i.e., agonists and antagonists of histamine, adrenaline, serotonin and dopamine receptors. The principal component analysis (PCA) method was applied to the protein-compound affinity matrix, which was given by thorough docking calculations between sets of many protein pockets and chemical compounds. The set of protein pockets did not necessary include the target protein. Each compound was depicted as a point in the PCA space. Compounds in a sphere, whose center was set to the known active compound in the multi-dimensional PCA space or to the average position of several known active compounds, were selected as candidate-hit compounds. Our method was found to be effective for finding the ligands of GPCRs based on known native ligands, even when only the soluble protein structures were used in the docking simulations.

Noise reduction method for molecular interaction energy: application to in silico drug screening and in silico target protein screening

We developed a new method to improve the accuracy of molecular interaction data using a molecular interaction matrix. This method was applied to enhance the database enrichment of in silico drug screening and in silico target protein screening using a protein-compound affinity matrix calculated by a protein-compound docking software. Our assumption was that the protein-compound binding free energy of a compound could be improved by a linear combination of its docking scores with many different proteins. We proposed two approaches to determine the coefficients of the linear combination. The first approach is based on similarity among the proteins, and the second is a machine-learning approach based on the known active compounds. These methods were applied to in silico screening of the active compounds of several target proteins and in silico target protein screening.

Synthetic curcuminoids modulate the arachidonic acid metabolism of human platelet 12-lipoxygenase and reduce sprout formation of human endothelial cells

Platelet 12-lipoxygenase (P-12-LOX) is overexpressed in different types of cancers, including prostate cancer, and the level of expression is correlated with the grade of this cancer. Arachidonic acid is metabolized by 12-LOX to 12(S)-hydroxyeicosatetraenoic acid [12(S)-HETE], and this biologically active metabolite is involved in prostate cancer progression by modulating cell proliferation in multiple cancer-related pathways inducing angiogenesis and metastasis. Thus, inhibition of P-12-LOX can reduce these two processes. Several lipoxygenase inhibitors are known, including plant and mammalian lipoxygenases, but only a few of them are known inhibitors of P-12-LOX. Curcumin is one of these lipoxygenase inhibitors. Using a homology model of the three-dimensional structure of human P-12-LOX, we did computational docking of synthetic curcuminoids (curcumin derivatives) to identify inhibitors superior to curcumin. Docking of the known inhibitors curcumin and NDGA to P-12-LOX was used to optimize the docking protocol for the system in study. Over 75% of the compounds of interest were successfully docked into the active site of P-12-LOX, many of them sharing similar binding modes. Curcuminoids that did not dock into the active site did not inhibit P-12-LOX. From a set of the curcuminoids that were successfully docked and selected for testing, two were found to inhibit human lipoxygenase better than curcumin. False-positive curcuminoids showed high LogP (theoretical) values, indicating poor water solubility, a possible reason for lack of inhibitory activity or/and nonrealistic binding. Additionally, the curcuminoids inhibiting P-12-LOX were tested for their ability to reduce sprout formation of endothelial cells (in vitro model of angiogenesis). We found that only curcuminoids inhibiting human P-12-LOX and the known inhibitor NDGA reduced sprout formation. Only limited inhibition of sprout formation at approximately IC(50) concentrations has been seen. At IC(50), a substantial amount of 12-HETE can be produced by lipoxygenase, providing a stimulus for angiogenic sprouting of endothelial cells. Increasing the concentration of lipoxygenase inhibitors above IC(50), thus decreasing the concentration of 12(S)-HETE produced, greatly reduced sprout formation for all inhibitors tested. This universal event for all tested lipoxygenase inhibitors suggests that the inhibition of sprout formation was most likely due to the inhibition of human P-12-LOX but not other cancer-related pathways.