Kinetic assay for high-throughput screening of in vitro transthyretin amyloid fibrillogenesis inhibitors

Stabilization of tetrameric transthyretin (TTR) by binding of small ligands is a current strategy aimed at inhibiting amyloid fibrillogenesis in transthyretin-associated pathologies, such as senile systemic amyloidosis (SSA) and familial amyloidotic polyneuropathy (FAP). A kinetic assay is developed for rapid evaluation of compounds as potential in vitro inhibitors in a high-throughput screening format. It is based on monitoring the time-dependent increase of absorbance due to turbidity occurring by acid-induced protein aggregation. The method uses the highly amyloidogenic Y78F mutant of human transthyretin (heterogously expressed in Escherichia coli cells). Initial rates of protein aggregation at different inhibitor concentrations follow a monoexponential dose-response curve from which inhibition parameters are calculated. For the assay development, thyroid hormones and nonsteroidal antiinflamatory drugs were chosen among other reference compounds. Some of them are already known to be in vitro inhibitors of TTR amyloidogenesis. Analysis time is optimized to last 1.5 h, and the method is implemented in microtiter plates for screening of libraries of potential fibrillogenesis inhibitors.     

Comparison of virtual high-throughput screening methods for the identification of phosphodiesterase-5 inhibitors

Reliable and effective virtual high-throughput screening (vHTS) methods are desperately needed to minimize the expenses involved in drug discovery projects. Here, we present an improvement to the negative image-based (NIB) screening: the shape, the electrostatics, and the solvation state of the target protein's ligand-binding site are included into the vHTS. Additionally, the initial vHTS results are postprocessed with molecular mechanics/generalized Born surface area (MMGBSA) calculations to estimate the favorability of ligand-protein interactions. The results show that docking produces very good early enrichment for phosphodiesterase-5 (PDE-5); however, in general, the NIB and the ligand-based screening performed better with or without the added electrostatics. Furthermore, the postprocessing of the NIB screening results using MMGBSA calculations improved the early enrichment for the PDE-5 considerably, thus, making hit discovery affordable.     

Combining ethnopharmacology and virtual screening for lead structure discovery: COX-inhibitors as application example

Virtual screening of large libraries of organic compounds combined with pharmacological high throughput screening is widely used for drug discovery in the pharmaceutical industry. Our aim was to explore the efficiency of using a biased 3D database comprising secondary metabolites from antiinflammatory medicinal plants as a source for the virtual screening. For this study pharmacophore models of cyclooxygenase I and II (COX-1, COX-2), key enzymes in the inflammation process, were generated with structure-based as well as common feature based modeling, resulting in three COX hypotheses. Four different multiconfomational 3D databases limited in molecular weight between 300 and 700 Da were applied to the screening in order to compare and analyze the obtained hit rates. Two of them were created in-house (DIOS, NPD). The database DIOS consists of 2752 compounds from phytochemical reports of antiinflammatory medicinal plants described by the ethnopharmacological source 'De material medica' of Pedanius Dioscorides, whereas NPD contains almost 80,000 compounds gathered arbitrarily from natural sources. In addition, two available multiconformational 3D libraries comprising marketed and development drug substances (DWI and NCI), mainly originating from synthesis, were used for comparison. As a test of the pharmacophore models' capability in natural sources, the models were used to search for known COX inhibitory natural products. This was achieved with some exceptions, which are discussed in the paper. Depending on the hypothesis used, DWI and NCI library searches produced hit rates in the range of 6.6% to 13.7%. A slight increase of the number of molecules assessed for binding was achieved with the database of natural products (NPD). Using the biased 3D database DIOS, however, the average increase of efficiency reached 77% to 133% compared to the hit rates resulting from WDI and NCI. The statistical benefit of a combination of an ethnopharmacological approach with the potential of computer aided drug discovery by in silico screening was demonstrated exemplified on the applied targets COX-1 and COX-2.     

Structure-based discovery of novel non-nucleosidic DNA alkyltransferase inhibitors: virtual screening and in vitro and in vivo activities

The human DNA-repair O (6)-alkylguanine DNA alkyltransferase (MGMT or hAGT) protein protects DNA from environmental alkylating agents and also plays an important role in tumor resistance to chemotherapy treatment. Available inhibitors, based on pseudosubstrate analogs, have been shown to induce substantial bone marrow toxicity in vivo. These deficiencies and the important role of MGMT as a resistance mechanism in the treatment of some tumors with dismal prognosis like glioblastoma multiforme, the most common and lethal primary malignant brain tumor, are increasing the attention toward the development of improved MGMT inhibitors. Here, we report the identification for the first time of novel non-nucleosidic MGMT inhibitors by using docking and virtual screening techniques. The discovered compounds are shown to be active in both in vitro and in vivo cellular assays, with activities in the low to medium micromolar range. The chemical structures of these new compounds can be classified into two families according to their chemical architecture. The first family corresponds to quinolinone derivatives, while the second is formed by alkylphenyl-triazolo-pyrimidine derivatives. The predicted inhibitor protein interactions suggest that the inhibitor binding mode mimics the complex between the excised, flipped out damaged base and MGMT. This study opens the door to the development of a new generation of MGMT inhibitors.     

The discovery of Kv1.5 blockers as a case study for the application of virtual screening approaches

Different virtual screening techniques are available as alternatives to high throughput screening. These different techniques have been rarely used together on the same target. We had the opportunity to do so in order to discover novel blockers of the voltage-dependent potassium channel Kv1.5, a potential target for the treatment of atrial fibrillation. Our corporate database was searched, using a protein-based pharmacophore, derived from a homology model, as query. As a result, 244 molecules were screened in vitro, 19 of them (7.8%) were found to be active. Five of them, belonging to five different chemical classes, exhibited IC50 values under 10 microM. The performance of this structure-based virtual screening protocol has been compared with those of similarity and ligand-based pharmacophore searches. The analysis of the results supports the conventional wisdom of using as many virtual screening techniques as possible in order to maximize the chance of finding as many chemotypes as possible.     

The identification of new ATAD2 bromodomain inhibitors: the application of combined ligand and structure-based virtual screening

Ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches were used to identify new inhibitors for ATAD2 bromodomain. The LBVS approach was used to search 23,129,083 clean compounds to identify compounds similar to an active compound with reported pIC₅₀ equal to 7.2. Based on LBVS results, 19 compounds were selected. To perform SBVS, by applying nine filters on 23,129,083 clean compounds, 1,057,060 compounds were selected. After performing SBVS on these selected compounds with idock software, 16 compounds with the lowest binding energies were selected. More accurate molecular docking analysis was performed on these 35 selected compounds by using iGEMDOCK software and six of them with the lowest binding energies were selected as hit compounds. These compounds were zinc36647229, zinc77969074, zinc13637358, zinc77971540, zinc12991296 and zinc19374204.     

Impact of scoring functions on enrichment in docking-based virtual screening: an application study on renin inhibitors

The docking program LigandFit/Cerius(2) has been used to perform shape-based virtual screening of databases against the aspartic protease renin, a target of determined three-dimensional structure. The protein structure was used in the induced fit binding conformation that occurs when renin is bound to the highly active renin inhibitor 1 (IC(50) = 2 nM). The scoring was calculated using several different scoring functions in order to get insight into the predictability of the magnitude of binding interactions. A database of 1000 diverse and druglike compounds, comprised of 990 members of a virtual database generated by using the iLib diverse software and 10 known active renin inhibitors, was docked flexibly and scored to determine appropriate scoring functions. All seven scoring functions used (LigScore1, LigScore2, PLP1, PLP2, JAIN, PMF, LUDI) were able to retrieve at least 50% of the active compounds within the first 20% (200 molecules) of the entire test database. A hit rate of 90% in the top 1.4% resulted using the quadruple consensus scoring of LigScore2, PLP1, PLP2, and JAIN. Additionally, a focused database was created with the iLib diverse software and used for the same procedure as the test database. Docking and scoring of the 990 focused compounds and the 10 known actives were performed. A hit rate of 100% in the top 8.4% resulted with use of the triple consensus scoring of PLP1, PLP2, and PMF. As expected, a ranking of the known active compounds within the focused database compared to the test database was observed. Adequate virtual screening conditions were derived empirically. They can be used for proximate docking and scoring application of compounds with putative renin inhibiting potency.     

Prediction of new chromene-based inhibitors of tubulin using structure-based virtual screening and molecular dynamics simulation methods

Multidrug resistance (MDR) is one of the serious problems in cancer research that causes failure in chemotherapy. Chromene-based compounds have been proven to be the novel anti-MDR agents for inhibiting proliferation of tumor cells through tubulin polymerization inhibition of by binding at the colchicine binding site. In this study, we screened a chromene-based database of small molecules using physicochemical, ADMET properties and molecular docking to identify potential hit compounds. In order to validate our hit compounds, molecular dynamics simulations and related analysis were carried out and the results suggest that our hit compounds (PubChem CIDs: 16814409, 17594471, 57367244 and 69899719) can prove to be potential inhibitors of tubulin. The in silico results show that the present hits, like colchicine, effectively suppressed the dynamic instability of microtubules and induced microtubule-depolymerization and cell cycle arrest.     

Integrating virtual screening and combinatorial chemistry for accelerated drug discovery

Virtual screening is increasingly being used in drug discovery programs with a growing number of successful applications. Experimental methodologies developed to speed up the drug discovery processes include high-throughput screening and combinatorial chemistry. The complementarities between computational and experimental screenings have been recognized and reviewed in the literature. Computational methods have also been used in the combinatorial chemistry field, in particular in library design. However, the integration of computational and combinatorial chemistry screenings has been attempted only recently. Combinatorial libraries (experimental or virtual) represent a notable source of chemically related compounds. Advances in combinatorial chemistry and deconvolution strategies, have enabled the rapid exploration of novel and dense regions in the chemical space. The present review is focused on the integration of virtual and experimental screening of combinatorial libraries. Applications of virtual screening to discover novel anticancer agents and our ongoing efforts towards the integration of virtual screening and combinatorial chemistry are also discussed.     

A multilayer screening approach toward the discovery of novel Pf-DHFR inhibitors

A small yet diverse xanthone library was build and computationally docked against wild type Pf-DHFR by Molegro Virtual Docker (MolDock). For analysis of results an integrated approach based on re-ranking, scaling (based on heavy atom counts), pose clustering and visual inspection was implemented. Standard methods such as self-docking (for docking), EF analysis, average rank determinations (for size normalization), and cluster quality indices (for pose clustering) were used for validation of results. Three compounds X5, X113A and X164B displayed contact footprints similar to the known inhibitors with good scores. Finally, 16 compounds were extracted from ZINC data base by similarity based screening, docking score and drug/lead likeness. Out of these 16 compounds, 11 displayed very close contact footprints to experimentally known inhibitors, indicating there potential utility in further drug discovery efforts.     

Novel small-molecule inhibitors of arylamine N-acetyltransferases: drug discovery by high-throughput screening

Arylamine N-acetyltransferases (NATs) are a family of enzymes found in eukaryotes and prokaryotes. While the precise endogenous function of NAT remains unknown for most organisms, recent evidence has shown that the expression of human NAT1 is up-regulated in estrogen receptor positive breast cancer. Additionally, NAT in mycobacteria is required for mycobacterial cell wall biosynthesis and survival of the organisms within macrophage. It is therefore important to develop small molecule inhibitors of NATs as molecular tools to study the function of NATs in various organisms. Such inhibitors may also prove useful in future drug design, for example in the development of anti tubercular agents. We describe a high-throughput screen of a proprietary library of 5016 drug-like compounds against three prokaryotic NAT enzymes and two eukaryotic NAT enzymes.     

Discovery of novel human acrosin inhibitors by virtual screening

Human acrosin is an attractive target for the discovery of male contraceptive drugs. For the first time, structure-based drug design was applied to discover structurally diverse human acrosin inhibitors. A parallel virtual screening strategy in combination with pharmacophore-based and docking-based techniques was used to screen the SPECS database. From 16 compounds selected by virtual screening, a total of 10 compounds were found to be human acrosin inhibitors. Compound 2 was found to be the most potent hit (IC₅₀ = 14 μM) and its binding mode was investigated by molecular dynamics simulations. The hit interacted with human acrosin mainly through hydrophobic and hydrogen-bonding interactions, which provided a good starting structure for further optimization studies.     

Integration of virtual and high throughput screening in lead discovery settings

In the last decade mass screening strategies became the main source of leads in drug discovery settings. Although high throughput (HTS) and virtual screening (VS) realize the same concept the different nature of these lead discovery strategies (experimental vs theoretical) results that they are typically applied separately. The majority of drug leads are still identified by hit-to-lead optimization of screening hits. Structural information on the target as well as on bound ligands, however, make structure-based and ligand-based virtual screening available for the identification of alternative chemical starting points. Although, the two techniques have rarely been used together on the same target, here we review the existing prominent studies on their true integration. Various approaches have been shown to apply the combination of HTS and VS and to better use them in lead generation. Although several attempts on their integration have only been considered at a conceptual level, there are numerous applications underlining its relevance that early-stage pharmaceutical drug research could benefit from a combined approach.     

Analysis of data fusion methods in virtual screening: theoretical model

This paper presents a theoretical model of how data fusion can be used to combine the results of multiple similarity searches of chemical databases. The model is based on frequency distributions of similarity values that are fused using a multiple integration over regions defined by the particular fusion rule that is being applied. For pairwise fusion, the resulting double integrals are straightforward to evaluate for simple model distributions. Similarity values for recovered-active and recovered-nonactive frequency distributions are independently modeled using a constant background, linearly biased terms, and a first-order correlated term. The model shows that two standard fusion rules can give performance enhancements in some cases but that the results of fusion are dependent on many factors that, taken together, can lead to seemingly inconsistent levels of enhancement.     

Physics-based scoring of protein-ligand complexes: enrichment of known inhibitors in large-scale virtual screening

We demonstrate that using an all-atom molecular mechanics force field combined with an implicit solvent model for scoring protein-ligand complexes is a promising approach for improving inhibitor enrichment in the virtual screening of large compound databases. The rescoring method is evaluated by the extent to which known binders for nine diverse, therapeutically relevant enzymes are enriched against a background of approximately 100,000 drug-like decoys. The improvement in enrichment is most robust and dramatic within the top 1% of the ranked database, that is, the first thousand compounds; below the first few percent of the ranked database, there is little overall improvement. The improved early enrichment is likely due to the more realistic treatment of ligand and receptor desolvation in the rescoring procedure. We also present anecdotal but encouraging results assessing the ability of the rescoring method to predict specificity of inhibitors for structurally related proteins.