Multistep Virtual Screening Research Articles

Designing of inhibitors against CTX-M-15 type β-lactamase: potential drug candidate against β-lactamases-producing multi-drug-resistant bacteria

CTX-M-15 are the most prevalent types of β-lactamases that hydrolyze almost all antibiotics of β-lactam group lead to multiple-antibiotic resistance in bacteria. Three β-lactam inhibitors are available for use in combination with different antibiotics of cephalosporine group against the CTX-M-15-producing strains. Therefore, strategies to identify novel anti β-lactamase agents with specific mechanisms of action are the need of an hour. In this study, we screened three novel non-β-lactam inhibitors against CTX-M-15 by multi-step virtual screening approach. The potential for virtually screened drugs was estimated through in vitro cell assays. Hence, we proposed a study to understand the binding mode of CTX-M-15 with inhibitors using bioinformatics and experimental approach. We calculated the dissociation constants (Kd), association constant (Ka), stoichiometry (n) and binding energies (ΔG) of compounds with the respective targets. Molecular dynamic simulation carried out for 25 ns, revealed that these complexes were found stable throughout the simulation with relative RMSD in acceptable range. Moreover, microbiological and kinetic studies further confirmed high efficacies of these inhibitors by reducing the minimum inhibitory concentration (MIC) and catalysis of antibiotics by β-lactamases in the presence of inhibitors. Therefore, we conclude that these potential inhibitors may be used as a lead molecule for future drug candidates against β-lactamases-producing bacteria.

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.

Identification of novel MEK1 inhibitors by pharmacophore and docking based virtual screening

Mitogen-activated protein kinase/mitogen-activated protein kinase kinase1 plays a key role in the Ras/Raf/MEK/ERK signaling pathway associated with cell growth, differentiation and development. In an effort to identify novel mitogen-activated protein kinase kinase1 inhibitors, an efficient multistep virtual screening approach was presented, including ligand-based pharmacophore (Hypo1) screening, physicochemical property filtering, and validated optimal docking analysis. Finally, six compounds were purchased and tested for their inhibitory activities in kinetics studies and cellular assays. Among them, compound M4 exhibited potent inhibition against mitogen-activated protein kinase kinase1 (IC50 = 3.5 μM) and modest proliferation inhibition in human HT-29 colon tumor line (IC50 = 13.6 μM) and human melanoma A375 cell line (IC50 = 16.2 μM). Taken together, our virtual screening might be useful for the mitogen-activated protein kinase kinase1 inhibitor research and M4 represents a new scaffold deserving further structural optimization for the treatment of mitogen-activated protein kinase kinase1-associated cancers. A ligand-based pharmacophore of MEK1 inhibitors was constructed and validated, then used to screen the SPECS compound library combined with optimal molecular docking (Glide XP), followed by biochemical and cellular assays.

Multi-step virtual screening to develop selective DYRK1A inhibitors

Developing selective inhibitors for a particular kinase remains a major challenge in kinase-targeted drug discovery. Here we performed a multi-step virtual screening for dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors by focusing on the selectivity for DYRK1A over cyclin-dependent kinase 5 (CDK5). To examine the key factors contributing to the selectivity, we constructed logistic regression models to discriminate between actives and inactives for DYRK1A and CDK5, respectively, using residue-based binding free energies. The residue-based parameters were calculated by molecular mechanics-generalized Born surface area (MM-GBSA) decomposition methods for kinase–ligand complexes modeled by computer ligand docking. Based on the findings from the logistic regression models, we built a three-dimensional (3D) pharmacophore model and chose filter criteria for the multi-step virtual screening. The virtual hit compounds obtained from the screening were assessed for their inhibitory activities against DYRK1A and CDK5 by in vitro assay. Our screening identified two novel selective DYRK1A inhibitors with IC50 values of several μM for DYRK1A and >100μM for CDK5, which can be further optimized to develop more potent selective DYRK1A inhibitors.

Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach.

Simultaneous inhibition of VEGFR2 and Src may enhance the efficacy of VEGFR2-targeted cancer therapeutics. Hence, development of dual inhibitors on VEGFR2 and Src can be a useful strategy for such treatments. A multistep virtual screening protocol, comprising ligand-based support vector machines method, drug-likeness rules filter and structure-based molecular docking, was developed and employed to identify dual inhibitors of VEGFR2 and Src from a large commercial chemical library. Kinase inhibitory assays and cell viability assays were then used for experimental validation. A set of compounds belonging to six different molecular scaffolds was identified and sent for biological evaluation. Compound 3c belonging to the 2-amino-3-cyanopyridine scaffold exhibited good antiproliferative effect and dual-target activities against VEGFR2 and Src. This study demonstrated the ability of the multistep virtual screening approach to identify novel multitarget agents.

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 (∼1.3million 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 3.8μM, respectively).

Identification of small-molecule inhibitors against SecA by structure-based virtual ligand screening.

The rapid rise of antibiotic-resistant bacteria is one of the major concerns in modern medicine. Therefore, to treat bacterial infections, there is an urgent need for new antibacterials-preferably directed against alternative bacterial targets. One such potential target is the preprotein translocation motor SecA. SecA is a peripheral membrane ATPase and a key component of the Sec secretion pathway, the major route for bacterial protein export across or into the cytoplasmic membrane. As SecA is essential for bacterial viability, ubiquitous and highly conserved in bacteria, but not present in eukaryotic cells, it represents an attractive antibacterial target. Using an in silico approach, we have defined several potentially druggable and conserved pockets on the surface of SecA. We show that three of these potentially druggable sites are important for SecA function. A starting collection of ~500 000 commercially available small-molecules was virtually screened against a predicted druggable pocket in the preprotein-binding domain of Escherichia coli SecA using a multi-step virtual ligand screening protocol. The 1040 top-scoring molecules were tested in vitro for inhibition of the translocation ATPase activity of E. coli SecA. Five inhibitors of the translocation ATPase, and not of basal or membrane ATPase, were identified with IC50 values <65 μm. The most potent inhibitor showed an IC50 of 24 μm. The antimicrobial activity was determined for the five most potent SecA inhibitors. Two compounds were found to possess weak antibacterial activity (IC50 ~198 μm) against E. coli, whereas some compounds showed moderate antibacterial activity (IC50 ~100 μm) against Staphylococcus aureus.

Discovery of novel new Delhi metallo-β-lactamases-1 inhibitors by multistep virtual screening.

The emergence of NDM-1 containing multi-antibiotic resistant "Superbugs" necessitates the needs of developing of novel NDM-1inhibitors. In this study, we report the discovery of novel NDM-1 inhibitors by multi-step virtual screening. From a 2,800,000 virtual drug-like compound library selected from the ZINC database, we generated a focused NDM-1 inhibitor library containing 298 compounds of which 44 chemical compounds were purchased and evaluated experimentally for their ability to inhibit NDM-1 in vitro. Three novel NDM-1 inhibitors with micromolar IC50 values were validated. The most potent inhibitor, VNI-41, inhibited NDM-1 with an IC50 of 29.6 ± 1.3 μM. Molecular dynamic simulation revealed that VNI-41 interacted extensively with the active site. In particular, the sulfonamide group of VNI-41 interacts directly with the metal ion Zn1 that is critical for the catalysis. These results demonstrate the feasibility of applying virtual screening methodologies in identifying novel inhibitors for NDM-1, a metallo-β-lactamase with a malleable active site and provide a mechanism base for rational design of NDM-1 inhibitors using sulfonamide as a functional scaffold.

A Novel Combined Pharmacophore Mapping and Quantitative Structure Selectivity Relationship Analysis for the Development of Potent and Selective Human Aldose Reductase Inhibitors

Present study describes the use of multiple pharmacophore mapping and three dimensional quantitative structure selectivity relationship (3D-QSSR) analysis for human aldose reductase (hALR2) and human aldehyde reductase (hALR1) inhibitors to develop selective molecules against hALR2. Two pharmacophore models one each for highly active hALR2 inhibitors and highly active hALR1 inhibitors were generated. Atom based 3D-QSSR analysis was performed employing docking based alignment of molecules. Sequential multi-step virtual screening was performed to screen PHASE database of approximately 1.5 million molecules. Finally, 22 potent and highly selective hALR2 inhibitors were obtained as potential hits using virtual screening protocol. This study of multiple pharmacophore mapping combined with 3D-QSSR analysis provided deep insight into the structural features requirement for selectivity and may be used as novel tool to design new selective and potent hALR2 inhibitors.

Novel Insights of Structure-Based Modeling for RNA-Targeted Drug Discovery

Substantial progress in RNA biology highlights the importance of RNAs (e.g., microRNAs) in diseases and the potential of targeting RNAs for drug discovery. However, the lack of RNA-specific modeling techniques demands the development of new tools for RNA-targeted rational drug design. Herein, we implemented integrated approaches of accurate RNA modeling and virtual screening for RNA inhibitor discovery with the most comprehensive evaluation to date of five docking and 11 scoring methods. For the first time, statistical analysis was heavily employed to assess the significance of our predictions. We found that GOLD:GOLD Fitness and rDock:rDock_solv could accurately predict the RNA ligand poses, and ASP rescoring further improved the ranking of ligand binding poses. Due to the weak correlations (R(2) < 0.3) of existing scoring with experimental binding affinities, we implemented two new RNA-specific scoring functions, iMDLScore1 and iMDLScore2, and obtained better correlations with R(2) = 0.70 and 0.79, respectively. We also proposed a multistep virtual screening approach and demonstrated that rDock:rDock_solv together with iMDLScore2 rescoring obtained the best enrichment on the flexible RNA targets, whereas GOLD:GOLD Fitness combined with rDock_solv rescoring outperformed other methods for rigid RNAs. This study provided practical strategies for RNA modeling and offered new insights into RNA-small molecule interactions for drug discovery.

Identification of diverse dipeptidyl peptidase IV inhibitors via structure-based virtual screening

Dipeptidyl peptidase IV (DPP4) is an important target for the treatment of type II diabetes mellitus. Inhibition of DPP4 will improve glycemic control in such patients by preventing the rapid breakdown and thereby prolonging the physiological actions of incretin hormones. Known DPP4 inhibitors (including marketed drugs and those drug candidates) appear to share similar structural features: the cyanopyrrolidine moieties, the xanthenes/pyrimidine parts and amino-like linkages. In this study, a multi-step virtual screening strategy including both rigid and flexible docking was employed to search for novel structures with DPP4 inhibition. From SPECS database, consisting of over 190,000 commercially available compounds, 99 virtual hits were picked up and 15 of them were eventually identified to have DPP4 inhibitory activities at 5 ~ 50μM. Diverse structures of our compounds were out of usual structural categories. Hence a pharmacophore model was built to further explore their common binding features on the enzyme. The results provided a new pathway for the discovery of DPP4 inhibitors and would be helpful for further optimization of DPP4 inhibitors.

Development of a novel class of B-RafV600E-selective inhibitors through virtual screening and hierarchical hit optimization

Oncogenic mutations in critical nodes of cellular signaling pathways have been associated with tumorigenesis and progression. The B-Raf protein kinase, a key hub in the canonical MAPK signaling cascade, is mutated in a broad range of human cancers and especially in malignant melanoma. The most prevalent B-Raf(V600E) mutant exhibits elevated kinase activity and results in constitutive activation of the MAPK pathway, thus making it a promising drug target for cancer therapy. Herein, we describe the development of novel B-Raf(V600E) selective inhibitors via multi-step virtual screening and hierarchical hit optimization. Nine hit compounds with low micromolar IC(50) values were identified as B-Raf(V600E) inhibitors through virtual screening. Subsequent scaffold-based analogue searching and medicinal chemistry efforts significantly improved both the inhibitor potency and oncogene selectivity. In particular, compounds 22f and 22q possess nanomolar IC(50) values with selectivity for B-Raf(V600E)in vitro and exclusive cytotoxicity against B-Raf(V600E) harboring cancer cells.

Combining 2D and 3D in silico methods for rapid selection of potential PDE5 inhibitors from multimillion compounds’ repositories: biological evaluation

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach when starting new drug discovery projects. If structures of active compounds are available rapid 2D similarity search can be performed on multimillion compounds' databases. This in silico approach can be combined with physico-chemical parameter filtering based on the property space of the active compounds and 3D virtual screening if the structure of the target protein is available. A multi-step virtual screening procedure was developed and applied to select potential phosphodiesterase 5 (PDE5) inhibitors in real time. The combined 2D/3D in silico method resulted in the identification of 14 novel PDE5 inhibitors with <1 μMIC(50) values and the hit rate in the second in silico selection and in vitro screening round exceeded the 20%.

Discovery of novel mGluR1 antagonists: A multistep virtual screening approach based on an SVM model and a pharmacophore hypothesis significantly increases the hit rate and enrichment factor

Development of glutamate non-competitive antagonists of mGluR1 (Metabotropic glutamate receptor subtype 1) has increasingly attracted much attention in recent years due to their potential therapeutic application for various nervous disorders. Since there is no crystal structure reported for mGluR1, ligand-based virtual screening (VS) methods, typically pharmacophore-based VS (PB-VS), are often used for the discovery of mGluR1 antagonists. Nevertheless, PB-VS usually suffers a lower hit rate and enrichment factor. In this investigation, we established a multistep ligand-based VS approach that is based on a support vector machine (SVM) classification model and a pharmacophore model. Performance evaluation of these methods in virtual screening against a large independent test set, M-MDDR, show that the multistep VS approach significantly increases the hit rate and enrichment factor compared with the individual SB-VS and PB-VS methods. The multistep VS approach was then used to screen several large chemical libraries including PubChem, Specs, and Enamine. Finally a total of 20 compounds were selected from the top ranking compounds, and shifted to the subsequent in vitro and in vivo studies, which results will be reported in the near future.

Multistep Virtual Screening for Rapid and Efficient Identification of Non‐Nucleoside Bacterial Thymidine Kinase Inhibitors

Antimicrobial activity of trimethoprim/sulfamethoxazole (SXT) against Staphylococcus aureus (S. aureus) is antagonized by thymidine, which is abundant in infected or inflamed human tissue. To restore the antimicrobial activity of SXT in the presence of thymidine, we screened for small-molecule inhibitors of S. aureus thymidine kinase with non-nucleoside scaffolds. We present the successful application of an adaptive virtual screening protocol for novel antibiotics using a combination of ligand- and structure-based approaches. Two consecutive rounds of virtual screening and in vitro testing were performed that resulted in several non-nucleoside hits. The most potent compound exhibits substantial antimicrobial activity against both methicillin-resistant S. aureus strain ATCC 700699 and nonresistant strain ATCC 29213, when combined with SXT in the presence of thymidine. This study demonstrates how virtual screening can be used to guide hit finding in antibacterial screening campaigns with minimal experimental effort.

MS-DOCK: Accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening

BackgroundThe number of protein targets with a known or predicted tri-dimensional structure and of drug-like chemical compounds is growing rapidly and so is the need for new therapeutic compounds or chemical probes. Performing flexible structure-based virtual screening computations on thousands of targets with millions of molecules is intractable to most laboratories nor indeed desirable. Since shape complementarity is of primary importance for most protein-ligand interactions, we have developed a tool/protocol based on rigid-body docking to select compounds that fit well into binding sites.ResultsHere we present an efficient multiple conformation rigid-body docking approach, MS-DOCK, which is based on the program DOCK. This approach can be used as the first step of a multi-stage docking/scoring protocol. First, we developed and validated the Multiconf-DOCK tool that generates several conformers per input ligand. Then, each generated conformer (bioactives and 37970 decoys) was docked rigidly using DOCK6 with our optimized protocol into seven different receptor-binding sites. MS-DOCK was able to significantly reduce the size of the initial input library for all seven targets, thereby facilitating subsequent more CPU demanding flexible docking procedures.ConclusionMS-DOCK can be easily used for the generation of multi-conformer libraries and for shape-based filtering within a multi-step structure-based screening protocol in order to shorten computation times.

Fast Structure-Based Virtual Ligand Screening Combining FRED, DOCK, and Surflex

A protocol was devised in which FRED, DOCK, and Surflex were combined in a multistep virtual ligand screening (VLS) procedure to screen the pocket of four different proteins. One goal was to evaluate the impact of chaining "freely available packages to academic users" on docking/scoring accuracy and CPU time consumption. A bank of 65 660 compounds including 49 known actives was generated. Our procedure is successful because docking/scoring parameters are tuned according to the nature of the binding pocket and because a shape-based filtering tool is applied prior to flexible docking. The obtained enrichment factors are in line with those reported in recent studies. We suggest that consensus docking/scoring could be valuable to some drug discovery projects. The present protocol could process the entire bank for one receptor in less than a week on one processor, suggesting that VLS experiments could be performed even without large computer resources.

Molecular dynamics and free energy analyses of cathepsin D-inhibitor interactions: insight into structure-based ligand design.

In this study, we compare the calculated and experimental binding free energies for a combinatorial library of inhibitors of cathepsin D (CatD), an aspartyl protease. Using a molecular dynamics (MD)-based, continuum solvent method (MM-PBSA), we are able to reproduce the experimental binding affinity for a set of seven inhibitors with an average error of ca. 1 kcal/mol and a correlation coefficient of 0.98. By comparing the dynamical conformations of the inhibitors complexed with CatD with the initial conformations generated by CombiBuild (University of California, San Francisco, CA, 1995), we have found that the docking conformation observed in an X-ray structure of one peptide inhibitor bound to CatD (Proc. Natl. Acad. Sci. U.S.A. 1993, 90, 6796-6800) is in good agreement with our MD simulation. However, the DOCK scoring function, based on intermolecular van der Waals and electrostatics, using a distance-dependent dielectric constant (J. Comput. Chem. 1992, 13, 505-524), poorly reproduces the trend of experimental binding affinity for these inhibitors. Finally, the use of PROFEC (J. Comput.-Aided Mol. Des. 1998, 12, 215-227) analysis allowed us to identify two possible substitutions to improve the binding of one of the better inhibitors to CatD. This study offers hope that current methods of estimating the free energy of binding are accurate enough to be used in a multistep virtual screening protocol.