Application Of Virtual Screening Research Articles

Discovery of Novel DUSP4 Inhibitors through the Virtual Screening with Docking Simulations

E-mail: ksj@kribb.re.krReceived April 16, 2014, Accepted May 7, 2014Dual specificity protein phosphatase 4 (DUSP4) has been considered a promising target for the developmentof therapeutics for various human cancers. Here, we report the first example for a successful application of thestructure-based virtual screening to identify the novel small-molecule DUSP4 inhibitors. As a consequence ofthe virtual screening with the modified scoring function to include an effective molecular solvation free energyterm, five micromolar DUSP4 inhibitors are found with the associated IC

Structure-based virtual screening for drug discovery: principles, applications and recent advances.

Structure-based drug discovery (SBDD) is becoming an essential tool in assisting fast and cost-efficient lead discovery and optimization. The application of rational, structure-based drug design is proven to be more efficient than the traditional way of drug discovery since it aims to understand the molecular basis of a disease and utilizes the knowledge of the three-dimensional structure of the biological target in the process. In this review, we focus on the principles and applications of Virtual Screening (VS) within the context of SBDD and examine different procedures ranging from the initial stages of the process that include receptor and library pre-processing, to docking, scoring and post-processing of topscoring hits. Recent improvements in structure-based virtual screening (SBVS) efficiency through ensemble docking, induced fit and consensus docking are also discussed. The review highlights advances in the field within the framework of several success studies that have led to nM inhibition directly from VS and provides recent trends in library design as well as discusses limitations of the method. Applications of SBVS in the design of substrates for engineered proteins that enable the discovery of new metabolic and signal transduction pathways and the design of inhibitors of multifunctional proteins are also reviewed. Finally, we contribute two promising VS protocols recently developed by us that aim to increase inhibitor selectivity. In the first protocol, we describe the discovery of micromolar inhibitors through SBVS designed to inhibit the mutant H1047R PI3Kα kinase. Second, we discuss a strategy for the identification of selective binders for the RXRα nuclear receptor. In this protocol, a set of target structures is constructed for ensemble docking based on binding site shape characterization and clustering, aiming to enhance the hit rate of selective inhibitors for the desired protein target through the SBVS process.

Hierarchical virtual screening approaches in small molecule drug discovery

Virtual screening has played a significant role in the discovery of small molecule inhibitors of therapeutic targets in last two decades. Various ligand and structure-based virtual screening approaches are employed to identify small molecule ligands for proteins of interest. These approaches are often combined in either hierarchical or parallel manner to take advantage of the strength and avoid the limitations associated with individual methods. Hierarchical combination of ligand and structure-based virtual screening approaches has received noteworthy success in numerous drug discovery campaigns. In hierarchical virtual screening, several filters using ligand and structure-based approaches are sequentially applied to reduce a large screening library to a number small enough for experimental testing. In this review, we focus on different hierarchical virtual screening strategies and their application in the discovery of small molecule modulators of important drug targets. Several virtual screening studies are discussed to demonstrate the successful application of hierarchical virtual screening in small molecule drug discovery.

Mechanistic insight into the radical scavenging activity of polyphenols and its application in virtual screening of phytochemical library: an in silico approach

Polyphenols are amply present in fruits, vegetables, beverages and thus very common items in our diet. They are better characterized as antioxidants and are able to scavenge various radicals and singlet oxygen. The present study aims to provide structural and electronic insight into the antioxidant activity of polyphenols and identify novel natural antioxidant. With systematic searches of the large descriptor spaces available in CODESSA, we found that number of benzene ring (constitutional descriptors), electrostatic descriptors related to the hydrogen bonding ability and the lowest unoccupied molecular orbital (LUMO) energy of the polyphenols are very important descriptors related to the electron donation ability of polyphenols, evident from t test values and principal component analysis. Decreasing LUMO energy facilitates electron transfer and stability of the resulting polyphenolic radical increases with the number of attached aromatic ring due to enhanced resonance. Hydrogen bonding present in the parent polyphenol also facilitates electron donation and stabilizes the phenolic radical. Using multiple linear regressions, we have derived two quantitative structure activity relation (QSAR) models with four and five descriptors, respectively, and validated using cross-validation techniques. The calculated square correlation coefficient for four and five descriptor models is R2 = 0.89 and R2 = 0.9, respectively, with cross-validated squared correlation coefficient \({R}_{\text{CV}}^{2}\) = 0.83 and \({R}_{\text{CV}}^{2}\) = 0.87, respectively. Virtual screening using the derived QSAR models on in-house developed phytochemical database identified several highly potent natural antioxidants exhibiting their activity by electron donation coupled proton transfer pathway.

An integrated approach to knowledge-driven structure-based virtual screening.

In many practical applications of structure-based virtual screening (VS) ligands are already known. This circumstance requires that the obtained hits need to satisfy initial made expectations i.e., they have to fulfill a predefined binding pattern and/or lie within a predefined physico-chemical property range. Based on the RApid Index-based Screening Engine (RAISE) approach, we introduce CRAISE-a user-controllable structure-based VS method. It efficiently realizes pharmacophore-guided protein-ligand docking to assess the library content but thereby concentrates only on molecules that have a chance to fulfill the given binding pattern. In order to focus only on hits satisfying given molecular properties, library profiles can be utilized to simultaneously filter compounds. CRAISE was evaluated on a range of strict to rather relaxed hypotheses with respect to its capability to guide binding-mode predictions and VS runs. The results reveal insights into a guided VS process. If a pharmacophore model is chosen appropriately, a binding mode below 2 Å is successfully reproduced for 85% of well-prepared structures, enrichment is increased up to median AUC of 73%, and the selectivity of the screening process is significantly enhanced leading up to seven times accelerated runtimes. In general, CRAISE supports a versatile structure-based VS approach allowing to assess hypotheses about putative ligands on a large scale.

Insight into the anti-amyloidogenic activity of polyphenols and its application in virtual screening of phytochemical database

Alzheimer’s Disease (AD) is the most frequent form of neurodegenerative disorder pathologically characterized by the presence of aggregates of Aβ in the region of the brain involved in memory and cognition. Polyphenols commonly found in several daily consumed food items and beverages have been demonstrated to inhibit Aβ aggregation using various biophysical or cell culture-based techniques. Here, insight into the anti-amyloidogenic activity of polyphenols has been explored using 2-D QSAR methodology implemented in CODESSA packages. Interestingly, hydrophobic interactions emerge as a significant descriptor for the recognition of amyloid fibril by polyphenols. Thus, it may be interpreted that highly potent polyphenols capably bind within the hydrophobic core region of the amyloid fibril and, thereby, destabilize the fibril by interrupting the hydrophobic interactions present within the fibril. Highly branched substituents and substituents with high degree of conformational flexibility on the polyphenolic structural scaffold enhance the fibril-destabilizing activity of compounds. In addition, specific electrostatic interaction between amyloid fibril and polyphenol plays a crucial role for the recognition of the fibril by polyphenols. The “application domain” of the derived QSAR model also has been defined, and the derived model has been used to screen a small in-house developed phytochemicals database comprising 200 compounds. 59 phytochemicals have been predicted as highly active anti-amyloidogenic phytochemicals. The modeling strategy is an efficient way to reduce the chemical search space and can selectively mine novel lead anti-amyloidogenic molecule from large chemical databases.

Hsp90 inhibitors, part 2: combining ligand-based and structure-based approaches for virtual screening application.

Hsp90 continues to be an important target for pharmaceutical discovery. In this project, virtual screening (VS) for novel Hsp90 inhibitors was performed using a combination of Autodock and Surflex-Sim (LB) scoring functions with the predictive ability of 3-D QSAR models, previously generated with the 3-D QSAutogrid/R procedure. Extensive validation of both structure-based (SB) and ligand-based (LB), through realignments and cross-alignments, allowed the definition of LB and SB alignment rules. The mixed LB/SB protocol was applied to virtually screen potential Hsp90 inhibitors from the NCI Diversity Set composed of 1785 compounds. A selected ensemble of 80 compounds were biologically tested. Among these molecules, preliminary data yielded four derivatives exhibiting IC50 values ranging between 18 and 63 μM as hits for a subsequent medicinal chemistry optimization procedure.

Targeting the hydrophobic pocket of autotaxin with virtual screening of inhibitors identifies a common aromatic sulfonamide structural motif

Modulation of autotaxin (ATX), the lysophospholipase D enzyme that produces lysophosphatidic acid, with small-molecule inhibitors is a promising strategy for blocking the ATX-lysophosphatidic acid signaling axis. Although discovery campaigns have been successful in identifying ATX inhibitors, many of the reported inhibitors target the catalytic cleft of ATX. A recent study provided evidence for an additional inhibitory surface in the hydrophobic binding pocket of ATX, confirming prior studies that relied on enzyme kinetics and differential inhibition of substrates varying in size. Multiple hits from previous high-throughput screening for ATX inhibitors were obtained with aromatic sulfonamide derivatives interacting with the hydrophobic pocket. Here, we describe the development of a ligand-based strategy and its application in virtual screening, which yielded novel high-potency inhibitors that target the hydrophobic pocket of ATX. Characterization of the structure-activity relationship of these new inhibitors forms the foundation of a new pharmacophore model of the hydrophobic pocket of ATX.

Virtual screening and biochemical evaluation to identify new inhibitors of mammalian target of rapamycin (mTOR)

Mammalian target of rapamycin (mTOR) is a promising target for the development of anticancer medicines. Here, we report the first example for a successful application of the structure-based virtual screening to identify new mTOR inhibitors. Using the scoring function improved by implementing the ligand solvation effects on protein–ligand association, six novel mTOR inhibitors are found with IC50 values ranging from 8 to 60μM. Because these new inhibitors are also computationally screened for having desirable physicochemical properties as a drug candidate, they deserve consideration for further development by structure–activity relationship studies to optimize the inhibitory and anticancer activities. Structural features relevant to the stabilization of the inhibitors in the ATP-binding site of mTOR are addressed in detail.

Identification of Potent Leukocyte Common Antigen-Related Phosphatase Inhibitors via Structure-Based Virtual Screening

Leukocyte common antigen-related phosphatase (LAR) has been considered a promising target for the development of therapeutics for neurological diseases. Here, we report the first example for a successful application of the structure-based virtual screening to identify the novel small-molecule LAR inhibitors. Five of these inhibitors revealed micromolar inhibitory activities with the associated IC50 values ranging from 2 to 6 µM. Because the newly identified inhibitors were also screened for having desirable physicochemical properties as a drug candidate, they may serve as a starting point of the structure-activity relationship study to optimize the medical efficacy. Structural features relevant to the stabilization of the new inhibitors in the active site of LAR are discussed in detail.

Protein pocket and ligand shape comparison and its application in virtual screening

Understanding molecular recognition is one major requirement for drug discovery and design. Physicochemical and shape complementarity between two binding partners is the driving force during complex formation. In this study, the impact of shape within this process is analyzed. Protein binding pockets and co-crystallized ligands are represented by normalized principal moments of inertia ratios (NPRs). The corresponding descriptor space is triangular, with its corners occupied by spherical, discoid, and elongated shapes. An analysis of a selected set of sc-PDB complexes suggests that pockets and bound ligands avoid spherical shapes, which are, however, prevalent in small unoccupied pockets. Furthermore, a direct shape comparison confirms previous studies that on average only one third of a pocket is filled by its bound ligand, supplemented by a 50 % subpocket coverage. In this study, we found that shape complementary is expressed by low pairwise shape distances in NPR space, short distances between the centers-of-mass, and small deviations in the angle between the first principal ellipsoid axes. Furthermore, it is assessed how different binding pocket parameters are related to bioactivity and binding efficiency of the co-crystallized ligand. In addition, the performance of different shape and size parameters of pockets and ligands is evaluated in a virtual screening scenario performed on four representative targets.

The use of virtual screening and differential scanning fluorimetry for the rapid identification of fragments active against MEK1

We report the analysis of an in-house fragment screening campaign for the oncology target MEK1. The application of virtual screening (VS) as a primary fragment screening approach, followed by biophysical validation using differential screening fluorimetry (DSF), with resultant binding mode determination by X-ray crystallography (X-ray), is presented as the most time and cost-effective combination of in silico and in vitro methods to identify fragments. We demonstrate the effectiveness of the VS–DSF workflow for the early identification of fragments to both ‘jump-start’ the drug discovery project and to complement biochemical screening data.

The influence of the inactives subset generation on the performance of machine learning methods.

BackgroundA growing popularity of machine learning methods application in virtual screening, in both classification and regression tasks, can be observed in the past few years. However, their effectiveness is strongly dependent on many different factors.ResultsIn this study, the influence of the way of forming the set of inactives on the classification process was examined: random and diverse selection from the ZINC database, MDDR database and libraries generated according to the DUD methodology. All learning methods were tested in two modes: using one test set, the same for each method of inactive molecules generation and using test sets with inactives prepared in an analogous way as for training. The experiments were carried out for 5 different protein targets, 3 fingerprints for molecules representation and 7 classification algorithms with varying parameters. It appeared that the process of inactive set formation had a substantial impact on the machine learning methods performance.ConclusionsThe level of chemical space limitation determined the ability of tested classifiers to select potentially active molecules in virtual screening tasks, as for example DUDs (widely applied in docking experiments) did not provide proper selection of active molecules from databases with diverse structures. The study clearly showed that inactive compounds forming training set should be representative to the highest possible extent for libraries that undergo screening.

SAR and Computer-Aided Drug Design Approaches in the Discovery of Peroxisome Proliferator-Activated Receptor γ Activators: A Perspective

Activators of PPARγ, Troglitazone (TGZ), Rosiglitazone (RGZ), and Pioglitazone (PGZ) were introduced for treatment of Type 2 diabetes, but TGZ and RGZ have been withdrawn from the market along with other promising leads due cardiovascular side effects and hepatotoxicity. However, the continuously improving understanding of the structure/function of PPARγ and its interactions with potential ligands maintain the importance of PPARγ as an antidiabetic target. Extensive structure activity relationship (SAR) studies have thus been performed on a variety of structural scaffolds by various research groups. Computer-aided drug discovery (CADD) approaches have also played a vital role in the search and optimization of potential lead compounds. This paper focuses on these approaches adopted for the discovery of PPARγ ligands for the treatment of Type 2 diabetes. Key concepts employed during the discovery phase, classification based on agonistic character, applications of various QSAR, pharmacophore mapping, virtual screening, molecular docking, and molecular dynamics studies are highlighted. Molecular level analysis of the dynamic nature of ligand-receptor interaction is presented for the future design of ligands with better potency and safety profiles. Recently identified mechanism of inhibition of phosphorylation of PPARγ at SER273 by ligands is reviewed as a new strategy to identify novel drug candidates.

Design, Synthesis and Evaluation of Quinoline-based Small Molecule Inhibitor of STAT3

As STAT3 has been validated as an anticancer target, its inhibitors have been shown to possess therapeutic promise for the treatment of human cancers. To identify novel and selective STAT3 inhibitors, a virtual screening based on the STAT3 SH2 domain was performed and a small molecule, 2-phenylquinoline-4-carboxylic acid (5a), with an inhibition constant Ki value of 17.53 μM to STAT3 was discovered. On this basis, the derivatives of 5a including esters, amides and dimers were synthesized. The bioactivity and inhibitory selectivity of the derivatives were assayed using human breast cancer cell lines, MDA-MB-468 and MCF-7. Among the derivatives, 5c and 9b showed the most potent inhibitory activity with a good selectivity, and also inhibited STAT3 protein level of MDA-MB-468 cells. The results demonstrated a successful application of virtual screening for lead discovery. Compound 9b might be an effective STAT3 inhibitor lead for the further development of antitumor agents. Keywords: 2-Phenyl quinoline-4-carboxylic acid, Breast cancer cell lines, Quinoline, SH2 domian, Small molecule inhibitor, STAT3

TorsionAnalyzer: exploring conformational space

Molecular conformations are used in a wide range of virtual screening applications to represent the conformational flexibility of a molecule. As the underlying conformational model has a major influence on the results of these applications, a closer insight into the conformational space of molecules is very important. We developed a new interactive graphical software tool for conformation analysis called TorsionAnalyzer. The tool uses a predefined set of over 450 SMARTS patterns to analyze the torsion angles of an input conformation. Each pattern describes a torsion angle and is associated with frequency histograms derived from CSD [1] and PDB [2] data. For each pattern a list of allowed torsion angles was automatically derived from the torsion histograms. Rotatable bonds of a conformation are colored according to their classification into usual, borderline and unusual torsion angles, using the list of allowed angles from the corresponding patterns. This allows the user to see at a glance if one of the torsion angles of an examined conformation is out of the ordinary. The TorsionAnalyzer supports easy modification of the existing SMARTS patterns as well as the preparation and storage of different sets of patterns for different sets of molecules.

Identification of Potent VHZ Phosphatase Inhibitors with Structure-Based Virtual Screening

VH1-like phosphatase Z (VHZ) has proved to be a promising target for the development of therapeutics for the treatment of human cancers. Here, we report the first example for a successful application of structure-based virtual screening to identify the novel small-molecule inhibitors of VHZ. These inhibitors revealed high potencies with the associated IC50 values ranging from 3 to 20 µM and were also screened for having desirable physicochemical properties as a drug candidate. Therefore, they deserve consideration for further development by structure-activity relationship studies to optimize inhibitory and anticancer activities. Structural features relevant to the stabilization of the newly identified inhibitors in the active site of VHZ are discussed in detail.

Kinome-wide Activity Modeling from Diverse Public High-Quality Data Sets

Large corpora of kinase small molecule inhibitor data are accessible to public sector research from thousands of journal article and patent publications. These data have been generated employing a wide variety of assay methodologies and experimental procedures by numerous laboratories. Here we ask the question how applicable these heterogeneous data sets are to predict kinase activities and which characteristics of the data sets contribute to their utility. We accessed almost 500,000 molecules from the Kinase Knowledge Base (KKB) and after rigorous aggregation and standardization generated over 180 distinct data sets covering all major groups of the human kinome. To assess the value of the data sets, we generated hundreds of classification and regression models. Their rigorous cross-validation and characterization demonstrated highly predictive classification and quantitative models for the majority of kinase targets if a minimum required number of active compounds or structure-activity data points were available. We then applied the best classifiers to compounds most recently profiled in the NIH Library of Integrated Network-based Cellular Signatures (LINCS) program and found good agreement of profiling results with predicted activities. Our results indicate that, although heterogeneous in nature, the publically accessible data sets are exceedingly valuable and well suited to develop highly accurate predictors for practical Kinome-wide virtual screening applications and to complement experimental kinase profiling.

Inhibitors of the Yersinia protein tyrosine phosphatase through high throughput and virtual screening approaches

The bacterial protein tyrosine phosphatase YopH is an essential virulence determinant in Yersinia pestis and a potential antibacterial drug target. Here we report our studies of screening for small molecule inhibitors of YopH using both high throughput and in silico approaches. The identified inhibitors represent a diversity of chemotypes and novel pTyr mimetics, providing a starting point for further development and fragment-based design of multi-site binding inhibitors. We demonstrate that the applications of high throughput and virtual screening, when guided by structural binding mode analysis, is an effective approach for identifying potent and selective inhibitors of YopH and other protein phosphatases for rational drug design.

Virtual Screening and Biochemical Evaluation of Mitogen-activated Protein Kinase Phosphatase 4 Inhibitors

Mitogen-activated protein kinase phosphatase 4 (MKP4) has proved to be a promising target for the development of therapeutics for the treatment of diabetes and the other metabolic diseases. Here, we report an example for a successful application of the structure-based virtual screening to identify three novel inhibitors of MKP4. These inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with <TEX>$IC_{50}$</TEX> values ranging from 4.9 to <TEX>$32.3{\mu}M$</TEX>. Therefore, they deserve consideration for further development by structure-activity relationship studies to optimize the inhibitory and antidiabetic activities. Structural features relevant to the stabilization of the newly identified inhibitors in the active site of MKP4 are discussed in detail.