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.     

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.     

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.     

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.     

Structure-based pharmacophore design and virtual screening for novel angiotensin converting enzyme 2 inhibitors

The metallopeptidase Angiotensin Converting Enzyme (ACE) is an important drug target for the treatment of hypertension, heart, kidney, and lung disease. Recently, a close and unique human ACE homologue termed ACE2 has been identified and found to be an interesting new cardiorenal disease target. With the recently resolved inhibitor-bound ACE2 crystal structure available, we have attempted a structure-based approach to identify novel potent and selective inhibitors. Computational approaches focus on pharmacophore-based virtual screening of large compound databases. Selectivity was ensured by initial screening for ACE inhibitors within an internal database and the Derwent World Drug Index, which could be reduced to zero false positives and 0.1% hit rate, respectively. An average hit reduction of 0.44% was achieved with a five feature hypothesis, searching approximately 3.8 million compounds from various commercial databases. Seventeen compounds were selected based on high fit values as well as diverse structure and subjected to experimental validation in a bioassay. We show that all compounds displayed an inhibitory effect on ACE2 activity, the six most promising candidates exhibiting IC50 values in the range of 62-179 microM.     

Structure-based virtual screening for novel potential selective inhibitors of class IIa histone deacetylases for cancer treatment

The fundamental cause of human cancer is strongly influenced by down- or up-regulations of epigenetic factors. Upregulated histone deacetylases (HDAC) have been shown to be effectively neutralized by the action of HDACs inhibitors (HDACi). However, cytotoxicity has been reported in normal cells because of non-specificity of several available HDACis that are in clinical use or at different phases of clinical trials. Because of the high amino acid sequence and structural similarity among HDAC enzymes, it is believed to be a challenging task to obtain isoform-selectivity. The essential aim of the present research work was to identify isoform-selective inhibitors against class IIa HDACs via structure-based drug design. Based on the highest binding affinity and isoform-selectivity, the top-ranked inhibitors were in silico tested for their absorption, distribution, metabolism, elimination, and toxicity (ADMET) properties, which were classified as drug-like compounds. Later, molecular dynamics simulation (MD) was carried out for all compound-protein complexes to evaluate the structural stability and the biding mode of the inhibitors, which showed high stability throughout the 100 ns simulation. Free binding energy predictions by MM-PBSA method showed the high binding affinity of the identified compounds toward their respective targets. Hence, these inhibitors could be used as drug candidates or as lead compounds for more in silico or in vitro optimization to design safe isoform-selective HDACs inhibitors.     

Discovery of novel wee1 inhibitors via structure-based virtual screening and biological evaluation

Wee1 plays a critical role in the arrest of G2/M cell cycle for DNA repair before entering mitosis. Many cancer cells have been identified as overexpression of Wee1. In this research, pharmacophore modeling, molecular docking and molecular dynamics simulation approaches were constructed to identify novel potential Wee1 inhibitors. A compound 8 was found to have a novel skeleton against Wee1 with an IC₅₀ value of 22.32 µM and a K_i value of 13.11 µM. Kinetic assays were employed to evaluate the compound 8 as a competitive inhibitor. Compound 8 was tested against A-549 tumor cell lines with IC₅₀ value of 17.8 µM. To investigate the intermolecular interaction of Wee1 and compound 8, further molecular dynamics simulations were performed. It indicates that the binding mode of compound 8 and reference ligand is similar. The active core scaffold of compound 8 could represent a promising lead compound for studying Wee1 and be used for further structural optimization to design more potent Wee1 inhibitors.     

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18?±?0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27?±?0.02 μM. Then, the structure–activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.

     

Pharmacophore identification, in silico screening, and virtual library design for inhibitors of the human factor Xa

Factor Xa inhibitors are innovative anticoagulant agents that provide a better safety/efficacy profile compared to other anticoagulative drugs. A chemical feature-based modeling approach was applied to identify crucial pharmacophore patterns from 3D crystal structures of inhibitors bound to human factor Xa (Pdb entries 1fjs, 1kns, 1eqz) using the software LIGANDSCOUT and CATALYST. The complex structures were selected regarding the criteria of high inhibitory potency (i.e. all ligands show K(i) values against factor Xa in the subnanomolar range) and good resolution (i.e. at least 2.2 A) in order to generate selective and high quality pharmacophore models. The resulting chemical-feature based hypotheses were used for virtual screening of commercial molecular databases such as the WDI database. Furthermore, a ligand-based molecular modeling approach was performed to obtain common-feature hypotheses that represent the relevant chemical interactions between 10 bioactive factor Xa inhibitors and the protein, respectively. In a next step a virtual combinatorial library was designed in order to generate new compounds with similar chemical and spatial properties as known inhibitors. The software tool ILIB DIVERSE was used for this procedure in order to provide new scaffolds of this group of anticoagulants. Finally we present the combination of these two techniques, hence virtual screening was performed with selective pharmacophore models in a focused virtual combinatorial database. De novo derived molecular scaffolds that were able to adequately satisfy the pharmacophore criteria are revealed and are promising templates for candidates for further development.     

Identification of novel inhibitors of mitogen-activated protein kinase phosphatase-1 with structure-based virtual screening

Mitogen-activated protein kinase phosphatase-1 (MKP-1) has proved to be an attractive target for the development of therapeutics for the treatment of cancer. We report the first example for a successful application of the structure-based virtual screening to identify the novel inhibitors of MKP-1. It is shown that the efficiency of virtual screening can be enhanced significantly by the incorporation of a new solvation energy term in the scoring function. The newly found inhibitors have desirable physicochemical properties as a drug candidate and reveal a moderate potency with IC₅₀ values ranging from 20 to 50 μM. Therefore, they deserve a consideration for further development by structure–activity relationship studies to optimize the inhibitory activities. Structural features relevant to the stabilization of the inhibitors in the active site of MKP-1 are discussed in detail.     

Molecular modeling on pyruvate phosphate dikinase of Entamoeba histolytica and in silico virtual screening for novel inhibitors

Pyruvate phosphate dikinase (PPDK) is the key enzyme essential for the glycolytic pathway in most common and perilous parasite Entamoeba histolytica. Inhibiting the function of this enzyme could control the wide spread of intestinal infections caused by Entamoeba histolytica in humans. With this objective, we modeled the three dimensional structure of the PPDK protein. We used templates with 51% identity and 67% similarity to employ homology-modeling approach. Stereo chemical quality of protein structure was validated by protein structure validation program PROCHECK and VERIFY3D. Experimental proof available in literature along with the in silico studies indicated Lys21, Arg91, Asp323, Glu325 and Gln337 to be the probable active sites in the target protein. Virtual screening was carried out using the genetic docking algorithm GOLD and a consensus scoring function X-Score to substantiate the prediction. The small molecule libraries (ChemDivision database, Diversity dataset, Kinase inhibitor database) were used for screening process. Along with the high scoring results, the interaction studies provided promising ligands for future experimental screening to inhibit the function of PPDK in Entamoeba histolytica. Further, the phylogeny study was carried out to assess the possibility of using the proposed ligands as inhibitors in related pathogens.     

Supervised scoring models with docked ligand conformations for structure-based virtual screening

Protein-ligand docking programs have been used to efficiently discover novel ligands for target proteins from large-scale compound databases. However, better scoring methods are needed. Generally, scoring functions are optimized by means of various techniques that affect their fitness for reproducing X-ray structures and protein-ligand binding affinities. However, these scoring functions do not always work well for all target proteins. A scoring function should be optimized for a target protein to enhance enrichment for structure-based virtual screening. To address this problem, we propose the supervised scoring model (SSM), which takes into account the protein-ligand binding process using docked ligand conformations with supervised learning for optimizing scoring functions against a target protein. SSM employs a rough linear correlation between binding free energy and the root mean square deviation of a native ligand for predicting binding energy. We applied SSM to the FlexX scoring function, that is, F-Score, with five different target proteins: thymidine kinase (TK), estrogen receptor (ER), acetylcholine esterase (AChE), phosphodiesterase 5 (PDE5), and peroxisome proliferator-activated receptor gamma (PPARgamma). For these five proteins, SSM always enhanced enrichment better than F-Score, exhibiting superior performance that was particularly remarkable for TK, AChE, and PPARgamma. We also demonstrated that SSM is especially good at enhancing enrichments of the top ranks of screened compounds, which is useful in practical drug screening.     

Multiple ligand docking by Glide: implications for virtual second-site screening

Performance of Glide was evaluated in a sequential multiple ligand docking paradigm predicting the binding modes of 129 protein-ligand complexes crystallized with clusters of 2-6 cooperative ligands. Three sampling protocols (single precision-SP, extra precision-XP, and SP without scaling ligand atom radii-SP hard) combined with three different scoring functions (GlideScore, Emodel and Glide Energy) were tested. The effects of ligand number, docking order and druglikeness of ligands and closeness of the binding site were investigated. On average 36?% of all structures were reproduced with RMSDs lower than 2??. Correctly docked structures reached 50?% when docking druglike ligands into closed binding sites by the SP hard protocol. Cooperative binding to metabolic and transport proteins can dramatically alter pharmacokinetic parameters of drugs. Analyzing the cytochrome P450 subset the SP hard protocol with Emodel ranking reproduced two-thirds of the structures well. Multiple ligand binding is also exploited by the fragment linking approach in lead discovery settings. The HSP90 subset from real life fragment optimization programs revealed that Glide is able to reproduce the positions of multiple bound fragments if conserved water molecules are considered. These case studies assess the utility of Glide in sequential multiple docking applications.     

Ensemble pharmacophore meets ensemble docking: a novel screening strategy for the identification of RIPK1 inhibitors

Programmed cell death has been a fascinating area of research since it throws new challenges and questions in spite of the tremendous ongoing research in this field. Recently, necroptosis, a programmed form of necrotic cell death, has been implicated in many diseases including neurological disorders. Receptor interacting serine/threonine protein kinase 1 (RIPK1) is an important regulatory protein involved in the necroptosis and inhibition of this protein is essential to stop necroptotic process and eventually cell death. Current structure-based virtual screening methods involve a wide range of strategies and recently, considering the multiple protein structures for pharmacophore extraction has been emphasized as a way to improve the outcome. However, using the pharmacophoric information completely during docking is very important. Further, in such methods, using the appropriate protein structures for docking is desirable. If not, potential compound hits, obtained through pharmacophore-based screening, may not have correct ranks and scores after docking. Therefore, a comprehensive integration of different ensemble methods is essential, which may provide better virtual screening results. In this study, dual ensemble screening, a novel computational strategy was used to identify diverse and potent inhibitors against RIPK1. All the pharmacophore features present in the binding site were captured using both the apo and holo protein structures and an ensemble pharmacophore was built by combining these features. This ensemble pharmacophore was employed in pharmacophore-based screening of ZINC database. The compound hits, thus obtained, were subjected to ensemble docking. The leads acquired through docking were further validated through feature evaluation and molecular dynamics simulation.     

A novel method for screening the glutathione transferase inhibitors

Background  

Glutathione transferases (GSTs) belong to the family of Phase II detoxification enzymes. GSTs catalyze the conjugation of glutathione to different endogenous and exogenous electrophilic compounds. Over-expression of GSTs was demonstrated in a number of different human cancer cells. It has been found that the resistance to many anticancer chemotherapeutics is directly correlated with the over-expression of GSTs. Therefore, it appears to be important to find new GST inhibitors to prevent the resistance of cells to anticancer drugs. In order to search for glutathione transferase (GST) inhibitors, a novel method was designed.     

Discovery of novel indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors by virtual screening

In this study, a combination of virtual screening methods were utilized to identify novel potential indoleamine 2,3-dioxygenase 1 (IDO1) inhibitors. A series of IDO1 potential inhibitors were identified by a combination of following steps: Lipinski's Rule of Five, Veber rules filter, molecular docking, HipHop pharmacophores, 3D-Quantitative structure activity relationship (3D-QSAR) studies and Pan-assay Interference Compounds (PAINS) filter. Three known categories of IDO1 inhibitors were used to constructed pharmacophores and 3D-QSAR models. Four point pharmacophores (RHDA) of IDO1 inhibitors were generated from the training set. The 3D-QSAR models were obtained using partial least squares (PLS) analyze based on the docking conformation alignment from the training set. The leave-one-out correlation (q²) and non-cross-validated correlation coefficient (r²_pred) of the best CoMFA model were 0.601 and 0.546, and the ones from the best CoMSIA model were 0.506 and 0.541, respectively. Six hits from Specs database were identified and analyzed to confirm their binding modes and key interactions to the amino acid residues in the protein. This work may provide novel backbones for new generation of inhibitors of IDO1.     

Discovery of novel checkpoint kinase 1 inhibitors by virtual screening based on multiple crystal structures

Incorporating receptor flexibility is considered crucial for improvement of docking-based virtual screening. With an abundance of crystallographic structures freely available, docking with multiple crystal structures is believed to be a practical approach to cope with protein flexibility. Here we describe a successful application of the docking of multiple structures to discover novel and potent Chk1 inhibitors. Forty-six Chk1 structures were first compared in single structure docking by predicting the binding mode and recovering known ligands. Combinations of different protein structures were then compared by recovery of known ligands and an optimal ensemble of Chk1 structures were selected. The chosen structures were used in the virtual screening of over 60?000 diverse compounds for Chk1 inhibitors. Six novel compounds ranked at the top of the hits list were tested experimentally, and two of these compounds inhibited Chk1 activity-the best with an IC(50) value of 9.6 μM. Further study indicated that achieving a better enrichment and identifying more diverse compounds was more likely using multiple structures than using only a single structure even when protein structures were randomly selected. Taking into account conformational energy difference did not help to improve enrichment in the top ranked list.     

Identification of novel inhibitors of tropomyosin-related kinase A through the structure-based virtual screening with homology-modeled protein structure

Tropomyosin-related kinase A (TrkA) is a promising target for the development of cancer and pain therapeutics. Here, we report the first successful example of the use of a structure-based virtual screening to identify novel TrkA inhibitors. The accuracy of the virtual screening was improved by introducing an accurate solvation free energy term into the original AutoDock scoring function. We applied a drug design protocol involving homology modeling, docking analysis of a large chemical library, and enzyme inhibition assays to identify six structurally diverse TrkA inhibitors with K(d) values ranging from 3 to 40 μM. The significant potencies and good physicochemical properties of these drug candidates strongly support their consideration in a development effort that would involve structure-activity relationship (SAR) studies to optimize the inhibitory activities. We also addressed the structural and energetic features associated with binding of the newly identified inhibitors in the ATP-binding site of TrkA. The results indicate that any structural modifications introduced for the purpose of enhancing the activity of TrkA inhibitors should maximize the attractive interactions within the ATP-binding site and simultaneously minimize the desolvation cost for complexation.     

Two-track virtual screening approach to identify both competitive and allosteric inhibitors of human small C-terminal domain phosphatase 1

Despite a wealth of persuasive evidence for the involvement of human small C-terminal domain phosphatase 1 (Scp1) in the impairment of neuronal differentiation and in Huntington’s disease, small-molecule inhibitors of Scp1 have been rarely reported so far. This study aims to the discovery of both competitive and allosteric Scp1 inhibitors through the two-track virtual screening procedure. By virtue of the improvement of the scoring function by implementing a new molecular solvation energy term and by reoptimizing the atomic charges for the active-site Mg²⁺ ion cluster, we have been able to identify three allosteric and five competitive Scp1 inhibitors with low-micromolar inhibitory activity. Consistent with the results of kinetic studies on the inhibitory mechanisms, the allosteric inhibitors appear to be accommodated in the peripheral binding pocket through the hydrophobic interactions with the nonpolar residues whereas the competitive ones bind tightly in the active site with a direct coordination to the central Mg²⁺ ion. Some structural modifications to improve the biochemical potency of the newly identified inhibitors are proposed based on the binding modes estimated with docking simulations.