Identification of critical chemical features for Aurora kinase-B inhibitors using Hip-Hop, virtual screening and molecular docking

This study was performed to find the selective chemical features for Aurora kinase-B inhibitors using the potent methods like Hip-Hop, virtual screening, homology modeling, molecular dynamics and docking. The best hypothesis, Hypo1 was validated toward a wide range of test set containing the selective inhibitors of Aurora kinase-B. Homology modeling and molecular dynamics studies were carried out to perform the molecular docking studies. The best hypothesis Hypo1 was used as a 3D query to screen the chemical databases. The screened molecules from the databases were sorted based on ADME and drug like properties. The selective hit compounds were docked and the hydrogen bond interactions with the critical amino acids present in Aurora kinase-B were compared with the chemical features present in the Hypo1. Finally, we suggest that the chemical features present in the Hypo1 are vital for a molecule to inhibit the Aurora kinase-B activity.     

Pharmacophore modeling,atom based 3D-QSAR,molecular docking and molecular dynamics studies on Escherichia coli ParE inhibitors

ATP dependent ParE enzyme is as an attractive target for the development of antibacterial agents. Atom based 3D-QSAR model AADHR.187 was developed based on the thirty eight Escherichia coli ParE inhibitors. The generated model showed statistically significant coefficient of determinations for the training (R² = 0.985) and test (R² = 0.86) sets. The cross-validated correlation coefficient (q2) was 0.976. The utility of the generated model was validated by the enrichment study. The model was also validated with structurally diverse external test set of ten compounds. Contour plot analysis of the generated model unveiled the chemical features necessary for the E. coli ParE enzyme inhibition. Extra-precision docking result revealed that hydrogen bonding and ionic interactions play a major role in ParE protein-ligand binding. Binding free energy was computed for the data set inhibitors to validate the binding affinity. A 30-ns molecular dynamics simulation showed high stability and effective binding of inhibitor 34 within the active site of ParE enzyme. Using the best fitted model AADHR.187, pharmacophore-based high-throughput virtual screening was performed to identify virtual hits. Based on the above studies three new molecules are proposed as E. coli ParE inhibitors with high binding affinity and favourable ADME properties.     

Pharmacophore generation,atom-based 3D-QSAR and molecular dynamics simulation analyses of pyridine-3-carboxamide-6-yl-urea analogues as potential gyrase B inhibitors

DNA gyrase subunit B (GyrB) is an attractive drug target for the development of antibacterial agents with therapeutic potential. In the present study, computational studies based on pharmacophore modelling, atom-based QSAR, molecular docking, free binding energy calculation and dynamics simulation were performed on a series of pyridine-3-carboxamide-6-yl-urea derivatives. A pharmacophore model using 49 molecules revealed structural and chemical features necessary for these molecules to inhibit GyrB. The best fitted model AADDR.13 was generated with a coefficient of determination (r²) of 0.918. This model was validated using test set molecules and had a good r² of 0.78. 3D contour maps generated by the 3D atom-based QSAR revealed the key structural features responsible for the GyrB inhibitory activity. Extra precision molecular docking showed hydrogen bond interactions with key amino acid residues of ATP-binding pocket, important for inhibitor binding. Further, binding free energy was calculated by the MM-GBSA rescoring approach to validate the binding affinity. A 10 ns MD simulation of inhibitor #47 showed the stability of the predicted binding conformations. We identified 10 virtual hits by in silico high-throughput screening. A few new molecules were also designed as potent GyrB inhibitors. The information obtained from these methodologies may be helpful to design novel inhibitors of GyrB.     

Pharmacophore modeling coupled with molecular dynamic simulation approach to identify new leads for meprin-β metalloprotease

Human meprin beta metalloprotease, a small subgroup of the astacin family, is a potent drug target for the treatment of several disorders such as fibrosis, neurodegenerative disease in particular Alzheimer and inflammatory bowel diseases. In this study, a ligand-based pharmacophore approach has been used for the selection of potentially active compounds to understand the inhibitory activities of meprin-β by using the sulfonamide scaffold based inhibitors. Using this dataset, a pharmacophore model (Hypo1) was selected on the basis of a highest correlation coefficient (0.959), lowest total cost (105.89) and lowest root mean square deviation (1.31 Å) values. All the pharmacophore hypotheses generated from the candidate inhibitors comprised four features: two hydrogen-bond acceptor, one hydrogen-bond donor and one zinc binder feature. The best validated pharmacophore model (Hypo1) was used for virtual screening of compounds from several databases. The selective hit compounds were filtered by drug likeness property, acceptable ADMET profile, molecular docking and DFT study. Molecular dynamic simulations with the final 10 hit compounds revealed that a large number of non-covalent interactions were formed with the active site and specificity sub-pockets of the meprin beta metalloprotease. This study assists in the development of the new lead molecules as well as gives a better understanding of their interaction with meprin-β.     

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.     

Stalis: A Computational Method for Template-Based Ab Initio Ligand Design

Proteins interact with small molecules through specific molecular recognition, which is central to essential biological functions in living systems. Therefore, understanding such interactions is crucial for basic sciences and drug discovery. Here, we present S tructure t emplate-based a b initio li gand design s olution (Stalis), a knowledge-based approach that uses structure templates from the Protein Data Bank libraries of whole ligands and their fragments and generates a set of molecules (virtual ligands) whose structures represent the pocket shape and chemical features of a given target binding site. Our benchmark performance evaluation shows that ligand structure-based virtual screening using virtual ligands from Stalis outperforms a receptor structure-based virtual screening using AutoDock Vina, demonstrating reliable overall screening performance applicable to computational high-throughput screening. However, virtual ligands from Stalis are worse in recognizing active compounds at the small fraction of a rank-ordered list of screened library compounds than crystal ligands, due to the low resolution of the virtual ligand structures. In conclusion, Stalis can facilitate drug discovery research by designing virtual ligands that can be used for fast ligand structure-based virtual screening. Moreover, Stalis provides actual three-dimensional ligand structures that likely bind to a target protein, enabling to gain structural insight into potential ligands. Stalis can be an efficient computational platform for high-throughput ligand design for fundamental biological study and drug discovery research at the proteomic level. © 2019 Wiley Periodicals, Inc.     

A promising in silico protocol to develop novel PPARγ antagonists as potential anticancer agents: Design,synthesis and experimental validation via PPARγ protein activity and competitive binding assay

Peroxisome proliferator-activated receptor gamma (PPARγ), a member of the nuclear receptor superfamily is an excellent example of targets that orchestrates cancer, inflammation, lipid and glucose metabolism. We report a protocol for the development of novel PPARγ antagonists by employing 3D QSAR based virtual screening for the identification of ligands with anticancer properties. The models are generated based on a large and diverse set of PPARγ antagonist ligands by the HYPOGEN algorithm using Discovery Studio 2019 drug design software. Among the 10 hypotheses generated, Hypotheses 2 showed the highest correlation coefficient values of 0.95 with less RMS deviation of 1.193. Validation of the developed pharmacophore model was performed by Fischer’s randomization and screening against test and decoy set. The GH score or goodness score was found to be 0.81 indicating moderate to a good model. The selected pharmacophore model Hypo 2 was used as a query model for further screening of 11,145 compounds from the PubChem, sc-PDB structure database, and designed novel ligands. Based on fit values and ADMET filter, the final 10 compounds with the predicated activity of ≤ 3 nM were subjected for docking analysis. Docking analysis revealed the unique binding mode with hydrophobic amino acid that can cause destabilization of the H12 which is an important molecular mechanism to prove its antagonist action. Based on high CDocker scores, Cpd31 was synthesized, purified, analyzed and screened for PPARγ competitive binding by TR-FRET assay. The biochemical protein binding results matched the predicted results. Further, Cpd31 was screened against cancer cells and validated the results.     

氧肟酸类组蛋白去乙酰化酶抑制剂的药效团模型研究

基于24个目前已知的氧肟酸类组蛋白去乙酰化酶抑制剂,我们运用Catalyst软件建立了一个三维药效团模型。其中,最好的药效团模型1,包含了四个化学特征（一个氢键供体,一个芳环和两个疏水基）,相关系数达到0.946,并由另外20个化合物进行了测试验证。我们第一次特征性描述了组蛋白去乙酰化酶的帽子（CAP）部分。我们的研究结果对于设计全新组蛋白去乙酰化酶抑制剂具有很好的指导作用。     

药物发现中常用化合物库特征描述与对比分析

随着计算技术的发展和分子模拟软件的日趋成熟, 虚拟筛选已经在药物发现过程中发挥着越来越重要的作用. 在虚拟筛选过程中, 所使用化合物库的质量对先导化合物发现的成功率起着至关重要的作用. 本文通过对已知药物库、天然产物库、中药原植物化学成分库、筛选常用商业化合物库以及研究者所在实验室建立的化合物库的分析比较, 从化合物库的分子多样性、化学空间和分子骨架等多个方面提取并对比每一种化合物库的特征, 发现了已知药物库与中药原植物化学成分库的特征相似性, 揭示了中药原植物化学成分库作为筛选库的类药性优势, 并且深化了对几种筛选用化合物库特征的认识和理解.     

Pharmacophore Mode lingand Virtual Screening to Design the Potential Influenza Virus Endonuclease Inhibitors

Influenza virus endonuclease is an attractive target for antiviral therapy in the treatment of influenza infection. The purpos e of this study is to design a novel antiviral agent with improved biological activities against the influenza virus endonuclease. In this study, chemical feature‐based 3D pharmacophore models were developed from 41 known influenza virus endonuclease inhibitors. The best quantitative pharmacohore model (Hypo 1), which consists of two hydrogen‐bond acceptors and two hydrophobic features, yields the highest correlation coefficient (R = 0.886). Hypo 1 was further validated by the cross validation method and the test set compounds. The application of this model for predicting the activities of 11 known influenza virus endonuclease inhibitors in the test set shows great success. The correlation coefficient of 0.942 and a cross validation of 95;% confidence level prove that this model is reliable in identifying structurally diverse compounds for influenza virus endonuclease inhibition. The most active compound (compound 1) from the training set was docked into the active site of the influenza virus endonuclease as an additional verification that the pharmacophore model is accurate. The docked conformation showed important hydrogen bond interactions between the compound and two amino acids, Lys 134 and Lys 137. After validation, this model was used to screen the NCI chemical database to identify new influenza virus endonuclease inhibitors. Our study shows that the to pranking compound out of the 10 newly identified compounds using fit value ranking has an estimated activity of 0.049 μM. These newly identified lead compounds can be further experimentally validated using in vitro techniques.     

Identification of Influenza PAN Endonuclease Inhibitors via 3D-QSAR Modeling and Docking-Based Virtual Screening

Structural and biochemical studies elucidate that PA_N may contribute to the host protein shutdown observed during influenza A infection. Thus, inhibition of the endonuclease activity of viral RdRP is an attractive approach for novel antiviral therapy. In order to envisage structurally diverse novel compounds with better efficacy as PA_N endonuclease inhibitors, a ligand-based-pharmacophore model was developed using 3D-QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery Studio. As the training set, 25 compounds were taken to generate a significant pharmacophore model. The selected pharmacophore Hypo1 was further validated by 12 compounds in the test set and was used as a query model for further screening of 1916 compounds containing 71 HIV-1 integrase inhibitors, 37 antibacterial inhibitors, 131 antiviral inhibitors and other 1677 approved drugs by the FDA. Then, six compounds (Hit01–Hit06) with estimated activity values less than 10 μM were subjected to ADMET study and toxicity assessment. Only one potential inhibitory ‘hit’ molecule (Hit01, raltegravir’s derivative) was further scrutinized by molecular docking analysis on the active site of PA_N endonuclease (PDB ID: 6E6W). Hit01 was utilized for designing novel potential PA_N endonuclease inhibitors through lead optimization, and then compounds were screened by pharmacophore Hypo1 and docking studies. Six raltegravir’s derivatives with significant estimated activity values and docking scores were obtained. Further, these results certainly do not confirm or indicate the seven compounds (Hit01, Hit07, Hit08, Hit09, Hit10, Hit11 and Hit12) have antiviral activity, and extensive wet-laboratory experimentation is needed to transmute these compounds into clinical drugs.     

Identification of Novel Covalent XPO1 Inhibitors Based on a Hybrid Virtual Screening Strategy

Nuclear export protein 1 (XPO1), a member of the nuclear export protein-p (Karyopherin-P) superfamily, regulates the transport of “cargo” proteins. To facilitate this important process, which is essential for cellular homeostasis, XPO1 must first recognize and bind the cargo proteins. To inhibit this process, small molecule inhibitors have been designed that inhibit XPO1 activity through covalent binding. However, the scaffolds for these inhibitors are very limited. While virtual screening may be used to expand the diversity of the XPO1 inhibitor skeleton, enormous computational resources would be required to accomplish this using traditional screening methods. In the present study, we report the development of a hybrid virtual screening workflow and its application in XPO1 covalent inhibitor screening. After screening, several promising XPO1 covalent molecules were obtained. Of these, compound 8 performed well in both tumor cell proliferation assays and a nuclear export inhibition assay. In addition, molecular dynamics simulations were performed to provide information on the mode of interaction of compound 8 with XPO1. This research has identified a promising new scaffold for XPO1 inhibitors, and it demonstrates an effective and resource-saving workflow for identifying new covalent inhibitors.     

Evaluation of potential inhibitors of squalene synthase based on virtual screening and in vitro studies

Squalene synthase (SQS) is a potential target for hyperlipidemia treatment. To identify novel chemical scaffolds of SQS inhibitors, we generated 3D-QSAR pharmacophore models using HypoGen. The best quantitative pharmacophore model, Hypo 1, was selected for virtual screening using two chemical databases, Specs and Traditional Chinese Medicine database (TCM). The best-mapped hit compounds were then subjected to filtering by Lipinski^’s rule of five and docking studies to refine the hits. Finally, five compounds were selected from the top-ranked hit compounds for SQS inhibitory assay in vitro. Three of these compounds could inhibit SQS in vitro, and should be further evaluated pre-clinically as a treatment for hyperlipidemia.     

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.     

In search of novel inhibitors of anti-cancer drug target fibroblast growth factor receptors: Insights from virtual screening,molecular docking,and molecular dynamics

Fibroblast growth factor receptors (FGFR) are an essential player in oncogenesis and tumor progression. LY2874455 was identified as a pan-FGFR inhibitor and has gone through phase I clinical trial. In the current study, virtual screening was conducted against the PubChem database using a pharmacophore model generated from the crystal structure of FGFR4 inhibited by LY2874455. PubChem 137300327 was identified as the most suitable compound from this screening. Later, molecular docking and molecular dynamics studies conducted with FGFRs corroborated the initial finding. Analysis of ADMET properties disclosed that LY2874455 and PubChem 137300327 share alike properties. Our study suggests that PubChem 137300327 is a potential pan-FGFR inhibitor and can be exploited to treat different cancers following validation in proper wet-lab experiments and study in animal cancer models. This compound also follows Lipinski’s rules and can be used as a lead compound to synthesize more effective anticancer compounds.