A rational approach to selective pharmacophore designing: an innovative strategy for specific recognition of Gsk3β

We propose a novel cheminformatics approach that combines structure and ligand-based design to identify target-specific pharmacophores with well-defined exclusion ability. Our strategy includes the prediction of selective interactions, developing structure, and knowledge-based selective pharmacophore models, followed by database screening and molecular docking. This unique strategy was employed in addressing the off-target toxicity of Gsk3β and CDKs. The connections of Gsk3β in eukaryotic cell apoptosis and the extensive potency of Gsk3β inhibitors to block cell death have made it a potential drug-discovery target for many grievous human disorders. Gsk3β is phylogenetically very closely related to the CDKs, such as CDK1 and CDK2, which are suggested to be the off-target proteins of Gsk3β inhibitors. Here, we have employed novel computational approaches in designing the ligand candidates that are potentially inhibitory against Gsk3β, with well-defined the exclusion ability to CDKs. A structure-ligand -based selective pharmacophore was modeled. This model was used to retrieve molecules from the zinc database. The hits retrieved were further screened by molecular docking and protein-ligand interaction fingerprints. Based on these results, four molecules were predicted as selective Gsk3β antagonists. It is anticipated that this unique approach can be extended to investigate any protein-ligand specificity.     

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure–activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set \({R}^{2}= 0.904\); test set: \({Q}^{2}= 0.676\); whole data set: stability \(s = 0.803\)). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.     

Pharmacophore generation and atom-based 3D-QSAR of novel 2-(4-methylsulfonylphenyl)pyrimidines as COX-2 inhibitors

Cyclooxygenase-2 (COX-2) inhibitors are widely used for the treatment of pain and inflammatory disorders such as rheumatoid arthritis and osteoarthritis. A series of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives has been reported as COX-2 inhibitors. In order to understand the structural requirement of these COX-2 inhibitors, a ligand-based pharmacophore and atom-based 3D-QSAR model have been developed. A five-point pharmacophore with four hydrogen bond acceptors (A) and one hydrogen bond donor (D) was obtained. The pharmacophore hypothesis yielded a 3D-QSAR model with good partial least-square (PLS) statistics results. The training set correlation is characterized by PLS factors (r ² = 0.642, SD = 0.65, F = 82.7, P = 7.617 e − 12). The test set correlation is characterized by PLS factors (Q ² _ext = 0.841, RMSE = 0.24,Pearson−R = 0.91). A docking study revealed the binding orientations of these inhibitors at active site amino acid residues (Arg513, Val523, Phe518, Ser530, Tyr355, His90) of COX-2 enzyme. The results of ligand-based pharmacophore hypothesis and atom-based 3D-QSAR give detailed structural insights as well as highlights important binding features of novel 2-(4-methylsulfonylphenyl)pyrimidine derivatives as COX-2 inhibitors which can provide guidance for the rational design of novel potent COX-2 inhibitors.     

An efficient multistep ligand-based virtual screening approach for GPR40 agonists

G protein-coupled receptor 40/free fatty acid receptor 1 (GPR40/FFAR1) is a member of the GPCR superfamily, and GPR40 agonists have therapeutic potential for type 2 diabetes. With the crystal structure of GPR40 currently unavailable, various ligand-based virtual screening approaches can be applied to identify novel agonists of GPR40. It is known that each ligand-based method has its own advantages and limitations. To improve the efficiency of individual ligand-based methods, an efficient multistep ligand-based virtual screening approach is presented in this study, including the pharmacophore-based screening, physicochemical property filtering, protein–ligand interaction fingerprint similarity analysis, and 2D-fingerprint structural similarity search. A focused decoy library was generated and used to evaluate the efficiency of this virtual screening protocol. This multistep workflow not only significantly improved the hit rate compared with each individual ligand-based method, but also identified diverse known actives from decoys. This protocol may serve as an efficient virtual screening tool for the targets without crystal structures available to discover novel active compounds.     

基于支持向量机、支持向量回归和分子对接的CYP450 1A2抑制剂的发现研究

支持向量机,支持向量回归和分子对接的计算方法已广泛应用于化合物的药理活性计算。为了提高计算的准确性和可靠性,拟以细胞色素P450酶1A2为研究载体,运用建立的联合SVM-SVR-Docking计算模型预测潜在的CYP1A2抑制剂。其中,建立的最优SVM定性模型训练集,内部测试集和外部测试集的准确率分别为99.432%,97.727%和91.667%。最优SVR定量模型训练集和测试集的R和MSE分别为0.763,0.013和0.753,0.056。实验表明两个模型具有较高的准确性和可靠性。通过对SVM和SVR模型结果的比较分析,发现连接性指数、分子构成描述符和官能团数目等分子描述符可能与CYP1A2抑制剂的辨识和活性预测密切相关。随后利用分子对接技术分析化合物与CYP1A2的结合构象及相互作用的稳定性。形成氢键相互作用的关键氨基酸包括THR124,ASP320;形成疏水相互作用的关键氨基酸包括ALA317和GLY316。所获得模型可用于天然产物化学成分中CYP1A2潜在抑制剂的活性计算及其介导的药物-药物相互作用预测提供理论指导,也为合理联合用药提供一定参考。共获得20个对CYP1A2具有潜在抑制活性的化合物。部分结果与文献结果相互印证,进一步说明了模型的准确性和联合计算策略的可靠性.     

Docking of a novel DNA methyltransferase inhibitor identified from high-throughput screening: insights to unveil inhibitors in chemical databases

Inhibitors of DNA methyltransferase (DNMT) are attractive compounds not only as potential therapeutic agents for the treatment of cancer and other diseases, but also as research tools to investigate the role of DNMTs in epigenetic events. Recent advances in high-throughput screening (HTS) for epigenetic targets and the availability of the first crystallographic structure of human DNMT1 encourage the integration of research strategies to uncover and optimize the activity of DNMT inhibitors. Herein, we present a binding model of a novel small-molecule DNMT1 inhibitor obtained by HTS, recently released in a public database. The docking model is in agreement with key interactions previously identified for established inhibitors using extensive computational studies including molecular dynamics and structure-based pharmacophore modeling. Based on the chemical structure of the novel inhibitor, a sequential computational screening of five chemical databases was performed to identify candidate compounds for testing. Similarity searching followed by molecular docking of chemical databases such as approved drugs, natural products, a DNMT-focused library, and a general screening collection, identified at least 108 molecules with promising DNMT inhibitory activity. The chemical structures of all hit compounds are disclosed to encourage the research community working on epigenetics to test experimentally the enzymatic and demethylating activity in vivo. Five candidate hits are drugs approved for other indications and represent potential starting points of a drug repurposing strategy.     

Comparative docking and CoMFA analysis of curcumine derivatives as HIV-1 integrase inhibitors

The docking studies and comparative molecular field analysis (CoMFA) were performed on highly active molecules of curcumine derivatives against 3′ processing activity of HIV-1 integrase (IN) enzyme. The optimum CoMFA model was selected with statistically significant cross-validated r² value of 0.815 and non-cross validated r ² value of 0.99. The common pharmacophore of highly active molecules was used for screening of HIV-1 IN inhibitors. The high contribution of polar interactions in pharmacophore mapping is well supported by docking and CoMFA results. The results of docking, CoMFA, and pharmacophore mapping give structural insights as well as important binding features of curcumine derivatives as HIV-1 IN inhibitors which can provide guidance for the rational design of novel HIV-1 IN inhibitors.     

Identification of novel potential HIF-prolyl hydroxylase inhibitors by in silico screening

Suppression of HIF-prolyl hydroxylase (PHD) activity by small-molecule inhibitors leads to the stabilization of hypoxia inducible factor and has been recognized as promising drug target for the treatment of ischemic diseases. In this study, pharmacophore-based virtual screening and molecular docking approaches were concurrently used with suitable modifications to identify target-specific PHD inhibitors with better absorption, distribution, metabolism, and excretion properties and to readily minimize false positives and false negatives. A customized method based on the active site information of the enzyme was used to generate a pharmacophore hypothesis (AAANR). The hypothesis was validated and utilized for chemical database screening and the retrieved hit compounds were subjected to molecular docking for further refinement. AAANR hypothesis comprised three H-bond acceptor, one negative ionizable group and one aromatic ring feature. The hypothesis was validated using decoy set with a goodness of fit score of 2 and was used as a 3D query for database screening. After manual selection, molecular docking and further refinement based on the molecular interactions of inhibitors with the essential amino acid residues, 18 hits with good absorption, distribution, metabolism, and excretion (ADME) properties were selected as excellent PHD inhibitors. The best pharmacophore hypothesis, AAANR, contains chemical features required for the effective inhibition of PHD. Using AAANR, we have identified 18 potential and diverse virtual leads, which can be readily evaluated for their potency as novel inhibitors of PHD. It can be concluded that the combination of pharmacophore, molecular docking, and manual interpretation approaches can be more successful than the traditional approach alone for discovering more effective inhibitors.     

Identification of dual Acetyl-CoA carboxylases 1 and 2 inhibitors by pharmacophore based virtual screening and molecular docking approach

Acetyl-CoA carboxylase (ACC) is a crucial metabolic enzyme that plays a vital role in obesity-induced type 2 diabetes and fatty acid metabolism. To identify dual inhibitors of Acetyl-CoA carboxylase1 and Acetyl-CoA carboxylase2, a pharmacophore modelling approach has been employed. The best HypoGen pharmacophore model for ACC2 inhibitors (Hypo1_ACC2) consists of one hydrogen bond acceptor, one hydrophobic aliphatic and one hydrophobic aromatic feature, whereas the best pharmacophore (Hypo1_ACC1) for ACC1 consists of one additional hydrogen-bond donor (HBD) features. The best pharmacophore hypotheses were validated by various methods such as test set, decoy set and Cat-Scramble methodology. The validated pharmacophore models were used to screen several small-molecule databases, including Specs, NCI, ChemDiv and Natural product databases to identify the potential dual ACC inhibitors. The virtual hits were then subjected to several filters such as estimated $\text{ IC}_{50}$ value, quantitative estimation of drug-likeness and molecular docking analysis. Finally, three novel compounds with diverse scaffolds were selected as potential starting points for the design of novel dual ACC inhibitors.     

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of ‘multiple pharmacophore screening’ for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.     

Discovery and identification of PIM-1 kinase inhibitors through a hybrid screening approach

PIM-1 kinase is an important therapeutic target in the treatment of cancer. Discovery and identification of PIM-1 Inhibitors with novel scaffolds are an effective way for developing potent therapeutic agents for the treatment of cancers. Here we proposed a hybrid screening approach which combines an optimal structure-based drug design strategy and a simple pharmacophore model to discover PIM-1 kinase inhibitors. With the proposed hybrid screening approach, the SPECS database containing 204,580 molecules was screened. In total, 89 hits were obtained. Forty three of them were purchased and tested in bioassays. Finally, 5 lead compounds with novel scaffolds were identified to exhibit promising antitumor activities against human leukemia cell line MV4-11, K-562 and human prostate cancer cell line PC-3 and DU145. Their $\hbox {IC}_{50}$ values range from 4.40 to $37.96 \,\upmu \hbox {M}$ . Three hits with 3 different scaffolds were selected from these five hits for binding mode analysis. It was demonstrated that the subtle differences in the interactions of the representatives with PIM-1 kinase contribute to the different inhibitory activities. It was also demonstrated that the suggested hybrid screening approach is an effective method to discover PIM-1 inhibitors possessing different scaffolds. These leads have a strong likelihood to act as further starting points for us in the optimization and development of potent PIM-1 inhibitors.     

Toward the computer-aided discovery of FabH inhibitors. Do predictive QSAR models ensure high quality virtual screening performance?

Antibiotic resistance has increased over the past two decades. New approaches for the discovery of novel antibacterials are required and innovative strategies will be necessary to identify novel and effective candidates. Related to this problem, the exploration of bacterial targets that remain unexploited by the current antibiotics in clinical use is required. One of such targets is the \(\beta \) -ketoacyl-acyl carrier protein synthase III (FabH). Here, we report a ligand-based modeling methodology for the virtual-screening of large collections of chemical compounds in the search of potential FabH inhibitors. QSAR models are developed for a diverse dataset of 296 FabH inhibitors using an in-house modeling framework. All models showed high fitting, robustness, and generalization capabilities. We further investigated the performance of the developed models in a virtual screening scenario. To carry out this investigation, we implemented a desirability-based algorithm for decoys selection that was shown effective in the selection of high quality decoys sets. Once the QSAR models were validated in the context of a virtual screening experiment their limitations arise. For this reason, we explored the potential of ensemble modeling to overcome the limitations associated to the use of single classifiers. Through a detailed evaluation of the virtual screening performance of ensemble models it was evidenced, for the first time to our knowledge, the benefits of this approach in a virtual screening scenario. From all the obtained results, we could arrive to a significant main conclusion: at least for FabH inhibitors, virtual screening performance is not guaranteed by predictive QSAR models.     

Integration-mediated prediction enrichment of quantitative model for Hsp90 inhibitors as anti-cancer agents: 3D-QSAR study

The present study describes a systematic 3D-QSAR study consisting of pharmacophore modeling, docking, and integration of ligand-based and structure-based drug design approaches, applied on a dataset of 72 Hsp90 inhibitors as anti-cancer agents. The best pharmacophore model, with one H-bond donor (HBD), one H-bond acceptor (HBA), one hydrophobic_aromatic (Hy_Ar), and two hydrophobic_aliphatic (Hy_Al) features, was developed using the Catalyst/HypoGen algorithm on a training set of 35 compounds. The model was further validated using test set, external set, Fisher’s randomization method, and ability of the pharmacophoric features to complement the active site amino acids. Docking analysis was performed using Hsp90 chaperone (PDB-Id: 1uyf) along with water molecules reported to be crucial for binding and catalysis (Sgobba et al. ChemMedChem 4:1399–1409, 2009). Furthermore, an integration of the ligand-based as well as structure-based drug design approaches was done leading to the integrated model, which was found to be superior over the best pharmacophore model in terms of its predictive ability on internal [integrated model 2: R _(train) = 0.954, R _(test) = 0.888; Hypo-01: R _(train) = 0.912 and R _(test) = 0.819] as well as on external data set [integrated model 2: R _(ext.set) = 0.801; Hypo-01: R _(ext.set) = 0.604].     

Pharmacophore mapping of the crucial mediators of acetylcholinesterase and butyrylcholinesterase dual inhibition in Alzheimer's disease

Optimization and re-optimization of bioactive molecules using in silico methods have found application in the design of more active ones. Herein, we applied a pharmacophore modeling approach to screen potent dual inhibitors of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) aimed at Alzheimer's disease (AD) treatment. The investigation entails molecular dynamics simulation, docking, pharmacophore modeling, drug-like screening, and binding energy analysis. We prepared a pharmacophore model from approved inhibitors of AChE and BuChE to predict the crucial moieties required for optimum molecular interaction with these proteins. The obtained pharmacophore model, used for database screening via some critical criteria, showed 229 hit molecules. Further analyses showed 42 likely dual inhibitors of AChE/BuChE with drug-like and pharmacokinetics properties the same as the approved cholinesterase inhibitors. Finally, we identified 14 dual molecules with improved potentials over the existing inhibitors and simulated ZINC92385797 bound to human AChE and BuChE structure after noticing that these 14 molecules are similar. The selected compound maintained relative stability at the active sites of both proteins over 120 ns simulation. Our integrated protocols showed the pertinent recipes of anti-AD drug design through the in silico pipeline.

Graphical abstract

     

Insights into the binding modes of human β<Subscript>3</Subscript>-adrenergic receptor agonists with ligand-based and receptor-based methods

Agonists of β₃-adrenergic receptor (AR) have been thought as potential drugs for the treatment of obesity, type II diabetes, and overactive bladder. In order to clarify the essential structure–activity relationship and the detailed binding modes of β₃-AR agonists as well as to identify new lead compounds activating β₃-AR, ligand-based and receptor-based methods were applied. The pharmacophore models were developed based on 144 β₃-AR agonists. Meanwhile, the homology model of the β₃-AR was built based on the crystal structure of β₂-AR. The pharmacophore model and the homology model mapped with each other very well, and some important information was obtained from the docking result. For example, agonists formed similar hydrogen-bonding interactions with residues Asp117, Arg315, and Asn332, π–π stacking interaction with residues Phe308, and hydrophobic interactions with residues Val118, Val121, Ala197, Phe198, Ala199, Phe309, and Phe328 of β₃-AR. And the major difference about binding mode from the crystal structures of β₁- and β₂-ARs is the hydrogen-bonding interaction with the residue Arg315, which corresponds to the residue Asn313 of β₁-AR and the residue His296 of β₂-AR, respectively. Our findings may be crucial for the design and development of novel selective and potent β₃-AR agonists.     

基于亲电/亲核反应描述符的气体介质绝缘强度预测

研究分子微观参数与气体介质绝缘强度的关联,可为SF₆替代气体筛选提供方向.本文基于密度泛函理论,采用M06-2X泛函与def2系列基组,计算了73种气体分子的亲电/亲核反应描述符,包括轨道能量参数、概念密度泛函理论的参数、不同电子概率密度等值面的静电势参数等;分析了各描述符与气体介质绝缘强度的相关性,以及描述符的独立性,最终提出了绝缘强度预测模型.最低空轨道能量、正负静电势表面积、静电势平均偏差、简缩局部亲电指数最小值与绝缘强度相关性较强,且彼此间相关性较低.预测模型在电子概率密度0.0002 a.u.时精度最优,其可决系数R²为0.809,均方误差MSE为0.096.