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.     

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.     

Natural products as DNA methyltransferase inhibitors: a computer-aided discovery approach

DNA methyltransferases (DNMTs) represent promising targets for the development of unique anticancer drugs. However, all DNMT inhibitors currently in clinical use are nonselective cytosine analogs with significant cytotoxic side-effects. Several natural products, covering diverse chemical classes, have indicated DNMT inhibitory activity, but these effects have yet to be systematically evaluated. In this study, we provide experimental data suggesting that two of the most prominent natural products associated with DNA methylation inhibition, (−)-epigallocathechin-3-gallate (EGCG) and curcumin, have little or no pharmacologically relevant inhibitory activity. We therefore conducted a virtual screen of a large database of natural products with a validated homology model of the catalytic domain of DNMT1. The virtual screening focused on a lead-like subset of the natural products docked with DNMT1, using three docking programs, following a multistep docking approach. Prior to docking, the lead-like subset was characterized in terms of chemical space coverage and scaffold content. Consensus hits with high predicted docking affinity for DNMT1 by all three docking programs were identified. One hit showed DNMT1 inhibitory activity in a previous study. The virtual screening hits were located within the biological-relevant chemical space of drugs, and represent potential unique DNMT inhibitors of natural origin. Validation of these virtual screening hits is warranted.     

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.     

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.     

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.     

基于结构的O-GlcNAc转移酶抑制剂的虚拟筛选

利用O-GlcNAc 转移酶同UDP-GlcNAc复合物的晶体结构,针对其催化位点,对ZINC库中的7134792个分子和FOG库中的4287550个分子进行三轮(HTVS、SP、XP)虚拟筛选,结果发现具有更好类药性的FOG库中包含更多对接得分更低的小分子,且具有更多新颖的化学片段.ZINC库中具有较低对接得分的分子可分为2类,分别占据UDP-GlcNAc的UDP和GlcNAc的结合位置,在此基础上设计得到的分子具有更好的对接得分.证明FOG分子库具有产生更多对接得分更低的分子,所预测和设计的小分子化合物可以成为潜在的抑制剂药物分子.     

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

     

An integrated structure- and pharmacophore-based MMP-12 virtual screening

MMP-12 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades elastin. The main pathologic role of MMP-12 overexpression was suggested to be associated with pathogenesis mechanism of inflammatory respiratory diseases and atherosclerosis. An integrated ligand- and structure-based virtual screening was employed in hope of finding inhibitors with new scaffolds and selectivity for MMP-12. Seven compounds among 18 experimentally tested compounds had a measurable effect on the inhibition of MMP-12 enzyme. Our results demonstrated the applicability of the developed pharmacophore model and selected crystal structure (PDB code: 3F17) to discover new MMP-12 inhibitors. The receptor structure was selected based on cross-docking results. Here, we report the discovery of new class of MMP-12 inhibitors that could be used for lead optimization. For the inhibition of MMP-12, the significance of its interactions with the catalytic residues Glu219 and Ala182 was emphasized through the inspection of the docking poses.     

De novo design of N-(pyridin-4-ylmethyl)aniline derivatives as KDR inhibitors: 3D-QSAR, molecular fragment replacement, protein-ligand interaction fingerprint, and ADMET prediction

Vascular endothelial growth factor (VEGF) and its receptor tyrosine kinase VEGFR-2 or kinase insert domain receptor (KDR) have been identified as promising targets for novel anticancer agents. To achieve new potent inhibitors of KDR, we conducted molecular fragment replacement (MFR) studies for the understanding of 3D-QSAR modeling and the docking investigation of arylphthalazines and 2-((1H-Azol-1-yl)methyl)-N-arylbenzamides-based KDR inhibitors. Two favorable 3D-QSAR models (CoMFA with q ², 0.671; r ², 0.969; CoMSIA with q ², 0.608; r ², 0.936) have been developed to predict the biological activity of new compounds. The new molecular database generated by MFR was virtually screened using Glide (docking) and further evaluated with CoMFA prediction, protein?Cligand interaction fingerprint (PLIF) and ADMET analysis. 44 N-(pyridin-4-ylmethyl)aniline derivatives as novel potential KDR inhibitors were finally obtained. In this paper, the work flow developed could be applied to de novo drug design and virtual screening potential KDR inhibitors, and use hit compounds to further optimize and design new potential KDR inhibitors.     

Successful computer guided planned synthesis of (4R)-thiazolidine carboxylic acid and its 2-substituted analogues as urease inhibitors

By using internal combinatorial library we were able to identify (4R)-thiazolidines carboxylic acid and its 2-substituted analogs as active inhibitors of urease. Molecular modeling and virtual screening were utilized to find out potential compounds. Computational techniques were employed at database of 90,000 ligands and selected the structure representing the low energy conformations, Grid and FlexX docking algorithms were used and the top binding ligands were synthesized and screened in wet-lab.     

Identification of new novel scaffold for Aurora A inhibition by pharmacophore modeling and virtual screening

Aurora kinases belong to family of highly conserved serine/threonine protein kinases that are involved in diverse cell cycle events and play a major role in regulation of cell division. Abnormal expression of Aurora kinases may lead to cancer; hence, these are considered as a potential target in cancer treatment. In this research article, we identified three novel Aurora A inhibitors using modern computational tools. A four-point common 3D pharmacophore hypothesis of Aurora A (AurA) inhibitors was developed using a diverse set of 55 thienopyrimidine derivatives. A three-dimensional quantitative structure–activity relationship (3D-QSAR) study was carried out using atom-based alignment of diverse set of 55 molecules to evaluate the structure– activity relationships. Docking and 3D-QSAR studies were performed with the 3D structure of AurA to evaluate the generated pharmacophore. The pharmacophore model and 3D-QSAR results complemented the results of our docking study. The pharmacophore hypothesis, which yields the best results, was used to screen the Zinc ‘clean drug-like’ database. Various database filters such as 3D-arrangement of pharmacophoric features, predicted activity and binding interaction score were used to retrieve hits having potential AurA inhibition activity.     

An in silico protocol for identifying mTOR inhibitors from natural products

The mammalian target of rapamycin (mTOR) is an anti-cancer target. In this study, we propose an in silico protocol for identifying mTOR inhibitors from the ZINC natural product database. First, a three-dimensional quantitative structure–activity relationship pharmacophore model was built based on known mTOR inhibitors. The model was validated with an external test set, Fischer’s randomization method, a decoy set and pharmacophore mapping conformation testing. The results showed that the model can predict the mTOR inhibition activity of the tested compounds. Virtual screening was performed based on the best pharmacophore model, and the results were then filtered using a molecular docking approach. In addition, molecular mechanics/generalized born surface area analysis was used to refine the selected candidates. The top 20 natural products were selected as potential mTOR inhibitors, and their structural scaffolds could serve as building blocks in designing drug-like molecules for mTOR inhibition.     

Molecular modeling studies, synthesis, and biological evaluation of Plasmodium falciparum enoyl-acyl carrier protein reductase (PfENR) inhibitors

The search for new antimalarial agents is necessary as current drugs in the market become vulnerable due to the emergence of resistance strains of Plasmodium falciparum (P. falciparum). The biosynthetic pathway for fatty acids has been recognized and validated as an important drug target in P.falciparum. One of the important enzymes in this pathway that has a determinant role in completing the cycles of chain elongation is Enoyl-ACP reductase (ENR) also popularly known as FabI. In this paper we report the design, synthesis, and microbial evaluation of inhibitors of Plasmodium enoyl reductase (PfENR). The search for inhibitors involved a virtual screening of the iResearch database with docking simulations. One of the hits was selected and modified to optimize its binding to PfENR; this resulted in the development of analogues of N-benzylidene-4-phenyl-1,3-thiazol-2-amine. The activity of these analogues was predicted from comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models constructed from a dataset of 43 known inhibitors of PfENR. The most promising molecules were synthesized and their structures characterized by spectroscopic techniques. The molecules were screened for in vitro antimalarial activity by whole-cell assay method. Two molecules, viz. VRC-007 and VRC-009, were found to be active at 4.67 and 7.01 μM concentrations, respectively.     

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.     

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.     

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.     

Scaffold hopping for identification of novel D2 antagonist based on 3D pharmacophore modelling of illoperidone analogs

The dopamine D(2) receptor is involved in the etiology of a number of disorders, such as Parkinson's disease, Huntington's Chorea, tardive dyskinesia and schizophrenia. Antagonism of D(2) receptors is implicated in the treatment of various psychiatric disorders. In order to understand essential structural features required for D(2) antagonism, this research article elaborates on the generation of a four-point 3D pharmacophore model which was extracted from a series of 45 novel 3-[[(aryloxy)alkyl]piperidinyl]-1,2-benzisoxazole derivatives. The best pharmacophore model generated consisted of four PRRR features: a positively charged group (P), and three aromatic rings (R). Based on the model generated, a statistically valid 3D-QSAR with good predictability (Q(2) = 0.756) was derived. For the validation of the pharmacophore hypothesis, active compounds were docked against the 3D structure of the D(2) receptor which was constructed through homology modeling. Further, the derived pharmacophore was used as a query to search the Zinc 'clean drug-like' database. Hits retrieved were passed progressively through filters, such as fitness score, predicted activity and docking scores. The resulting hits present new scaffolds with a strong potential for D(2) antagonist.