Development of predictive pharmacophore model for in silico screening,and 3D QSAR CoMFA and CoMSIA studies for lead optimization,for designing of potent tumor necrosis factor alpha converting enzyme inhibitors

A chemical feature-based pharmacophore model was developed for Tumor Necrosis Factor-α converting enzyme (TACE) inhibitors. A five point pharmacophore model having two hydrogen bond acceptors (A), one hydrogen bond donor (D) and two aromatic rings (R) with discrete geometries as pharmacophoric features was developed. The pharmacophore model so generated was then utilized for in silico screening of a database. The pharmacophore model so developed was validated by using four compounds having proven TACE inhibitory activity which were grafted into the database. These compounds mapped well onto the five listed pharmacophoric features. This validated pharmacophore model was also used for alignment of molecules in CoMFA and CoMSIA analysis. The contour maps of the CoMFA/CoMSIA models were utilized to provide structural insight for activity improvement of potential novel TACE inhibitors. The pharmacophore model so developed could be used for in silico screening of any commercial/in house database for identification of TACE inhibiting lead compounds, and the leads so identified could be optimized using the developed CoMSIA model. The present work highlights the tremendous potential of the two mutually complementary ligand-based drug designing techniques (i.e. pharmacophore mapping and 3D-QSAR analysis) using TACE inhibitors as prototype biologically active molecules.     

Glutamine: fructose-6-phosphate amidotransferase (GFAT): homology modelling and designing of new inhibitors using pharmacophore and docking based hierarchical virtual screening protocol

Glutamine: fructose-6-phosphate amidotransferase (GFAT), also termed GFPT1 and GFAT1, catalyzes the first committed step of the hexosamine biosynthesis pathway in mammals and consequently plays an important role in type 2 diabetes. In the present study, a combination of pharmacophore modelling, homology modelling, and molecular docking analysis was performed to design new glutamine competitive inhibitors of human GFAT, and to investigate important interaction details of inhibitor molecules. A pharmacophore model of GFAT inhibitors was developed, subsequently validated, and utilized for the screening by the PHASE database to identify new molecules. Afterwards, homology modelling was performed to construct the glutamine-binding site of the GFAT protein. The modelled active site was utilized to dock the studied molecules to investigate important receptor-ligand interactions and to scrutinize database-screened molecules on the basis of essential interactions. This systematic in silico protocol helped us to identify new molecules that would be explored for the treatment of type 2 diabetes and its complications.     

Hierarchical virtual screening: identification of potential high-affinity and selective β(3)-adrenergic receptor agonists

The hierarchical virtual screening (HVS) study, consisting of pharmacophore modelling, docking and VS of the generated focussed virtual library, has been carried out to identify novel high-affinity and selective β(3)-adrenergic receptor (β-AR) agonists. The best pharmacophore model, comprising one H-bond donor, two hydrophobes, one positive ionizable and one negative ionizable feature, was developed based on a training set of 51 β(3)-AR agonists using the pharmacophore generation protocol implemented in Discovery Studio. The model was further validated with the test set, external set and ability of the pharmacophoric features to complement the active site amino acids of the homology modelled β(3)-AR developed using MODELLER software. The focussed virtual library was generated using the structure-based insights gained from our earlier reported comprehensive study focussing on the structural basis of β-AR subtype selectivity of representative agonists and antagonists. The HVS with the sequential use of the best pharmacophore model and homology modelled β(3)-AR in the screening of the generated focussed library has led to the identification of potential virtual leads as novel high-affinity and selective β(3)-AR agonists.     

Energetic analysis of fragment docking and application to structure-based pharmacophore hypothesis generation

We have developed a method that uses energetic analysis of structure-based fragment docking to elucidate key features for molecular recognition. This hybrid ligand- and structure-based methodology uses an atomic breakdown of the energy terms from the Glide XP scoring function to locate key pharmacophoric features from the docked fragments. First, we show that Glide accurately docks fragments, producing a root mean squared deviation (RMSD) of <1.0 Å for the top scoring pose to the native crystal structure. We then describe fragment-specific docking settings developed to generate poses that explore every pocket of a binding site while maintaining the docking accuracy of the top scoring pose. Next, we describe how the energy terms from the Glide XP scoring function are mapped onto pharmacophore sites from the docked fragments in order to rank their importance for binding. Using this energetic analysis we show that the most energetically favorable pharmacophore sites are consistent with features from known tight binding compounds. Finally, we describe a method to use the energetically selected sites from fragment docking to develop a pharmacophore hypothesis that can be used in virtual database screening to retrieve diverse compounds. We find that this method produces viable hypotheses that are consistent with known active compounds. In addition to retrieving diverse compounds that are not biased by the co-crystallized ligand, the method is able to recover known active compounds from a database screen, with an average enrichment of 8.1 in the top 1% of the database.     

Refined pharmacophore features for virtual screening of human thromboxane A2 receptor antagonists

For a long time, the structural basis of TXA2 receptor is limited due to the lack of crystal structure information, till the release of the crystal structure of TXA2 receptor, which deepens our understanding about ligand recognition and selectivity mechanisms of this physiologically important receptor. In this research, we report the successful implementation in the discovery of an optimal pharmacophore model of human TXA2 receptor antagonists through virtual screening. Structure-based pharmacophore models were generated based on two crystal structures of human TXA2 receptor (PDB entry 6IIU and 6IIV). Docking simulation revealed interaction modes of the virtual screening hits against TXA2 receptor, which was validated through molecular dynamics simulation and binding free energy calculation. ADMET properties were also analyzed to evaluate the toxicity and physio-chemical characteristics of the hits. The research would provide valuable insight into the binding mechanisms of TXA2 receptor antagonists and thus be helpful for designing novel antagonists.     

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.     

A Novel Pharmacophore Model Derived from a Class of Capsid Protein Enterovirus 71 Inhibitors

Capsid protein enterovirus 71 (EV71) is one of the major viruses that cause the severe encephalitis and thus result in a high mortality in children less than 5 years of age.In an effort to discover new potent inhibitors against EV71,a novel three-dimensional pharmacophore model was developed on 24 inhibitors with different molecular structures and bioactivities.The best hypothesis (Hypo1) has a high predictive power and consists of four features,namely,one hydrophobic point (HY) and three hydrogen-bond acceptors (HA).Two key features of the best Hypo1,HY1 and HA3 match well with an important narrow hydrophobic canyon and with the surface of LYS274 in the target EV71 active site,respectively.The more versatile feature,HA1,is firstly found to be very influential on these compounds' bioactivities,which may interact with the other side of the active site in the EV71 receptor.The application of the model is successful in predicting the activities of 30 known EV71 inhibitors with a correlation coefficient of 0.831.Furthermore,Hypo1 demonstrates a superior screening capability for retrieving inhibitors from the database with a high enrichment factor of 70.This study provides some important clues in search for more potent inhibitors against EV71 infection.     

Identification of Novel β3-Adrenoceptor Agonists Using Energetic Analysis, Structure Based Pharmacophores and Virtual Screening

β3 Adrenergic receptor (β3-AR), is a potential therapeutic target for the treatment of type II diabetes and obesity. We report the identification of novel compounds as β3-AR agonists by integrating different approaches of energetic analysis, structure based pharmacophore designing and virtual screening. In a step wise filtering protocol, structure based virtual screening of 2,33,450 compounds was done. These molecules were docked into the active site of the receptor utilizing three levels of accuracy; ligands passing the HTVS (high throughput virtual screening) step were subsequently analyzed in Glide SP (Standard Precision) and finally in Glide XP (Extra Precision) to estimate the receptor ligand binding affinities. In the second step a total of 300 pharmacophore hypotheses were generated from a set of known and diverse β3-AR agonists. The best hypothesis showed six features: three hydrogen bond acceptors, one positively charged group, and two aromatic rings. To cross validate, pharmacophore filtering was done on the set of shortlisted compounds from structure based VS (virtual screening). The different screening techniques employed were validated using enrichment factor calculations. The energetic based Pharmacophore performed fairly well at distinguishing active from the inactive compounds and yielded a greater diversity of active molecules whereas the number of actives retrieved in the case of structure based screening was the highest.     

Quantitative structure-agonist activity relationship of capsaicin analogues

Summary The MULTIple Computer Automated Structure Evaluation (MULTICASE) methodology has been used to study the quantitative structure-agonist activity relationship of a series of capsaicin agonists. A number of substructures and physicochemical properties of capsaicin analogues were identified as being responsible for high agonist potency. The optimal log P value for the agonist potency as estimated from QSAR analysis is 5.12. It was also found that a cluster of inactive molecules in the database have lipophilicity values below 2.94. Molecular modeling was employed to elucidate the detailed structural features of the pharmacophore of capsaicin analogues. Systematic conformational analysishas shown that the activity of capsaicin analogues strongly depends upon their ability to reach the required conformational profile. Based upon these observations, a three-dimensional pharmacophore model for the capsaicin-receptor interactions is proposed.     

Effective virtual screening protocol for CYP2C9 ligands using a screening site constructed from flurbiprofen and S-warfarin pockets

An effective virtual screening protocol was developed against an extended active site of CYP2C9, which was derived from X-ray structures complexed with flubiprofen and S-warfarin. Virtual screening has been effectively supported by our structure-based pharmacophore model. Importance of hot residues identified by mutation data and structural analysis was first estimated in an enrichment study. Key role of Arg108 and Phe114 in ligand binding was also underlined. Our screening protocol successfully identified 76% of known CYP2C9 ligands in the top 1% of the ranked database resulting 76-fold enrichment relative to random situation. Relevance of the protocol was further confirmed in selectivity studies, when 89% of CYP2C9 ligands were retrieved from a mixture of CYP2C9 and CYP2C8 ligands, while only 22% of CYP2C8 ligands were found applying the structure-based pharmacophore constraints. Moderate discrimination of CYP2C9 ligands from CYP2C18 and CYP2C19 ligands could also be achieved extending the application domain of our virtual screening protocol for the entire CYP2C family. Our findings further demonstrate the existence of an active site comprising of at least two binding pockets and strengthens the need of involvement of protein flexibility in virtual screening.     

Construction and Evaluation of Merged Pharmacophore Based on Peroxisome Proliferator Receptor-Alpha Agonists

Pharmacophore is a commonly used method for molecular simulation, including ligand-based pharmacophore (LBP) and structure-based pharmacophore (SBP). LBP can be utilized to identify active compounds usual with lower accuracy, and SBP is able to use for distinguishing active compounds from inactive compounds with frequently higher missing rates. Merged pharmacophore (MP) is presented to integrate advantages and avoid shortcomings of LBP and SBP. In this work, LBP and SBP models were constructed for the study of peroxisome proliferator receptor-alpha (PPARα) agonists. According to the comparison of the two types of pharmacophore models, mainly and secondarily pharmacological features were identified. The weight and tolerance values of these pharmacological features were adjusted to construct MP models by single-factor explorations and orthogonal experimental design based on SBP model. Then, the reliability and screening efficiency of the best MP model were validated by three databases. The best MP model was utilized to compute PPARα activity of compounds from traditional Chinese medicine. The screening efficiency of MP model outperformed individual LBP or SBP model for PPARα agonists, and was similar to combinatorial screening of LBP and SBP. However, MP model might have an advantage over the combination of LBP and SBP in evaluating the activity of compounds and avoiding the inconsistent prediction of LBP and SBP, which would be beneficial to guide drug design and optimization.     

Receptor-Based Pharmacophore Modeling in the Search for Natural Products for COVID-19 Mpro

Considering the urgency of the COVID-19 pandemic, we developed a receptor-based pharmacophore model for identifying FDA-approved drugs and hits from natural products. The COVID-19 main protease (M^pro) was selected for the development of the pharmacophore model. The model consisted of a hydrogen bond acceptor, donor, and hydrophobic features. These features demonstrated good corroboration with a previously reported model that was used to validate the present model, showing an RMSD value of 0.32. The virtual screening was carried out using the ZINC database. A set of 208,000 hits was extracted and filtered using the ligand pharmacophore mapping, applying the lead-like properties. Lipinski’s filter and the fit value filter were used to minimize hits to the top 2000. Simultaneous docking was carried out for 200 hits for natural drugs belonging to the FDA-approved drug database. The top 28 hits from these experiments, with promising predicted pharmacodynamic and pharmacokinetic properties, are reported here. To optimize these hits as M^pro inhibitors and potential treatment options for COVID-19, bench work investigations are needed.     

Toward the identification of a reliable 3D QSAR pharmacophore model for the CCK2 receptor antagonism

The present study describes application of computational approaches to identify a validated and reliable 3D QSAR pharmacophore model for the CCK-2R antagonism through integrated ligand and structure based studies using anthranilic sulfonamide and 1,3,4-benzotriazepine based CCK-2R antagonists. The best hypothesis consisted five features viz. two aliphatic hydrophobic, one aromatic hydrophobic, one H-bond acceptor, and one ring aromatic feature with an excellent correlation for 34 training set (r2(training) = 0.83) and 58 test set compounds (r2(test) = 0.74). This model was validated through F-test and docking studies at the active site of the plausible CCK-2R where the 99% significance and well corroboration with the pharmacophore model respectively describes the model's reliability. The model also predicts well to other known clinically effective CCK-2R antagonists. Therefore, the developed model may useful in finding new scaffolds that may aid in design and develop new chemical entities (NCEs) as potent CCK-2R antagonists before their synthesis.     

Multiple receptor-ligand based pharmacophore modeling and molecular docking to screen the selective inhibitors of matrix metalloproteinase-9 from natural products

Matrix metalloproteinase-9 (MMP-9) is an attractive target for cancer therapy. In this study, the pharmacophore model of MMP-9 inhibitors is built based on the experimental binding structures of multiple receptor-ligand complexes. It is found that the pharmacophore model consists of six chemical features, including two hydrogen bond acceptors, one hydrogen bond donor, one ring aromatic regions, and two hydrophobic (HY) features. Among them, the two HY features are especially important because they can enter the S1′ pocket of MMP-9 which determines the selectivity of MMP-9 inhibitors. The reliability of pharmacophore model is validated based on the two different decoy sets and relevant experimental data. The virtual screening, combining pharmacophore model with molecular docking, is performed to identify the selective MMP-9 inhibitors from a database of natural products. The four novel MMP-9 inhibitors of natural products, NP-000686, NP-001752, NP-014331, and NP-015905, are found; one of them, NP-000686, is used to perform the experiment of in vitro bioassay inhibiting MMP-9, and the IC₅₀ value was estimated to be only 13.4 µM, showing the strongly inhibitory activity of NP-000686 against MMP-9, which suggests that our screening results should be reliable. The binding modes of screened inhibitors with MMP-9 active sites were discussed. In addition, the ADMET properties and physicochemical properties of screened four compounds were assessed. The found MMP-9 inhibitors of natural products could serve as the lead compounds for designing the new MMP-9 inhibitors by carrying out structural modifications in the future.     

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.     

Virtual screening studies to design potent CDK2-cyclin A inhibitors

The cell division cycle is controlled by cyclin-dependent kinases (CDK), which consist of a catalytic subunit (CDK1-CDK8) and a regulatory subunit (cyclin A-H). Pharmacophore analysis indicates that the best inhibitor model consists of (1) two hydrogen bond acceptors, (2) one hydrogen bond donor, and (3) one hydrophobic feature. The HypoRefine pharmacophore model gave an enrichment factor of 1.31 and goodness of fit score of 0.76. Docking studies were carried out to explore the structural requirements for the CDK2-cyclin A inhibitors and to construct highly predictive models for the design of new inhibitors. Docking studies demonstrate the important role of hydrogen bond and hydrophobic interactions in determining the inhibitor-receptor binding affinity. The validated pharmacophore model is further used for retrieving the most active hits/lead from a virtual library of molecules. Subsequently, docking studies were performed on the hits, and novel series of potent leads were suggested based on the interaction energy between CDK2-cyclin A and the putative inhibitors.     

Homology modeling,docking and structure-based pharmacophore of inhibitors of DNA methyltransferase

DNA methyltransferase 1 (DNMT1) is an emerging epigenetic target for the treatment of cancer and other diseases. To date, several inhibitors from different structural classes have been published. In this work, we report a comprehensive molecular modeling study of 14 established DNTM1 inhibitors with a herein developed homology model of the catalytic domain of human DNTM1. The geometry of the homology model was in agreement with the proposed mechanism of DNA methylation. Docking results revealed that all inhibitors studied in this work have hydrogen bond interactions with a glutamic acid and arginine residues that play a central role in the mechanism of cytosine DNA methylation. The binding models of compounds such as curcumin and parthenolide suggest that these natural products are covalent blockers of the catalytic site. A pharmacophore model was also developed for all DNMT1 inhibitors considered in this work using the most favorable binding conformations and energetic terms of the docked poses. To the best of our knowledge, this is the first pharmacophore model proposed for compounds with inhibitory activity of DNMT1. The results presented in this work represent a conceptual advance for understanding the protein–ligand interactions and mechanism of action of DNMT1 inhibitors. The insights obtained in this work can be used for the structure-based design and virtual screening for novel inhibitors targeting DNMT1.     

Pharmacophore modeling and parallel screening for PPAR ligands

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.     

基于结构和药效团特征的人类腺苷受体拮抗剂选择性比较

腺苷受体是重要的治疗靶标,选择性腺苷受体拮抗剂具有广泛的临床应用前景.本文通过同源模建构建了腺苷A₁、A_2B和A₃受体的结构,采用LigandScout 3.12软件分别构建了腺苷受体四种亚型的拮抗剂药效团模型.然后利用Schrödinger程序中的Induced Fit Docking模块完成受体-拮抗剂结合模式的预测,并与药效团结果进行比对.结果发现,由于结合口袋部位的残基在家族间高度保守,模建得到的各个亚型受体的初始结构活性口袋部位极为相似,无法用于亚型选择性拮抗剂的识别.而腺苷受体四种亚型拮抗剂药效团的药效特征与空间排布都不同,并与以前突变实验信息相吻合.研究结果说明,结合口袋部位的优化是模建中的关键步骤,基于配体的药效团模型所包含的一系列药效特征元素如氢键受体、氢键供体、疏水基团、芳环中心,可以很好地表征受体结合部位氢键、疏水空腔的位置及其方向.本文研究结果可以为进一步的优化同源模建结果,寻找新型的人类腺苷受体选择性拮抗剂提供理论依据.