A three-dimensional chemical phase pharmacophore mapping,QSAR modelling and electronic feature analysis of benzofuran salicylic acid derivatives as LYP inhibitors

Lymphoid tyrosine phosphatase (LYP), encoded by the PTPN22 gene, has a critical negative regulatory role in T-cell antigen receptor (TCR) and emerged as a promising drug target for human autoimmune diseases. A five-point pharmacophore with two hydrogen bond acceptors, one hydrogen bond donor and two aromatic ring features was generated for a series of benzofuran salicylic acid derivatives as LYP inhibitors in order to elucidate their anti-autoimmune activity. The generated pharmacophore yielded a significant 3D-QSAR model with r² of 0.9146 for a training set of 27 compounds. The model also showed excellent predictive power with Q² of 0.7068 for a test set of eight compounds. The investigation of the 3D-QSAR model has revealed the structural insights which could lead to more potent analogues. The most active and inactive compounds were further subjected to electronic structure analysis using density functional theory (DFT) at B3LYP/3?21^?G level to support the 3D-QSAR predictions. The results obtained from this study are expected to be useful in the proficient design and development of benzofuran salicylic acid derivatives as inhibitors of LYP.     

Molecular description of pyrimidine-based inhibitors with activity against FAK combining 3D-QSAR analysis,molecular docking and molecular dynamics

Focal adhesion kinase (FAK) is a promising target for developing more effective anticancer drugs. To better understand the structure-activity relationships and mechanism of actions of FAK inhibitors, a molecular modeling study using 3D-QSAR, molecular docking, molecular dynamics simulations, and binding free energy analysis were conducted. Two types of satisfactory 3D-QSAR models were generated, comprising the CoMFA model (R²_cv = 0.528, R²_pred = 0.7557) and CoMSIA model (R²_cv = 0.757, R²_pred = 0.8362), for predicting the inhibitory activities of novel inhibitors. The derived contour maps indicate structural characteristics for substituents on the template. Molecular docking, molecular dynamic simulations and binding free energy calculations further reveal that the binding of inhibitors to FAK is mainly contributed from hydrophobic, electrostatic and hydrogen bonding interactions. In addition, some key residues (Arg14, Glu88, Cys90, Arg138, Asn139, Leu141, and Leu155) responsible for ligand-receptor binding are highlighted. All structural information obtained from 3D-QSAR models and molecular dynamics is consist with the available experimental activities. All the results will facilitate the optimization of this series of FAK inhibitors with higher inhibitory activities.     

Binding mode of inhibitors and Cryptosporidium parvum IMP dehydrogenase: A combined ligand- and receptor-based study

A combined ligand- and target-based approach was used to analyse the interaction models of Cryptosporidium parvum inosine 5’-monophosphate dehydrogenase (CpIMPDH) with selective inhibitors. First, a ligand-based pharmacophore model was generated from 20 NAD⁺ competitive CpIMPDH inhibitors with the HipHop module. The characteristic of the NAD⁺ binding site of CpIMPDH was then described, and the binding modes of the representative inhibitors were studied by molecular docking. The combination of the pharmacophore model and the docking results allowed us to evaluate the pharmacophore features and structural information of the NAD⁺ binding site of CpIMPDH. This research supports the proposal of an interaction model inside the NAD⁺ binding site of CpIMPDH, consisting of four key interaction points: two hydrophobic-aromatic groups, a hydrophobic-aliphatic group and a hydrogen bond donor. This study also provides guidance for the design of more potent CpIMPDH inhibitors for the treatment of Cryptosporidium infections.     

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 modelling,atom-based 3D-QSAR generation and virtual screening of molecules projected for mPGES-1 inhibitory activity

COX-2 inhibitors exhibit anticancer effects in various cancer models but due to the adverse side effects associated with these inhibitors, targeting molecules downstream of COX-2 (such as mPGES-1) has been suggested. Even after calls for mPGES-1 inhibitor design, to date there are only a few published inhibitors targeting the enzyme and displaying anticancer activity. In the present study, we have deployed both ligand and structure-based drug design approaches to hunt novel drug-like candidates as mPGES-1 inhibitors. Fifty-four compounds with tested mPGES-1 inhibitory value were used to develop a model with four pharmacophoric features. 3D-QSAR studies were undertaken to check the robustness of the model. Statistical parameters such as r² = 0.9924, q² = 0.5761 and F test = 1139.7 indicated significant predictive ability of the proposed model. Our QSAR model exhibits sites where a hydrogen bond donor, hydrophobic group and the aromatic ring can be substituted so as to enhance the efficacy of the inhibitor. Furthermore, we used our validated pharmacophore model as a three-dimensional query to screen the FDA-approved Lopac database. Finally, five compounds were selected as potent mPGES-1 inhibitors on the basis of their docking energy and pharmacokinetic properties such as ADME and Lipinski rule of five.     

Pharmacophore modeling, 3D-QSAR,docking, and molecular dynamics simulation on topoisomerase IV inhibitors of wild type Staphylococcus aureus

Staphylococcus aureus is a gram-positive bacterium. It is a foremost cause of skin and respiratory infections, endocarditis, osteomyelitis, Ritter’s disease, and bacteraemia. Topoisomerase enzyme is involved in preventing or correcting topological problems of overwinding or underwinding occurring in DNA before replication process. An exhaustive molecular modeling studies that includes pharmacophore modeling, ligand-based three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, molecular dynamics simulation, and ADME calculations were performed on isothiazoloquinolones derivatives which are reported as effective inhibitors against topoisomerase IV of wild type S. aureus. In pharmacophore modeling by using pharmacophore alignment and scoring engine (PHASE) a five-point model (AHHRR.3) was generated with existing compounds having statistical significant as correlation coefficient (R ² = 0.954), cross-validation coefficient (Q ² = 0.650), and F value of 130.5. Ligand-based 3D-QSAR study was applied using comparative molecular field analysis (CoMFA) with Q ² = 0.616, R ² = 0.989, and comparative molecular similarity indices analysis (CoMSIA) with Q ² = 0.510, R ² = 0.995. The predictive ability of this model was determined using a test set of molecules that gave acceptable predictive correlation (R ² Pred) values 0.55 and 0.56 for CoMFA and CoMSIA, respectively. Docking and molecular dynamic simulations were employed to position the inhibitors into protein active site to find out the most probable binding mode and most reliable conformations. Developed pharmacophore models and docking methods provide guidance to design enhanced activity molecules.

     

Identification of potential HIV-1 integrase strand transfer inhibitors: In silico virtual screening and QM/MM docking studies

HIV-1 integrase (IN) is a retroviral enzyme that catalyses integration of the reverse-transcribed viral DNA into the host genome, which is necessary for efficient viral replication. In this study, we have performed an in silico virtual screening for the identification of potential HIV-1 IN strand transfer (ST) inhibitors. Pharmacophore modelling and atom-based 3D-QSAR studies were carried out for a series of compounds belonging to 3-Hydroxypyrimidine-2,4-diones. Based on the ligand-based pharmacophore model, we obtained a five-point pharmacophore with two hydrogen bond acceptors (A), one hydrogen bond donor (D), one hydrophobic group (H) and one aromatic ring (R) as pharmacophoric features. The pharmacophore hypothesis AADHR was used as a 3D query in a sequential virtual screening study to filter small molecule databases Maybridge, ChemBridge and Asinex. Hits matching with pharmacophore hypothesis AADHR were retrieved and passed progressively through Lipinski’s rule of five filtering, molecular docking and hierarchical clustering. The five compounds with best hits with novel and diverse chemotypes were subjected to QM/MM docking, which showed improved docking accuracy. We further performed molecular dynamics simulation and found three compounds that form stable interactions with key residues. These compounds could be used as a leads for further drug development and rational design of HIV-1 IN inhibitors.     

Molecular dynamics and pharmacophore modelling studies of different subtype (ALK and EGFR (T790M)) inhibitors in NSCLC

Extensively validated 3D pharmacophore models for ALK (anaplastic lymphoma kinase) and EGFR (T790M) (epithelial growth factor receptor with acquired secondary mutation) were developed. The pharmacophore model for ALK (r² = 0.96, q² = 0.692) suggested that two hydrogen bond acceptors and three hydrophobic groups arranged in 3-D space are essential for the binding affinity of ALK inhibitors. Similarly, the pharmacophore model for EGFR (T790M) (r² = 0.92, q² = 0.72) suggested that the presence of a hydrogen bond acceptor, two hydrogen bond donors and a hydrophobic group plays vital role in binding of an inhibitor of EGFR (T790M). These pharmacophore models allowed searches for novel ALK and EGFR (T790M) dual inhibitors from multiconformer 3D databases (Asinex, Chembridge and Maybridge). Finally, the eight best hits were selected for molecular dynamics simulation, to study the stability of their complexes with both proteins and final binding orientations of these molecules. After molecular dynamics simulations, one hit has been predicted to possess good binding affinity for both ALK and EGFR (T790M), which can be further investigated for its experimental in-vitro/in-vivo activities.     

Molecular modelling studies on adamantane-based Ebola virus GP-1 inhibitors using docking,pharmacophore and 3D-QSAR

The pathogenic Ebola virus (EBOV) causes a potential health risk and global spread. To date, few drugs are available for the treatment of Ebola virus disease (EVD) that allow researchers to use computational methods for designing potential drugs. The developed PHASE-based common six-point pharmacophore hypothesis (AADHPR_1) showed the necessity of two hydrogen bond acceptor features, one hydrogen bond donor feature, one hydrophobic group feature, one positively ionizable and one aromatic ring feature for further designing. We developed best 3D-QSAR models with high regression coefficients for the training (r²>0.82) and test (Q²>0.5) sets for both atoms-based and field-based 3D-QSAR models. The molecule 1A-4 (docking score = –4.711 kcal/mol) was obtained as best docked (SP mode) on Ebola virus envelope glycoprotein (PDB ID-3CSY) as compared with the standards oseltamivir (docking score = –4.39 kcal/mol) and zanamivir (docking score = –3.392 kcal/mol). The obtained ZINC hit ZINC58935541 showed a good docking score of –4.892 kcal/mol. The ZINC58935541 molecule also showed a strong binding affinity towards the receptor cavity of Ebola virus envelope glycoprotein when simulated for 1.2 ns. The good QikProp parameters reflect the fact that this molecule, upon optimization into a lead, might become a good candidate for the treatment of EVD.     

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.     

Homology modeling and 3D–QSAR study of benzhydrylpiperazine δ opioid receptor agonists

The binding affinity of a series of benzhydrylpiperazine δ opioid receptor agonists were pooled and evaluated by using 3D-QSAR and homology modeling/molecular docking methods. Ligand-based CoMFA and CoMSIA 3D-QSAR analyses with 46 compounds were performed on benzhydrylpiperazine analogues by taking the most active compound BW373U86 as the template. The models were generated successfully with q² value of 0.508 and r² value of 0.964 for CoMFA, and q² value of 0.530 and r² value of 0.927 for CoMSIA. The predictive capabilities of the two models were validated on the test set with R²_pred value of 0.720 and 0.814, respectively. The CoMSIA model appeared to work better in this case. A homology model of active form of δ opioid receptor was established by Swiss-Model using a reported crystal structure of active μ opioid receptor as a template, and was further optimized using nanosecond scale molecular dynamics simulation. The most active compound BW373U86 was docked to the active site of δ opioid receptor and the lowest energy binding pose was then used to identify binding residues such as s Gln105, Lys108, Leu125, Asp128, Tyr129, Leu200, Met132, Met199, Lys214, Trp274, Ile277, Ile304 and Tyr308. The docking and 3D-QSAR results showed that hydrogen bond and hydrophobic interactions played major roles in ligand-receptor interactions. Our results highlight that an approach combining structure-based homology modeling/molecular docking and ligand-based 3D-QSAR methods could be useful in designing of new opioid receptor agonists.     

Pharmacophore modelling and electronic feature analysis of hydroxamic acid derivatives,the HIV integrase inhibitors

Hydroxamic acid derivatives with metal ion binding properties were collected from the literature to generate a pharmacophore and 3D-QSAR model for HIV strand transfer inhibition. The derived pharmacophore model (AAAHRR) recognizes both metal ion binding site and hydrophobic group. The QSAR model generated using this hypothesis expressed statistical significance (r ² = 0.971 for the training set and q ² = 0.913 for the test set). The ability of this pharmacophore model to retrieve other metal ion binding inhibitors was examined by screening the ChemBank database (ligandinfo) incorporated with 10 known strand transfer inhibitors. The studied favourable and unfavourable contours of chemical features (H-bond donor, acceptor and hydrophobic sites) revealed the role of hydrophobic substitution at the fluorobenzene ring and cyclization of the metal ion binding hydroxamic acid in effective integrase inhibition. Analysis of the frontier orbitals, HOMO and LUMO revealed that the nucleophilic / electrophilic interactions depend on the significant overlapping observed at the azaindole and hydroxamic acid groups. In essence, the generated pharmacophore model is competent enough to disclose the essential site-specific interactions involved in the inhibition of HIV integrase, and hence can be used in virtual screening to identify novel scaffolds as leads with increased anti-viral potency.     

6-氮杂吲唑类Pim-1激酶抑制剂的3D-QSAR、药效团模型研究和分子设计

Pim-1激酶通过作用于多种信号通路或靶点影响肿瘤的发生发展,近年来被认为是肿瘤治疗的良好靶标。本文采用SYBYL-X2.1.1软件中的Topomer CoMFA、GALAHAD模块建立计算机模型,研究39个基于6-氮杂吲唑环的Pim-1激酶抑制剂的三维定量构效关系及药效团特征元素。结果显示,Topomer CoMFA建模所得交叉验证系数(q²)和相关系数(r²)分别为0.756和0.951,结合外部验证表明此3D-QSAR模型具有较高预测能力及较好的统计学稳定性,同时,用等势图描述了R1、R2基团处立体场、静电场对活性的具体影响。药效团研究结果表明,含氢键受体的芳香杂环母核结构,以及侧链取代基中含有芳香杂环结构对化合物的活性贡献较大。最后根据上述模型信息新设计了15个Pim-1激酶抑制剂分子并完成活性预测及分子对接模式研究,其中4个分子的预测pIC₅₀高于建模分子中活性最好的化合物17,Surflex-Dock分析显示新设计分子均与Pim-1激酶形成较强氢键相互作用。基于6-氮杂吲唑环的Pim-1激酶抑制剂的3D-QS...     

Discovery and design of new PI3K inhibitors through pharmacophore-based virtual screening,molecular docking,and binding free energy analysis

Phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT)/the mammalian target of rapamycin (mTOR) signaling pathway is one of the sought after therapeutic target for treating human cancers. This pathway is often hyper activated in cancers. In the present study, pharmacophore-based virtual screening, molecular docking, and binding free energy calculations were performed on a series of quinoline derivatives which were reported to be effective against PI3Kα. A five-point pharmacophore hypothesis with one hydrogen bond acceptor (A), one hydrogen bond donor (D), one hydrophobic group (H), and two aromatic rings (R) was developed with acceptable R² and Q² values of 0.93 and 0.60 respectively. Eventually, common pharmacophore hypothesis-based screening was conducted against TOSLab, CPP, and ASINEX macrocylce databases, and potential hits were identified which were further subjected to rigorous docking process in order to screen out drug like molecules having crucial interactions with the target PI3Kα. Finally, binding free energy analysis was carried out for the top hits obtained from docking process. We also designed new 1, 3, 4-oxadiazole-based cyclic peptides by incorporating the structural features of the hits obtained from the above databases. Among the designed cyclic peptides, the cyclic peptide with tryptophan moiety showed good interactions and free binding energy values. On the whole, this study helped us in identifying new promising molecules as PI3Kα inhibitors which can be explored further to generate greater number of compounds with better pharmacokinetic properties.     

Docking-based 3D-QSAR study of pyridyl aminothiazole derivatives as checkpoint kinase 1 inhibitors

Checkpoint kinase 1 (Chk1) is a promising target for the design of novel anticancer agents. In the present work, molecular docking simulations and three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were performed on pyridyl aminothiazole derivatives as Chk1 inhibitors. AutoDock was used to determine the probable binding conformations of all the compounds inside the active site of Chk1. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were developed based on the docking conformations and alignments. The CoMFA model produced statistically significant results with a cross-validated correlation coefficient (q²) of 0.608 and a coefficient of determination (r²) of 0.972. The reliable CoMSIA model with q² of 0.662 and r² of 0.970 was obtained from the combination of steric, electrostatic and hydrogen bond acceptor fields. The predictive power of the models were assessed using an external test set of 14 compounds and showed reasonable external predictabilities (r²_pred) of 0.668 and 0.641 for CoMFA and CoMSIA models, respectively. The models were further evaluated by leave-ten-out cross-validation, bootstrapping and progressive scrambling analyses. The study provides valuable information about the key structural elements that are required in the rational design of potential drug candidates of this class of Chk1 inhibitors.     

Strategies to design pyrazolyl urea derivatives for p38 kinase inhibition: a molecular modeling study

The p38 protein kinase is a serine–threonine mitogen activated protein kinase, which plays an important role in inflammation and arthritis. A combined study of 3D-QSAR and molecular docking has been undertaken to explore the structural insights of pyrazolyl urea p38 kinase inhibitors. The 3D-QSAR studies involved comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA). The best CoMFA model was derived from the atom fit alignment with a cross-validated r ² (q ²) value of 0.516 and conventional r ² of 0.950, while the best CoMSIA model yielded a q ² of 0.455 and r ² of 0.979 (39 molecules in training set, 9 molecules in test set). The CoMFA and CoMSIA contour maps generated from these models provided inklings about the influence of interactive molecular fields in the space on the activity. GOLD, Sybyl (FlexX) and AutoDock docking protocols were exercised to explore the protein–inhibitor interactions. The integration of 3D-QSAR and molecular docking has proffered essential structural features of pyrazolyl urea inhibitors and also strategies to design new potent analogues with enhanced activity.     

Identification of potential influenza virus endonuclease inhibitors through virtual screening based on the 3D-QSAR model

Influenza endonucleases have appeared as an attractive target of antiviral therapy for influenza infection. With the purpose of designing a novel antiviral agent with enhanced biological activities against influenza endonuclease, a three-dimensional quantitative structure-activity relationships (3D-QSAR) model was generated based on 34 influenza endonuclease inhibitors. The comparative molecular similarity index analysis (CoMSIA) with a steric, electrostatic and hydrophobic (SEH) model showed the best correlative and predictive capability (q ² = 0.763, r ² = 0.969 and F = 174.785), which provided a pharmacophore composed of the electronegative moiety as well as the bulky hydrophobic group. The CoMSIA model was used as a pharmacophore query in the UNITY search of the ChemDiv compound library to give virtual active compounds. The 3D-QSAR model was then used to predict the activity of the selected compounds, which identified three compounds as the most likely inhibitor candidates.     

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

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

Molecular docking and 3D-QSAR study of pyranmycin derivatives against 16S rRNA A site

To understand pharmacophore properties of pyranmycin derivatives and to design novel inhibitors of 16S rRNA A site, comparative molecular field analysis (CoMFA) approach was applied to analyze three-dimensional quantitative structure–activity relationship (3D-QSAR) of 17 compounds. AutoDock 3.0.5 program was employed to locate the orientations and conformations of the inhibitors interacting with 16S rRNA A site. The interaction mode was demonstrated in the aspects of inhibitor conformation, hydrogen bonding and electrostatic interaction. Similar binding conformations of these inhibitors and good correlations between the calculated binding free energies and experimental biological activities suggest that the binding conformations of these inhibitors derived from docking procedure were reasonable. Robust and predictive 3D-QSAR model was obtained by CoMFA with q² values of 0.723 and 0.993 for cross-validated and non-cross-validated, respectively. The 3D-QSAR model built here will provide clear guidelines for novel inhibitors design based on the Pyranmycin derivatives against 16S rRNA A site.