Pharmacophore-based virtual screening and Bayesian model for the identification of potential human leukotriene A4 hydrolase inhibitors

N-alkylamides from Litsea cubeba (Lour.) Pers. with potential anti-inflammatory activity

Six amides, including a new N-alkylamide (1), four known N-alkylamides (2-5) and one nicotinamide (6) were isolated from Litsea cubeba (Lour.) Pers., which is a pioneer herb traditionally utilized in medicine. Their structures were elucidated on the basis of 1D and 2D NMR experiments and by comparison of their spectroscopic and physical data with the literature values. Cubebamide (1) is a new cinnamoyltyraminealkylamide and possessed obvious anti-inflammatory activity against NO production with IC50 values of 18.45 μM. Further in-depth pharmacophore-based virtual screening and molecular docking were carried out to reveal the binding mode of the active compound inside the 5-LOX enzyme. The results indicate that L. cubeba, and the isolated amides might be useful in the development of lead compounds for the prevention of inflammatory diseases.

Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure-activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

Efficacy coefficient method assisted quadruple-activities 3D-QSAR pharmacophore model for application in environmentally friendly PAE molecular modification

Phthalate acid esters (PAEs) are among the most widely used plasticizers in plastic products. They are easily diffused from plastic during use and seriously affect the environment and human health. Therefore, designing environmentally friendly PAE derivatives has important practical applications. In this paper, the environmentally friendly molecular modification of PAEs was carried out according to a comprehensive structural evaluation based on a three-dimensional quantitative structure-activity relationship (3D-QSAR) pharmacophore model of four activity modes. First, the efficacy coefficient method was used to process the mobility, toxicity, degradation and bioconcentration data of the PAEs to calculate comprehensive evaluation values. The PAE 3D-QSAR pharmacophore complex model was constructed based on the PAE four-activity comprehensive evaluation value (a comprehensive value representing the mobility, toxicity, degradation and bioconcentration of the PAEs), and a total of 4 PAE derivatives with reduced comprehensive evaluation values were obtained. Functional evaluation of the derivatives showed that the five PAEs with lower comprehensive evaluation values were stable in the environment, while the insulating properties of the derivative molecules were less affected. Following the four-activity pharmacophore model (Hypo 1) of the target molecules, dimethyl phthalate (DMP) and di-n-octyl phthalate (DNOP), comprehensive evaluation models and their mobility, toxicity, degradation and bioconcentration single-activity models, the substitution sites selected by the comprehensive evaluation model were demonstrated to be highly representative. By constructing a two-dimensional quantitative structure-activity relationship (2D-QSAR) model of the comprehensive evaluation values of the PAEs and the four single-effect 2D-QSAR models of their derivatives, the different effects of the five key parameters on the comprehensive evaluation values, toxicity, degradation, mobility and bioconcentration of molecules were analysed.

Computer-Driven Development of an in Silico Tool for Finding Selective Histone Deacetylase 1 Inhibitors

Histone deacetylases (HDACs) are a class of epigenetic modulators overexpressed in numerous types of cancers. Consequently, HDAC inhibitors (HDACIs) have emerged as promising antineoplastic agents. Unfortunately, the most developed HDACIs suffer from poor selectivity towards a specific isoform, limiting their clinical applicability. Among the isoforms, HDAC1 represents a crucial target for designing selective HDACIs, being aberrantly expressed in several malignancies. Accordingly, the development of a predictive in silico tool employing a large set of HDACIs (aminophenylbenzamide derivatives) is herein presented for the first time. Software Phase was used to derive a 3D-QSAR model, employing as alignment rule a common-features pharmacophore built on 20 highly active/selective HDAC1 inhibitors. The 3D-QSAR model was generated using 370 benzamide-based HDACIs, which yielded an excellent correlation coefficient value (R² = 0.958) and a satisfactory predictive power (Q² = 0.822; Q²_F3 = 0.894). The model was validated (r²_{ext_ts} = 0.794) using an external test set (113 compounds not used for generating the model), and by employing a decoys set and the receiver-operating characteristic (ROC) curve analysis, evaluating the Güner-Henry score (GH) and the enrichment factor (EF). The results confirmed a satisfactory predictive power of the 3D-QSAR model. This latter represents a useful filtering tool for screening large chemical databases, finding novel derivatives with improved HDAC1 inhibitory activity.

Detection Method of Environmentally Friendly Non-POP PBDEs by Derivatization-Enhanced Raman Spectroscopy Using the Pharmacophore Model

Background:

Polybrominated diphenyl ethers (PBDEs) are dangerous for the environment and human health because of their persistent organic pollutant (POP) characteristics, which have attracted extensive research attention. Raman spectroscopy is a simple highly sensitive detection operation. This study was performed to obtain environmentally friendly non-POP PBDE derivatives with simple detection-based molecular design and provide theoretical support for establishing enhanced Raman spectroscopic detection techniques.

Methods:

A three-dimensional quantitative structure-activity relationship (3DQSAR) pharmacophore model of characteristic PBDE Raman spectral was established using 20 and 10 PBDEs as training and test sets, respectively. Full-factor experimental design was used to modify representative commercial PBDEs, and their flame retardancy and POP characteristics were evaluated.

Results:

The pharmacophore model (Hypo1) exhibited good predictive ability with the largest correlation coefficient (R2) of 0.88, the smallest root mean square (RMS) value of 0.231, and total cost of 81.488 with a configuration value of 12.56 (˂17).74 monosubstituted and disubstituted PBDE derivatives were obtained based on the Hypo 1 pharmacophore model and full-factor experimental design auxiliary. Twenty PBDE derivatives were screened, and their flame-retardant capabilities were enhanced and their migration and bio-concentration were reduced (log(KOW) <5), with unchanged toxicity and high biodegradability. The Raman spectral intensities increased up to 380%. In addition, interference analysis of the Raman peaks by group frequency indicated that the 20 PBDE derivatives were easily detected with no interference in gaseous environments.

Conclusion:

Nine pharmacophore models were constructed in this study; Hypo 1 was the most accurate. Twenty PBDE derivatives showed Raman spectral intensities increased up to 380%; these were classified as new non-POP environmentally friendly flame retardants with low toxicity, low migration, good biodegradability, and low bio-concentrations. 2D QSAR analysis showed that the most positive Milliken charge and lowest occupied orbital energy were the main contributors to the PBDE Raman spectral intensities. Raman peak analysis revealed no interference between the derivatives in gaseous environments.

Investigation of novel chemical scaffolds targeting prolyl oligopeptidase for neurological therapeutics

Prolyl oligopeptidase (POP) is a potential therapeutic target for treatment of several neurological disorders and α-synucleinopathies including Parkinson's disease. Most of the known POP inhibitors failed in the clinical trials due to poor pharmacokinetic properties and blood-brain impermeability. Therefore, a training set of 30 structurally diverse compounds with a wide range of inhibitory activity against POP was used to generate a quantitative pharmacophore model, Hypo 3, to identify potential POP inhibitors with desirable drug-like properties. Validations through test set, cost analysis, and Fisher's randomization methods proved that Hypo 3 accurately predicted the known inhibitors among inactive compounds. Hypo 3 was employed as 3D query for virtual screening on an in-house drug-like chemical database containing compounds with good brain permeability and ADMET parameters. Database screening with Hypo 3 resulted in 99 compounds that were narrowed down to 21 compounds through molecular docking. Among them, five compounds were identified in our earlier studies, while two compounds showed in vitro POP inhibition. The current study proposed new 16 virtually screened compounds as potential inhibitors against POP that possess Gold docking score in the range of 64.61-75.74 and Chemscore of -32.25 to -38.35. Furthermore, the top scoring four hit compounds were subjected to molecular dynamics simulations to reveal their appropriate binding modes and assessing binding free energies. The hit compounds interacted with POP effectively via hydrogen bonds with important active site residues along with hydrophobic interactions. Moreover, the hit compounds had key inter-molecular interactions and better binding free energies as compared to the reference inhibitor. A potential new hydrogen bond interaction was discovered between Hit 2 with the Arg252 residue of POP. To conclude, we propose four hit compounds with new structural scaffolds against POP for the lead development of POP-based therapeutics for neurological disorders.

Computational Tool for Fast in silico Evaluation of hERG K+ Channel Affinity

The development of a novel comprehensive approach for the prediction of hERG activity is herein presented. Software Phase has been used to derive a 3D-QSAR model, employing as alignment rule a common pharmacophore built on a subset of 22 highly active compounds (threshold Ki: 50 nM) against hERG K⁺ channel. Five features comprised the pharmacophore: two aromatic rings (R₁ and R₂), one hydrogen-bond acceptor (A), one hydrophobic site (H), and one positive ionizable function (P). The sequential 3D-QSAR model developed with a set of 421 compounds (randomly divided in training and test set) yielded a test set (Q²) = 0.802 and proved to be predictive with respect to an external test set of 309 compounds that were not used to generate the model (rext_ts2 = 0.860). Furthermore, the model was submitted to an in silico validation for assessing the reliability of the approach, by applying a decoys set, evaluating the Güner and Henry score (GH) and the Enrichment Factor (EF), and by using the ROC curve analysis. The outcome demonstrated the high predictive power of the inclusive 3D-QSAR model developed for the hERG K⁺ channel blockers, confirming the fundamental validity of the chosen approach for obtaining a fast proprietary cardiotoxicity predictive tool to be employed for rationally designing compounds with reduced hERG K⁺ channel activity at the early steps of the drug discovery trajectory.

In silico study of subtilisin-like protease 1 (SUB1) from different Plasmodium species in complex with peptidyl-difluorostatones and characterization of potent pan-SUB1 inhibitors

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Homology models of four SUB1 orthologues from P. falciparum species were produced.

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We analyzed the binding mode of our previous difluorostatone inhibitors to six SUB1.

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In vitro activity of our difluorostatone-based inhibitors was correctly predicted.

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We derived a structure-based pan-SUB1 pharmacophore, and validated it in silico.

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We confirmed that development of pan-SUB1 inhibitors is a feasible task.

Plasmodium falciparum subtilisin-like protease 1 (SUB1) is a novel target for the development of innovative antimalarials. We recently described the first potent difluorostatone-based inhibitors of the enzyme ((4S)-(N-((N-acetyl-l-lysyl)-l-isoleucyl-l-threonyl-l-alanyl)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (1) and (4S)-(N-((N-acetyl-l-isoleucyl)-l-threonyl-l-alanylamino)-2,2-difluoro-3-oxo-4-aminopentanoyl)glycine (2)). As a continuation of our efforts towards the definition of the molecular determinants of enzyme-inhibitor interaction, we herein propose the first comprehensive computational investigation of the SUB1 catalytic core from six different Plasmodium species, using homology modeling and molecular docking approaches. Investigation of the differences in the binding sites as well as the interactions of our inhibitors 1,2 with all SUB1 orthologues, allowed us to highlight the structurally relevant regions of the enzyme that could be targeted for developing pan-SUB1 inhibitors. According to our in silico predictions, compounds 1,2 have been demonstrated to be potent inhibitors of SUB1 from all three major clinically relevant Plasmodium species (P. falciparum, P. vivax, and P. knowlesi). We next derived multiple structure-based pharmacophore models that were combined in an inclusive pan-SUB1 pharmacophore (SUB1-PHA). This latter was validated by applying in silico methods, showing that it may be useful for the future development of potent antimalarial agents.

Ligand-based pharmacophore modeling and Bayesian approaches to identify c-Src inhibitors

Abstract Cellular Src (c-Src) kinases play a critical role in cell adhesion, proliferation, angiogenesis and cancer. Ligand-based pharmacophore models, used to identify the critical chemical features of c-Src inhibitors, were generated and validated by training, test and decoy sets, respectively. Best pharmacophore model, Hypo1, consists of four features such as HBA, HBD, Hy-Ar and RA. Hypo1 was used in virtual screening of the chemical databases such as Maybridge, Chembridge and NCI. The sorted compounds by Hypo1 were further reduced by applying drug-like properties and ADMET. Totally, 85 compounds which showed the good drug-like properties were selected from three databases and subjected to molecular docking for refinement of the retrieved hits by analysing the suitable orientation of the compounds in the active site of c-Src. Finally, 18 compounds were selected based on consensus scoring and hydrogen bond interactions with critical amino acids such as Met341, Thr338, Glu339 or Asp404. In addition, the Bayesian model was generated from the training set to find suitable fragments for inhibition of the c-Src function. Based on the above finding, we suggested that the Hypo1 and the good fragments from the Bayesian model will be helpful to select the compounds from various databases to identify the novel and potent c-Src inhibitor.

Development of predictive quantitative structure–activity relationship model and its application in the discovery of human leukotriene A4 hydrolase inhibitors

Background: Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4. Results & discussion: The quantitative structure–activity relationship (QSAR) models were developed using genetic function approximation and validated. A training set of 26 diverse compounds and their molecular descriptors were used to develop highly correlating QSAR models. A six-descriptor model explaining the biological activity of the training and test sets with correlation values of 0.846 and 0.502, respectively, was selected as the best model and used in a database screening of drug-like Maybridge database followed by molecular docking. Conclusion: Based on the predicted potent inhibitory activities, expected binding mode and molecular interactions at the active site of hLTA4H final leads were selected as to be utilized in designing future hLTA4H inhibitors.

Structural origins for the loss of catalytic activities of bifunctional human LTA4H revealed through molecular dynamics simulations

Human leukotriene A4 hydrolase (hLTA4H), which is the final and rate-limiting enzyme of arachidonic acid pathway, converts the unstable epoxide LTA4 to a proinflammatory lipid mediator LTB4 through its hydrolase function. The LTA4H is a bi-functional enzyme that also exhibits aminopeptidase activity with a preference over arginyl tripeptides. Various mutations including E271Q, R563A, and K565A have completely or partially abolished both the functions of this enzyme. The crystal structures with these mutations have not shown any structural changes to address the loss of functions. Molecular dynamics simulations of LTA4 and tripeptide complex structures with functional mutations were performed to investigate the structural and conformation changes that scripts the observed differences in catalytic functions. The observed protein-ligand hydrogen bonds and distances between the important catalytic components have correlated well with the experimental results. This study also confirms based on the structural observation that E271 is very important for both the functions as it holds the catalytic metal ion at its location for the catalysis and it also acts as N-terminal recognition residue during peptide binding. The comparison of binding modes of substrates revealed the structural changes explaining the importance of R563 and K565 residues and the required alignment of substrate at the active site. The results of this study provide valuable information to be utilized in designing potent hLTA4H inhibitors as anti-inflammatory agents.

Molecular dynamics simulation study and hybrid pharmacophore model development in human LTA4H inhibitor design

Human leukotriene A4 hydrolase (hLTA4H) is a bi-functional enzyme catalyzes the hydrolase and aminopeptidase functions upon the fatty acid and peptide substrates, respectively, utilizing the same but overlapping binding site. Particularly the hydrolase function of this enzyme catalyzes the rate-limiting step of the leukotriene (LT) cascade that converts the LTA4 to LTB4. This product is a potent pro-inflammatory activator of inflammatory responses and thus blocking this conversion provides a valuable means to design anti-inflammatory agents. Four structurally very similar chemical compounds with highly different inhibitory profile towards the hydrolase function of hLTA4H were selected from the literature. Molecular dynamics (MD) simulations of the complexes of hLTA4H with these inhibitors were performed and the results have provided valuable information explaining the reasons for the differences in their biological activities. Binding mode analysis revealed that the additional thiophene moiety of most active inhibitor helps the pyrrolidine moiety to interact the most important R563 and K565 residues. The hLTA4H complexes with the most active compound and substrate were utilized in the development of hybrid pharmacophore models. These developed pharmacophore models were used in screening chemical databases in order to identify lead candidates to design potent hLTA4H inhibitors. Final evaluation based on molecular docking and electronic parameters has identified three compounds of diverse chemical scaffolds as potential leads to be used in novel and potent hLTA4H inhibitor design.

Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors

Mesenchymal epithelial transition factor (c-Met) is an attractive target for cancer therapy. Three-dimensional pharmacophore hypotheses were built based on a set of known structurally diverse c-Met inhibitors. The best pharmacophore model, which identified inhibitors with an associated correlation coefficient of 0.983 between their experimental and estimated IC(50) values, consisted of two hydrogen-bond acceptors, one hydrophobic, and one ring aromatic feature. The highly predictive power of the model was rigorously validated by test set prediction and Fischer's randomization method. The high values of enrichment factor and receiver operating characteristic (ROC) score indicated the model performed fairly well at distinguishing active from inactive compounds. The model was then applied to screen compound database for potential c-Met inhibitors. A filtering protocol, including druggability and molecular docking, were also applied in hits selection. The final 38 molecules, which exhibited good estimated activities, desired binding mode and favorable drug likeness were identified as potential c-Met inhibitors. Their novel backbone structures could be served as scaffolds for further study, which may facilitate the discovery and rational design of potent c-Met kinase inhibitors.

Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors

Background

Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.

Methods

Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.

Results

The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.

Conclusion

A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.

3D QSAR pharmacophore modeling, in silico screening, and density functional theory (DFT) approaches for identification of human chymase inhibitors

Human chymase is a very important target for the treatment of cardiovascular diseases. Using a series of theoretical methods like pharmacophore modeling, database screening, molecular docking and Density Functional Theory (DFT) calculations, an investigation for identification of novel chymase inhibitors, and to specify the key factors crucial for the binding and interaction between chymase and inhibitors is performed. A highly correlating (r = 0.942) pharmacophore model (Hypo1) with two hydrogen bond acceptors, and three hydrophobic aromatic features is generated. After successfully validating "Hypo1", it is further applied in database screening. Hit compounds are subjected to various drug-like filtrations and molecular docking studies. Finally, three structurally diverse compounds with high GOLD fitness scores and interactions with key active site amino acids are identified as potent chymase hits. Moreover, DFT study is performed which confirms very clear trends between electronic properties and inhibitory activity (IC(50)) data thus successfully validating "Hypo1" by DFT method. Therefore, this research exertion can be helpful in the development of new potent hits for chymase. In addition, the combinational use of docking, orbital energies and molecular electrostatic potential analysis is also demonstrated as a good endeavor to gain an insight into the interaction between chymase and inhibitors.