Imidazole-containing farnesyltransferase inhibitors: 3D quantitative structure–activity relationships and molecular docking

One of the most promising anticancer and recent antimalarial targets is the heterodimeric zinc-containing protein farnesyltransferase (FT). In this work, we studied a highly diverse series of 192 Abbott-initiated imidazole-containing compounds and their FT inhibitory activities using 3D-QSAR and docking, in order to gain understanding of the interaction of these inhibitors with FT to aid development of a rational strategy for further lead optimization. We report several highly significant and predictive CoMFA and CoMSIA models. The best model, composed of CoMFA steric and electrostatic fields combined with CoMSIA hydrophobic and H-bond acceptor fields, had r ² = 0.878, q ² = 0.630, and r _pred² = 0.614. Docking studies on the statistical outliers revealed that some of them had a different binding mode in the FT active site based on steric bulk and available active site space, explaining why the predicted activities differed from the experimental activities. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.     

An in silico approach to design peptide mimetics based on docking and molecular dynamics simulation of EGFR–matuzumab complex

Epidermal growth factor receptor (EGFR) plays an essential role in anticancer therapy. Matuzumab is an antibody for the treatment of colorectal, lung and stomach cancer. Matuzumab binds efficiently to EGFR and blocks its phosphorylation. The recent clinical successes have established application of peptides for cancer treatment. The present contribution introduces an in silico approach to design peptides based on molecular dynamics simulation (MDs) of the matuzumab-EGFR complex in water environment. Moreover, principal component analysis has been used to select multiple conformations of the complex in MDs for designing the peptides. The paratope and epitope in each conformation of the complex were determined, and the alanine scanning was used to identify the hot spots of EGFR conformers. The conformations of the peptides were optimized using PEP-FOLD server and MDs. The selected conformations were analyzed in a docking study to realize the binding site of the EGFR. Finally, pharmokinetics properties of the peptides were calculated. The designed oligopeptides were EWRSYYYWH, YYYWHNEWN, YYYWHNEWS and HNHSRNYGS with a higher affinity to the EGFR relative to the previously reported peptides. The newly designed peptides were investigated for their in vivo toxicities on rats, and all of them were non-toxic.     

Predicting toxicity of benzene derivatives by molecular hologram derived quantitative structure–activity relationships (QSARS)

Holographic quantitative structure–activity relationship (HQSAR) is an emerging QSAR technique with the combined application of molecular hologram, which encodes the frequency of occurrence of various molecular fragment types, and the subsequent partial least squares (PLS) regression analysis. Based on molecular hologram, alignment-free QSAR models could be rapidly and easily developed with highly statistical significance and predictive ability. In this paper, the toxicity data for a series of 83 benzene derivatives to the autotrophic Chlorella vulgaris (IGC⁵⁰, negative logarithmic form of 6-h 50% population growth inhibition concentration in mmol/l) were subjected to HQSAR analysis and this resulted in a model with a high predictive ability. The robustness and predictive ability of the model were validated by “leave-one-out” (LOO) cross-validation procedure and an external testing set. The influence of fragment distinction parameters and fragment size on the quality of the HQSAR model have been also discussed.     

In silico exploratory study using structure–activity relationship models and metabolic information for prediction of mutagenicity based on the Ames test and rodent micronucleus assay

The mutagenic potential of chemicals is a cause of growing concern, due to the possible impact on human health. In this paper we have developed a knowledge-based approach, combining information from structure–activity relationship (SAR) and metabolic triggers generated from the metabolic fate of chemicals in biological systems for prediction of mutagenicity in vitro based on the Ames test and in vivo based on the rodent micronucleus assay. In the first part of the work, a model was developed, which comprises newly generated SAR rules and a set of metabolic triggers. These SAR rules and metabolic triggers were further externally validated to predict mutagenicity in vitro, with metabolic triggers being used only to predict mutagenicity of chemicals, which were predicted unknown, by SARpy. Hence, this model has a higher accuracy than the SAR model, with an accuracy of 89% for the training set and 75% for the external validation set. Subsequently, the results of the second part of this work enlist a set of metabolic triggers for prediction of mutagenicity in vivo, based on the rodent micronucleus assay. Finally, the results of the third part enlist a list of metabolic triggers to find similarities and differences in the mutagenic response of chemicals in vitro and in vivo.     

Design of new CD38 inhibitors based on CoMFA modelling and molecular docking analysis of 4‑amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides

In this study, based on molecular docking analysis and comparative molecular field analysis (CoMFA) modelling of a series of 71 CD38 inhibitors including 4?amino-8-quinoline carboxamides and 2,4-diamino-8-quinazoline carboxamides, new CD38 inhibitors were designed. The interactions of the molecules with the greatest and the lowest activities with the nicotinamide mononucleotide (NMN) binding site were investigated by molecular docking analysis. A CoMFA model with four partial least squares regression (PLSR) components was developed to predict the CD38 inhibitory activity of the molecules. The r² values for the training and test sets were 0.89 and 0.82, respectively. The Q² values for leave-one-out cross-validation (LOO-CV) and leave-many-out cross-validation (LMO-CV) tests on the training set were 0.65 and 0.64, respectively. The CoMFA model was validated by calculating several statistical parameters. CoMFA contour maps were interpreted, and structural features that influence the CD38 inhibitory activity of molecules were determined. Finally, seven new CD38 inhibitors with greater activity with respect to the greatest active molecules were designed.     

Insight into crucial inhibitor–enzyme interaction of arylamides as novel direct inhibitors of the enoyl ACP reductase (InhA) from Mycobacterium tuberculosis: computer-aided molecular design

Abstract  

The enoyl ACP reductase enzyme (InhA) involved in the type II fatty acid biosynthesis pathway of Mycobacterium tuberculosis is an attractive target enzyme for antitubercular drug development. Arylamide derivatives are a novel class of InhA inhibitors used to overcome the drug-resistance problem of isoniazid, the frontline drug for tuberculosis treatment. Their remarkable property of inhibiting the InhA enzyme directly without requiring any coenzyme, makes them especially appropriate for the design of new antibacterials. In order to find a sound binding conformation for the different arylamide analogs, molecular docking experiments were performed with subsequent QSAR investigations. The X-ray conformation of one arylamide within its cocrystallized complex with InhA was used as a starting conformation for the docking experiments. The results thus obtained are perfectly consistent (rmsd = 0.73 ?) with the results from X-ray analysis. A thorough investigation of the arylamide binding modes with InhA provided ample information about structural requirements for appropriate inhibitor–enzyme interactions. Three different QSAR models were established using two three-dimensional (CoMFA and CoMSIA) and one two-dimensional (HQSAR) techniques. With statistically ensured models, the QSAR results obtained had high correlation coefficients between molecular structure properties of 28 arylamide derivatives and their biological activity. Molecular fragment contributions to the biological activity of arylamides could be obtained from the HQSAR model. Finally, a graphic interpretation designed in different contour maps provided coincident information about the ligand–receptor interaction thus offering guidelines for syntheses of novel analogs with enhanced biological activity.     

First cupracarborane commo-clusters based on the medium-cage nido-5,6-C2B8H12-carborane and molecular structure of [Ph3PEt][commo-9,9′-Cu(nido-7,8-C2B8H11)2]

A reaction of anhydrous CuCl₂ with Na salts of the medium-cage carborane [7-X-nido-5,6-C₂B₈H₁₀]^?(X = H or I) derivatives in THF leads to new cupracarborane commo-clusters, [commo-9,9′-Cu(nido-7,8-C₂B₈H₁₁)₂]^? and [commo-9,9′-Cu(11-I-nido-7,8-C₂B₈H₁₀)₂]^?, in moderate yields. The clusters were isolated as stable [Ph₃PEt]⁺ salts and characterized by 1H, ³¹P{¹H}, and ¹¹B/¹¹B{¹H} NMR spectroscopy and X-ray crystallography (for the unsubstituted derivative). The use in this reaction of the reducing agent Na₂SO₃ considerably increases the yields of both complexes from 25 and 18% to 74 and 68%, respectively.     

Electronic structure and molecular properties of [Re6−x Os x Se8Cl6](4−x)− (x = 0–3) clusters: A study based on time-dependent density functional theory including spin-orbit and solvent effects

Relativistic time-dependent density functional (TDDFT) calculations including spin-orbit interactions via the zero order regular approximation (ZORA) and solvent effects are carried out on the [Re_6?x Os _x Se₈Cl₆]^(4?x)? (x = 0–3) cluster. These calculations indicate that the lowest energy electronic transitions of the MMCT and LMCT type are similar to those observed in strongly luminescent 24-electron hexanuclear rhenium chalcogenide clusters [Re₆Se₈Cl₆]^4?. Thus our calculations predict that [Re_6?x Os _x Se₈Cl₆]^(4?x)? (x = 0–3) clusters could be luminescent.