Insight into molecular mechanism underlying the transesterification catalysed by penicillin G amidase (PGA) using a combination protocol of experimental assay and theoretical analysis

Penicillin G amidase (PGA) is one of the most recognised biocatalysts because of its critical application in the antibiotic industry. Herein, the additive effects involved in transesterification catalysed by PGA are explored in detail using a combination of experimental analysis and theoretical modelling. The transesterification ability of PGA is experimentally determined with 17 N-containing compounds as additives, and, on this basis, a series of quantitative structure–activity relationship (QSAR) models are developed from various physicochemical parameters characterising structural variation over the additives. The resulting models exhibit both good stability and predictive power, from which five most important properties that highlight structural basis and reaction mechanism underlying the transesterification are extracted, revealing that the topological property and electrostatic profile of additives exert a significant effect on reaction yield; the charge distribution around additive molecules is the most significant factor controlling reaction yield, and then the topological structure. Furthermore, it is inferred that the additive imidazole might constitute the catalytic triad of Ser, Glu or Asp involved in PGA active site, which appears similar to lipase, rendering PGA with the catalytic ability of transesterfication. The study highlights the potential application of QSAR methodology in the field of enzymatic regulator design.     

Design of novel quinazolinone derivatives as inhibitors for 5HT7 receptor

To study the pharmacophore properties of quinazolinone derivatives as 5HT₇ inhibitors, 3D QSAR methodologies, namely Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied, partial least square (PLS) analysis was performed and QSAR models were generated. The derived model showed good statistical reliability in terms of predicting the 5HT₇ inhibitory activity of the quinazolione derivative, based on molecular property fields like steric, electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor fields. This is evident from statistical parameters like q² (cross validated correlation coefficient) of 0.642, 0.602 and r² (conventional correlation coefficient) of 0.937, 0.908 for CoMFA and CoMSIA respectively. The predictive ability of the models to determine 5HT₇ antagonistic activity is validated using a test set of 26 molecules that were not included in the training set and the predictive r² obtained for the test set was 0.512 & 0.541. Further, the results of the derived model are illustrated by means of contour maps, which give an insight into the interaction of the drug with the receptor. The molecular fields so obtained served as the basis for the design of twenty new ligands. In addition, ADME (Adsorption, Distribution, Metabolism and Elimination) have been calculated in order to predict the relevant pharmaceutical properties, and the results are in conformity with required drug like properties.     

Models of the pharmacophoric pattern and affinity trend of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate derivatives as σ1 ligands

Sigma-1 (σ₁) affinities of methyl 2-(aminomethyl)-1-phenylcyclopropane-1-carboxylate (MAPCC) derivatives were modelled by the genetic algorithm with linear assignment of hypermolecular alignment of datasets (GALAHAD) and the comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) methods. GALAHAD was used for deriving the 3D pharmacophore pattern that encompasses the most potent σ₁ ligands within this series. Five MAPCC derivatives with a high σ₁ affinity were used for deriving this model. The obtained model included a nitrogen atom, the hydrophobes and the hydrogen bond acceptor features; it was able to identify other potent σ₁ ligands. On the other hand, CoMFA and CoMSIA methods were used for deriving quantitative structure–activity relationship (QSAR) models. All QSAR models were trained with 17 compounds, after which they were evaluated for predictive ability with additional five compounds. The best QSAR model was obtained by using CoMSIA, including steric, electrostatic and hydrophobic fields, and had a good predictive quality according to both internal and external validation criteria. In general, the models described herein provide meaningful information relevant for the rational design of new σ₁ ligands.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

Structure–activity relationship study of antioxidative peptides by QSAR modeling: the amino acid next to C‐terminus affects the activity

Screening, isolation and in vitro assays have been used for characterization of antioxidative peptides derived from food proteins, and incompatible deductions of structural characteristics derived from the isolated peptides have been brought forward. However, there is still little information concerning the structure‐activity relationship of antioxidative peptides. QSAR modeling was performed, respectively, on synthetic tripeptides and tetrapeptides related to LLPHH. According to cumulative squared multiple correlation coefficients (R²), cumulative cross‐validation coefficients (Q²) and relative standard deviation for calibration set (RSD_c), two credible models for tripeptide and tetrapeptide databases, respectively, have been built with partial least squares (PLS) regression (R² for models of tripeptide and tetrapeptide are 0.744 and 0.943, Q² are 0.631 and 0.414, and RSD_c are 0.323 and 0.111, respectively). Meanwhile, according to the cumulative multiple correlation coefficient for the predictive set ($R_{\rm {ext}}^{2}$ ) and the relative standard deviation for the predictive set (RSD_p), the predictive ability of the model for tripeptides also is excellent ($R_{\rm {ext}}^{2}$ and RSD_p are 0.719 and 0.450, respectively). Hydrogen bond property and hydrophilicity of the amino acid residue next to the C‐terminus, and the hydrophobicity as well as electronic propertyof the N‐terminus are more significant; meanwhile, the electronic property of the C‐terminus is beneficial for antioxidant activity. The structural characteristics we found are very useful in understanding and predicting the peptide structures responsible for activity and development of functional foods with peptides as active compounds, or antioxidative peptides as alternatives to other antioxidants. Copyright © 2011 European Peptide Society and John Wiley & Sons, Ltd.     

Design,synthesis and antimalarial activity of novel bis{N-[(pyrrolo[1,2-a]quinoxalin-4-yl)benzyl]-3-aminopropyl}amine derivatives

Novel series of bis- and tris-pyrrolo[1,2-a]quinoxaline derivatives 1 were synthesized and tested for in vitro activity upon the intraerythrocytic stage of W2 and 3D7 Plasmodium falciparum strains. Biological results showed good antimalarial activity with IC₅₀ in the μM range. In attempting to investigate the large broad-spectrum antiprotozoal activities of these new derivatives, their properties toward Leishmania donovani were also investigated and revealed their selective antiplasmodial profile. In parallel, the in vitro cytotoxicity of these molecules was assessed on the human HepG2 cell line. Structure–activity relationships of these new synthetic compounds are discussed here. The bis-pyrrolo[1,2-a]quinoxalines 1n and 1p were identified as the most potent antimalarial candidates with selectivity index (SI) of 40.6 on W2 strain, and 39.25 on 3D7 strain, respectively. As the telomeres of the parasite could constitute an attractive target, we investigated the possibility of targeting Plasmodium telomeres by stabilizing the Plasmodium telomeric G-quadruplexes through a FRET melting assay by our new compounds.     

Application of quantitative structure‐activity relationship analysis on an antibody and alternariol‐like compounds interaction study

The antigen‐antibody interaction determines the sensitivity and specificity of competitive immunoassay for hapten detection. In this paper, the specificity of a monoclonal antibody against alternariol‐like compounds was evaluated through indirect competitive ELISA. The results showed that the antibody had cross‐reactivity with 33 compounds with the binding affinity (expressed by IC₅₀) ranging from 9.4 ng/mL to 12.0 μg/mL. All the 33 compounds contained a common moiety and similar substituents. To understand how this common moiety and substituents affected the recognition ability of the antibody, a three‐dimensional quantitative structure‐activity relationship (3D‐QSAR) between the antibody and the 33 alternariol‐like compounds was constructed using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methods. The q² values of the CoMFA and CoMSIA models were 0.785 and 0.782, respectively, and the r² values were 0.911 and 0.988, respectively, indicating that the models had good predictive ability. The results of 3D‐QSAR showed that the most important factor affecting antibody recognition was the hydrogen bond mainly formed by the hydroxyl group of alternariol, followed by the hydrophobic force mainly formed by the methyl group. This study provides a reference for the design of new hapten and the mechanisms for antibody recognition.     

Prediction of HIV-1 protease inhibitory activity of 4-hydroxy-5,6-dihydropyran-2-ones: QSAR study

Inhibition of human immunodeficiency virus 1 (HIV-1) protease is an important strategy for the treatment of HIV and acquired immune deficiency syndrome (AIDS). Therefore, HIV-1 protease inhibitory activity of dihydropyranone derivatives has been analyzed with different physico-chemical parameters. In the present work, QSAR studies were performed on a series of 4-hydroxy-5,6-dihydropyran-2-ones to explore the physico-chemical parameters responsible for their HIV-1 protease inhibitory activity. Physico-chemical parameters were calculated using WIN CAChe 6.1. Stepwise multiple linear regression analysis was performed to derive QSAR models which were further evaluated for statistical significance and predictive power by internal and external validation. The selected best QSAR model was having correlation coefficient (R)?=?0.875 and cross-validated squared correlation coefficient (Q²)?=?0.707. The developed significant QSAR model indicates that hydrophobicity of whole molecule and the substituent present at sixth position of dihydropyranones play an important role in the HIV-1 protease inhibitory activities of 4-hydroxy-5,6-dihydropyran-2-ones.     

On the relationship of anthranilic derivatives structure and the FXR (Farnesoid X receptor) agonist activity

Farnesoid X receptor (FXR) is a nuclear receptor related to lipid and glucose homeostasis and is considered an important molecular target to treatment of metabolic diseases as diabetes, dyslipidemia, and liver cancer. Nowadays, there are several FXR agonists reported in the literature and some of it in clinical trials for liver disorders. Herein, a compound series was employed to generate QSAR models to better understand the structural basis for FXR activation by anthranilic acid derivatives (AADs). Furthermore, here we evaluate the inclusion of the standard deviation (SD) of EC₅₀ values in QSAR models quality. Comparison between the use of experimental variance plus average values in model construction with the standard method of model generation that considers only the average values was performed. 2D and 3D QSAR models based on the AAD data set including SD values showed similar molecular interpretation maps and quality (Q²_LOO, Q²_(F2), and Q²_(F3)), when compared to models based only on average values. SD-based models revealed more accurate predictions for the set of test compounds, with lower mean absolute error indices as well as more residuals near zero. Additionally, the visual interpretation of different QSAR approaches agrees with experimental data, highlighting key elements for understanding the biological activity of AADs. The approach using standard deviation values may offer new possibilities for generating more accurate QSAR models based on available experimental data.     

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis – a case study on HIV-1 protease inhibitors

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r_loo² values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r² values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r_pred²) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r_pred² of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.     

In silico design of potent EGFR kinase inhibitors using combinatorial libraries

This paper is an attempt to design 4-anilinoquinazoline compounds having promising anticancer activities against epidermal growth factor (EGFR) kinase inhibition, using virtual combinatorial library approach. Partial least squares method has been applied for the development of a quantitative structure–activity relationship (QSAR) model based on training and test set approaches. The partial least squares model showed some interesting results in terms of internal and external predictability against EGFR kinase inhibition for such type of anilinoquinazoline derivatives. In virtual screening study, out of 4860 compounds in chemical library, 158 compounds were screened and finally, 10 compounds were selected as promising EGFR kinase inhibitors based on their predicted activities from the QSAR model. These derivatives were subjected to molecular docking study to investigate the mode of binding with the EGFR kinase, and the two compounds (ID 3639 and 3399) showing similar type of docking score and binding patterns with that of the existing drug molecules like erlotinib were finally reported.     

Synthesis,Characterization, Antimicrobial and Antimalarial Activities of Azines Based Schiff Bases and their Pd(II) Complexes

Herein, we report synthesis, characterization, antimicrobial and antimalarial activities of azines Schiff base ligands (L¹−L⁴) and their palladium (II) complexes ( C¹−C⁴ ) of [Pd(L)(OAc)₂] type. The azine ligands (L¹−L⁴) were prepared by condensation of carbonyl compounds with hydrazine hydrate and their complexes by the reaction of palladium acetate with L¹−L⁴ ligands in 1 : 1 molar ratio. The prepared ligands and their complexes were characterized by spectral characterization using ¹H &¹³C-NMR, FT-IR and mass spectral studies, which revealed that the ligands coordinates via azomethine nitrogen and heteroatom or aryl carbon with palladium. Moreover, Schiff bases and their palladium (II) complexes have been screened for their antibacterial (S. aureus, B. subtillis, and S. typhi, P. aeruginosa), antifungal (C. albicans, A. niger, and A. clavatus) and antimalarial (P. falciparum) activities. The Schiff base L⁴ showed good results for antibacterial against S. aureus (MIC, 50 μg/mL) and antimalarial against P. falciparum (IC₅₀, 0.83 μg/mL). The complex C¹ showed best antibacterial activity (MIC, 62.5 μg/mL) against S. typhi and the complex C⁴ exhibited remarkable antimalarial activity (IC₅₀, 0.42 μg/mL) among the tested compounds. Thus, azines based ligands and their Pd complexes can be good antimicrobial and antimalarial agents if explored further.