First report on exploring structural requirements of alpha and beta thymidine analogs for PfTMPK inhibitory activity using in silico studies

With the emergence of multi-drug resistance of the currently available antimalarial drugs including the “magic bullet” artemisinin derivatives in the market, there is an urgent need for discovery and development of new potent antimalarial molecules. The present work deals with quantitative structure–activity relationship (QSAR) modeling, pharmacophore mapping and docking studies of a series of 35 thymidine analogs as inhibitors of Plasmodium falciparum thymidylate kinase (PfTMPK), an enzyme that catalyzes phosphorylation of thymidine monophosphate (TMP) to thymidine diphosphate (TDP). The models were validated both internally and externally and significant statistical results were obtained, indicating the robustness and reliability of the developed models. The docking study was performed using the LigandFit option of receptor–ligand interactions protocol section available in Discovery Studio 2.1 where lower RMSD values (0.6931 Å) between the co-crystallized ligand and re-docked ligand assured that the ligand was bound in the same binding pocket. The QSAR, pharmacophore mapping and docking studies provide an understanding of important structural requirements or essential molecular properties, or features of molecules, and important binding interactions, and provide an important guidance for the chemist to synthesis of new molecules with improved PfTMPK inhibitory activity profile. This work revealed the importance of –NH-fragment, electrophilicity of the molecules and the number of oxygen atom towards the PfTMPK inhibitory activity of the molecules. To the best of our knowledge, this work presents the first QSAR and pharmacophore report for thymidine analogs which may serve as an efficient tool for the design and synthesis of potent molecules as PfTMPK inhibitors to address the increasing threat of multi-drug resistance against P. falciparum.     

aug-MIA-QSAR modeling of antimicrobial activities and design of multi-target anilide derivatives

The antibacterial activity against Bacillus subtilis, Staphylococcus aureus and Escherichia coli, as well as the antifungal activity against Aspergillus niger of a series of anilide derivatives have been modeled using augmented multivariate image analysis applied to quantitative structure–activity relationship (aug-MIA-QSAR). This QSAR approach is based on 2D molecular shape, as well as atomic sizes and colors to encode chemical, physical and biological properties. Predictive models with r² from 0.65 to 0.83 were used to estimate the antimicrobial activities of novel anilide analogs, which were built from the combination of substructures of the most active antimicrobial compounds along the series. Given the synergistic effect of different substituents to provide new molecules, promising compounds were proposed, highlighting a considerable multi-antimicrobial activity.     

Molecular and statistical modeling of reduction peak potential and lipophilicity of platinum(IV) complexes

We report the results of the quantitative structure–property relationship analysis of 31 Pt(IV) complexes, for three of which the synthesis is reported for the first time. The X-ray structural analysis of one complex of the series was performed to demonstrate that the PM6 semiempirical method satisfactorily reproduces key features of the geometry of the complexes investigated. Molecular properties extracted from such calculations were then used to construct models of experimental data such as electrochemical peak potentials (evaluated by cyclic voltammetry) and the octanol–water partition coefficient (evaluated by a reversed-phase high performance liquid chromatography method), which are key aspects in the design of such Pt(IV) complexes as potential anticancer prodrugs. Statistically accurate models for both properties were found using combinations of surface areas, orbital energies, dipole moments, and atomic partial charges. These models could form the basis of virtual screening of potential drug molecules, allowing the prediction of properties, closely related to the antiproliferative activity of Pt(IV) complexes, directly from calculated data.     

Evaluation of Antimicrobial Activity of New Mastoparan Derivatives Using QSAR and Computational Mutagenesis

Antimicrobial peptides, also called body defense peptides, are used against a wide range of pathogens, such as negative- and positive-gram bacteria, mycobacteria, fungi, viruses, etc. Contrary to antibiotics, antimicrobial peptides do not develop resistance. Their wide antimicrobial spectrum situates them as important and attractive targets in research and pharmaceutical industry in order to obtain new structures using modern drug design techniques. We present here eleven QSAR models in which antimicrobial activity expressed as minimal inhibitory concentration values at Bacillus subtilis of 37 mastoparan analogs was correlated with different physicochemical parameters like: number of hydrophobic centers, molecular area and volume, internal dipole moment, refractivity, RPCG (relative positive charges) and number of donor and acceptor atoms generating by use of the computational software Sybyl. Significant R² (0.68–0.72) correlation coefficients and standard error of prediction SEE (0.199–0.230) were obtained, indicating that the established equations can be used. Thus, these linear models allowed us to create a library of 19 derivatives of mastoparan analogs obtained through computational mutagenesis. We propose this library of compounds as a source of possible derivatives with a more potent antimicrobial activity.     

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)     

A Method for Structure–Activity Analysis of Quorum-Sensing Signaling Peptides from Naturally Transformable Streptococci

Many species of streptococci secrete and use a competence-stimulating peptide (CSP) to initiate quorum sensing for induction of genetic competence, bacteriocin production, and other activities. These signaling molecules are small, unmodified peptides that induce powerful strain-specific activity at nano-molar concentrations. This feature has provided an excellent opportunity to explore their structure–function relationships. However, CSP variants have also been identified in many species, and each specifically activates its cognate receptor. How such minor changes dramatically affect the specificity of these peptides remains unclear. Structure–activity analysis of these peptides may provide clues for understanding the specificity of signaling peptide–receptor interactions. Here, we use the Streptococcus mutans CSP as an example to describe methods of analyzing its structure–activity relationship. The methods described here may provide a platform for studying quorum-sensing signaling peptides of other naturally transformable streptococci.     

The analysis of structure-anticancer and antiviral activity relationships for macrocyclic pyridinophanes and their analogues on the basis of 4D QSAR models (simplex representation of molecular structure)

A new 4D-QSAR approach has been considered. For all investigated molecules the 3D structural models have been created and the set of conformers (fourth dimension) have been used. Each conformer is represented as a system of different simplexes (tetratomic fragments of fixed structure, chirality and symmetry). The investigation of influence of molecular structure of macrocyclic pyridinophanes, their analogues and certain other compounds on anticancer and antiviral (anti-influenza, antiherpes and antiadenovirus) activity has been carried out by means of the 4D-QSAR. Statistic characteristics for QSAR of PLS (partial least squares) models are satisfactory (R = 0.92-0.97; CVR = 0.63-0.83). Molecular fragments increasing and decreasing biological activity were defined. This information may be useful for design, and direct synthesis of novel anticancer and antiviral agents.     

Probing the structural requirements of A-type Aurora kinase inhibitors using 3D-QSAR and molecular docking analysis

Aurora-A, the most widely studied isoform of Aurora kinase overexpressed aberrantly in a wide variety of tumors, has been implicated in early mitotic entry, degradation of natural tumor suppressor p53 and centrosome maturation and separation; hence, potent inhibitors of Aurora-A may be therapeutically useful drugs in the treatment of various forms of cancer. Here, we report an in silico study on a group of 220 reported Aurora-A inhibitors with six different substructures. Three-dimensional quantitative structure–activity relationship (3D-QSAR) studies were carried out using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) techniques on this series of molecules. The resultant optimum 3D-QSAR models exhibited an r _cv² value of 0.404-0.582 and their predictive ability was validated using an independent test set, ending in r _pred² 0.512-0.985. In addition, docking studies were employed to explore these protein–inhibitor interactions at the molecular level. The results of 3D-QSAR and docking analyses validated each other, and the key structural requirements affecting Aurora-A inhibitory activities, and the influential amino acids involved were identified. To the best of our knowledge, this is the first report on 3D-QSAR modeling of Aurora-A inhibitors, and the results can be used to accurately predict the binding affinity of related analogues and also facilitate the rational design of novel inhibitors with more potent biological activities.     

Computational design and structure–property relationship studies on heptazines

This study aimed to design novel nitrogen-rich heptazine derivatives as high energy density materials (HEDM) by exploiting systematic structure–property relationships. Molecular structures with diverse energetic substituents at varying positions in the basic heptazine ring were designed. Density functional techniques were used for prediction of gas phase heat of formation by employing an isodesmic approach, while crystal density was assessed by packing calculations. The results reveal that nitro derivatives of heptazine possess a high heat of formation and further enhancement was achieved by the substitution of nitro heterocycles. The crystal packing density of the designed compounds varied from 1.8 to 2 g cm⁻³, and hence, of all the designed molecules, nitro derivatives of heptazine exhibit better energetic performance characteristics in terms of detonation velocity and pressure. The calculated band gap of the designed molecules was analyzed to establish sensitivity correlations, and the results reveal that, in general, amino derivatives possess better insensitivity characteristics. The overall performance of the designed compounds was moderate, and such compounds may find potential applications in gas generators and smoke-free pyrotechnic fuels as they are rich in nitrogen content.     

Noscapinoids with anti-cancer activity against human acute lymphoblastic leukemia cells (CEM): a three dimensional chemical space pharmacophore modeling and electronic feature analysis

We have identified a new class of microtubule-binding compounds—noscapinoids—that alter microtubule dynamics at stoichiometric concentrations without affecting tubulin polymer mass. Noscapinoids show great promise as chemotherapeutic agents for the treatment of human cancers. To investigate the structural determinants of noscapinoids responsible for anti-cancer activity, we tested 36 structurally diverse noscapinoids in human acute lymphoblastic leukemia cells (CEM). The IC₅₀ values of these noscapinoids vary from 1.2 to 56.0 μM. Pharmacophore models of anti-cancer activity were generated that identify two hydrogen bond acceptors, two aromatic rings, two hydrophobic groups, and one positively charged group as essential structural features. Additionally, an atom-based quantitative structure–activity relationship (QSAR) model was developed that gave a statistically satisfying result (R ² = 0.912, Q ² = 0.908, Pearson R = 0.951) and effectively predicts the anti-cancer activity of training and test set compounds. The pharmacophore model presented here is well supported by electronic property analysis using density functional theory at B3LYP/3-21*G level. Molecular electrostatic potential, particularly localization of negative potential near oxygen atoms of the dimethoxy isobenzofuranone ring of active compounds, matched the hydrogen bond acceptor feature of the generated pharmacophore. Our results further reveal that all active compounds have smaller lowest unoccupied molecular orbital (LUMO) energies concentrated over the dimethoxy isobenzofuranone ring, azido group, and nitro group, which is indicative of the electron acceptor capacity of the compounds. Results obtained from this study will be useful in the efficient design and development of more active noscapinoids.     

Exploring structural features of EGFR–HER2 dual inhibitors as anti-cancer agents using G-QSAR approach

Abstract

Simultaneous inhibition of EGFR and HER2 by dual-targeting inhibitors is an established anti-cancer strategy. Therefore, a recent trend in drug discovery involves understanding the features of such dual inhibitors. In this study, three different G-QSAR models were developed corresponding to individual EGFR, HER2 and the dual-model for both receptors. The dual-model provided site-specific information wherein (i) increasing electronegative character and higher index of saturated carbon at R4 position; (ii) presence of chlorine atom at R2 position; (iii) decreasing alpha modified shape index at R1 and R3 positions; and (iv) less electronegativity at R2 position; were found important for enhancing the dual activity. Also, comparison of dual-model with the EGFR/HER2 individual models revealed that it incorporates the properties of both models and, thus, represents a combination of EGFR/HER2. Further, fragment analysis revealed that R2 and R4 are important for imparting high potency while specificity is decided by R1/R3 fragment. We also checked the predictive ability of the dual-model by determining applicability domain using William’s plot. Also, analysis of active molecules showed they show favorable substitutions that agree with the constructed dual-model. Thus, we have been successful in developing a single dual-response QSAR model to get an insight into various structural features influencing EGFR/HER2 activity.