Current Computer - Aided Drug Design - Volume 11, Issue 2, 2015

Pakistan possesses a rich and vast source of natural products (NPs). Some of these secondary metabolites have been identified as potent therapeutic agents. However, the medicinal usage of most of these compounds has not yet been fully explored. The discoveries for new scaffolds of NPs as inhibitors of certain enzymes or receptors using advanced computational drug discovery approaches are also limited due to the unavailability of accurate 3D structures of NPs. An organized database incorporating all relevant information, therefore, can facilitate to explore the medicinal importance of the metabolites from Pakistani Biodiversity. The Chemical Database of Pakistan (ChemDP; release 01) is a fully-referenced, evolving, web-based, virtual database which has been designed and developed to introduce natural products (NPs) and their derivatives from the biodiversity of Pakistan to Global scientific communities. The prime aim is to provide quality structures of compounds with relevant information for computer-aided drug discovery studies. For this purpose, over 1000 NPs have been identified from more than 400 published articles, for which 2D and 3D molecular structures have been generated with a special focus on their stereochemistry, where applicable. The PM7 semiempirical quantum chemistry method has been used to energy optimize the 3D structure of NPs. The 2D and 3D structures can be downloaded as .sdf, .mol, .sybyl, .mol2, and .pdb files – readable formats by many chemoinformatics/bioinformatics software packages. Each entry in ChemDP contains over 100 data fields representing various molecular, biological, physico-chemical and pharmacological properties, which have been properly documented in the database for end users. These pieces of information have been either manually extracted from the literatures or computationally calculated using various computational tools. Cross referencing to a major data repository i.e. ChemSpider has been made available for overlapping compounds. An android application of ChemDP is available at its website. The ChemDP is freely accessible at www.chemdp.com.

The recent H5N2 flu epidemic in US Midwest has led to deaths of millions of turkeys and farm bred poultry. While no human infections are reported to date, the rapid mutations in flu viruses can lead to more pathogenic subtypes. We have investigated such possibilities and have shown that H5N4, H5N9 and H5N6 are the most likely candidates for next round of viral reassortments, amongst which H5N9, if reassorted from Asiatic strains, could be highly pathogenic. We discuss here possibilities of anticipatory rational vaccine design based on work done earlier.

Variation in high-dimensional data is often caused by a few latent factors, and hence dimension reduction or variable selection techniques are often useful in gathering useful information from the data. In this paper we consider two such recent methods: Interrelated two-way clustering and envelope models. We couple these methods with traditional statistical procedures like ridge regression and linear discriminant analysis, and apply them on two data sets which have more predictors than samples (i.e. n << p scenario) and several types of molecular descriptors. One of these datasets consists of a congeneric group of Amines while the other has a much diverse collection compounds. The difference of prediction results between these two datasets for both the methods supports the hypothesis that for a congeneric set of compounds, descriptors of a certain type are enough to provide good QSAR models, but as the data set grows diverse including a variety of descriptors can improve model quality considerably.

B-Raf mutations are identified in 40-50% of patients with melanoma and among them, the substitution of valine for glutamic acid at position 600 (V600EB-Raf) is the most frequent. Treatment of these patients with B-Raf inhibitors has been associated with a clear clinical benefit. Unfortunately, multiple resistance mechanisms have been identified and new potent and selective inhibitors are currently needed. In this work, five different type II inhibitors, which bind V600EB-Raf in its DFG-out conformation, have been studied using molecular dynamics, free energy calculations and energy decomposition analysis. The ranking of calculated MM-PB/GBSA binding affinities is in good agreement with the experimentally measured ones. The per-residue decomposition of ΔGbinding, within the MM-GBSA approach, has been used to identify the key residues governing the allosteric binding of the studied compounds to the V600EB-Raf protein kinase. Results indicate that although van der Waals interactions are key determinants for binding, hydrogen bonds also play an important role. This work also provides a better structural understanding of the binding of DFG-out inhibitors to V600EB-Raf, which can be used in a further step for rational design of a new class of B-Raf potent inhibitors.

Estrogen receptors exist as two subtypes ERα and ERβ, which are characterized by various distributions in human tissues and diverse transcription regulation. Ligands capable of selective ERβ activation show positive effects in treatment of such diseases as certain cancers, endometriosis, inflammatory diseases, and assist in maintaining cardiovascular and nervous system health. Thus far, there are no pharmaceutical drugs available acting on this target. In order to provide new treatment for such diseases, a new generation of selective estrogen receptor modulators is required. This remains an unsolved task due to several difficulties. It is known that minor modifications of ER agonists can influence the selectivity of their binding. The majority of designed ligands acting on ER possess chiral centers thus exist as stereoisomers. Unfortunately, not every spatial isomer is individually considered in experimental research. The molecular docking was applied to investigate the structural basis of diverse selectivity and binding affinity of selected estrogen receptor β agonists. Docking simulations revealed that terminal aromatic rings positioned in the A- and D-ring regions are a factor that determines binding affinity of ERβ agonists. This positioning can be ascribed to the presence of two terminal hydroxyl groups, a rigid linker, and the introduction of aliphatic substituents. The side substituents of underlined molecular scaffold should adopt inside characterized cavities I and II in order to provide selectivity. The bulkiness, attachment to linker and stereochemistry of the substituents affect ERβ selectivity. These molecular features should be considered during search and design of new improved ERβ agonists.

In this study, the role of highly structured water molecules present in the active site of the leukotriene A4 hydrolase (LTA4H) enzyme has been critically examined through the docking experiments. It was observed that different experiments were necessary to perform the docking studies. The ligands capable of displacing or interacting with bound-water(s) displayed different binding poses as well as the scores. The docking scores E (CvdW) from Glide and ChemScore (CS) from FlexX, with and without bound-waters, obtained through different docking experiments were used to construct two structure guided bi-parametric linear regression models using the IC50 enzyme activity data. The predictive squared correlation coefficients (Q2) obtained for these models, with and without bound waters, were found to be 0.73 and 0.67, respectively. These models were validated using test and validated sets of compounds. Utilizing these QSAR models, 409 proposed structures were docked and their respective predicted IC50 data were generated. From the in-silico evaluation of these 409 proposed structures representing diverse chemotypes, 39 compounds were triaged, synthesized and evaluated in the enzyme inhibition assay. The predicted and experimental biological data (IC50) was correlated and the square of the correlation coefficient (R2) between the observed and calculated IC50 was found to be 0.87.

A 3D-QSAR selectivity analysis of 53 adamantyl heteroaryl urea derivatives active against M. tuberculosis is reported. These analogs inhibit Mycobacterial Membrane Protein Large 3 (MmpL3), a proposed transporter for cell wall mycolic acids. However, these analogs also exhibit affinity towards human soluble epoxide hydrolase (sEH) enzyme, making them pharmacologically undesirable. Thus, COMFA and CoMSIA selective studies viz ligand and receptor-based alignment has been described to evaluate key pharmacophoric structural features that may possibly play a crucial role for selective inhibition. This hypothesis was experimentally validated and successfully tested on four novel adamantyl urea based derivatives with known biological activity. Therefore, this approach may pave way to novel specific inhibitors in tuberculosis drug discovery process.

The paper is an attempt for QSAR modeling based on topological, electrostatic, quantum chemical, constitutional, geometrical and physicochemical indices computed from the structures of 59 set of synthesized chalcone derivatives tested for the cell cycle inhibition of mitotic G2/M phase using multiple linear regression method. Impact of such computed structural descriptors towards antimitotic and antiproliferative activities was analysed by ridge regression (RR) studies. The RR model explained that the topological indices alone can produce significant influence upon the pharmacological responses while combination of topological, electrostatic and quantum chemical descriptors can enhance the degree of impact towards antimitotic and antiproliferative activities of these compounds. Furthermore, QSAR models were formulated utilizing only topological and the combination of topological, electrostatic and quantum chemical descriptors respectively by multiple linear regression method and the validation of the model was performed by searching the predictability of the QSAR models. Satisfactory results were obtained in terms of model quality expressed as R2 = 0.826, QLoo2 = 0.710, Rpred2 = 0.771 respectively for the topological indices. Combination of topological, electrostatic and quantum chemical descriptors resulted in an increase of R2 = 0.965, QLoo 2 = 0.891, Rpred2 = 0.849. The generated model predicted that BCUT descriptors (Charge) using modified partial equalization of orbital electronegativity (MPEOE), autocorrelation descriptors, information content descriptor and HOMO descriptor are very much crucial for modeling highly active chalcone compounds. Quantitative structure-activity relationships modeling of 59 set of synthesized chalcone derivatives were tested for the inhibition of mitotic G2/M phase using ridge regression and multiple linear regression methodologies. The generated model predicted that BCUT descriptors (Charge) using modified partial equalization of orbital electronegativity (MPEOE), autocorrelation descriptors, information content descriptor and HOMO descriptor are very much crucial for producing mitotic spindle inhibition of chalcone compounds.