Current Computer - Aided Drug Design - Volume 10, Issue 4, 2014

Influenza viruses are characterized by two surface proteins - the hemagglutinin (HA) of which there are 16 varieties, and the neuraminidase (NA) of which there are 9, each subtype characterized by its antigenic properties. Although theoretically 16 x 9 combinations are possible, only a few like the H1N1, H3N2, etc are seen to occur more frequently. Numerous studies with select subtypes like H1N1, H5N1, etc., have explained this phenomena by indicating that viral viability necessitates functional balance between the NA and HA so that only some combinations are favored. However, the reasons for this balance or its characteristics and whether this is universal for influenza subtypes are not yet known. Using novel graphical techniques and hypothesizing a coupling between the HA and NA, we devised a coupling factor to estimate the interdependence, if any, between HA and NA sequences covering a global sample of 10 subtypes and 164 sequences. We found that (a) the coupling we hypothesized between HAs and NAs is characteristic of each subtype, (b) within each subtype the coupling value is significantly different for human infecting strains and those that infect avians, and (c) artificial strains made up by mixing and matching HAs and NAs from different subtypes produce coupling factors that are far from the characteristic values for the parent subtype indicating possibly non-viable viruses, a result that matches with experimental evidence of Zhang et al. [1]. We also show that some natural strains that did not fit the characteristic values for its subtype could have been possible mismatches during viral packaging. Our observations have important consequences for drug and vaccine design and for monitoring of influenza virus reassortments and possible evolution of human pandemics.

Structure-activity relationships in a data set of HPV6-E1 helicase ATPase inhibitors were investigated based on two different sets of descriptors. Statistically significant four parameter Quantitative Structure-Activity Relationships (QSAR) models were constructed and validated in both cases (R2=0.849; R2 cv=0.811; F=52.20; s2=0.25; N=42). A Fragment based QSAR (FQSAR) approach was applied for developing a fragment-QSAR equation, which enabled the construction of virtual structures for novel ATPase inhibitors with desired or pre-defined activity.

Peptide deformylase (PDF) has emerged as an important antibacterial drug target. Considerable effort is being directed toward developing peptidic and non-peptidic inhibitors for this metalloprotein. In this work, the known peptidic inhibitor BB-3497 and its various ionization and tautomeric states are evaluated for their inhibition efficiency against PDF using a molecular mechanics (MM) approach as well as a mixed quantum mechanics/molecular mechanics (QM/MM) approach, with an aim to understand the interactions in the binding site. The evaluated Gibbs energies of binding with the mixed QM/MM approach are shown to have the best predictive power. The experimental pose is found to have the most negative Gibbs energy of binding, and also the smallest strain energy. A quantum mechanical evaluation of the active site reveals the requirement of strong chelation by the ligand with the metal ion. The investigated ligand chelates the metal ion through the two oxygens of its reverse hydroxamate moiety, particularly the N-O- oxygen, forming strong covalent bonds with the metal ion, which is penta-coordinated. In the uninhibited state, the metal ion is tetrahedrally coordinated, and hence chelation with the inhibitor is associated with an increase of the metal ion coordination. Thus, the strong binding of the ligand at the binding site is accounted for.

Ataxia telangiectasia-mutated and Rad3-related (ATR) protein kinase is an attractive anticancer target. In this study, comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were performed on a series of aminopyrazine ATR inhibitors. The models generated by CoMFA had a cross-validated coefficient (q2) of 0.752 and a regression coefficient (r2) of 0.947. The CoMSIA models had a q2 of 0.728 and an r2 of 0.936. The reasonable quantitative structure-activity relationship model showed robust predictive ability. The contour map provided guidelines for building novel virtual compounds based on compound NO.40. In addition, the 3D structure of ATR was modeled by homology modeling. Molecular dynamic simulations were employed to optimize the structure. The docking results offered insights into the interactions between the inhibitors and the active site for potent analysis. This study provides useful guidance for the discovery of more potent compounds.

Quantitative three dimensional structure activity relationship (3D-QSAR) studies were performed on phenothiazine derivatives as Butyrylcholinesterase (BuChE) inhibitors. Pharmacophore Alignment and Scoring Engine (PHASE) was used to develop predictive Common Pharmacophore Hypotheses (CPHs). The alignment thus obtained was used for Comparative Molecular Field Analysis (CoMFA)/Comparative Molecular Similarity Indices Analysis (CoMSIA) model development. A fourpoint common pharmacophore hypothesis, comprising of one acceptor, one hydrophobic region and two aromatic ring centres was generated. A structurally diverse set of 80 molecules was used of which 56 were grouped into training set to develop the model and the rest 24 molecules into test set to validate the CoMFA/CoMSIA models. The models so developed showed a good r2 predictive of 0.7587 for CoMFA and 0.7737 for CoMSIA. CoMFA and CoMSIA models had excellent Q2 (cross-validated coefficient) of 0.7125 and 0.7093, respectively which showed high correlative and predictive abilities of the models. The 3-D contour maps of CoMFA/CoMSIA provided interpretable explanation of SAR for the compounds and also permitted interesting conclusions about the substituent effects on the phenothiazine derivatives. The outcomes of the study would help in the rational design of novel and potent therapeutic agents as specific BuChE inhibitors for symptomatic or disease modifying treatment of AD.

Acquired immunodeficiency syndrome (AIDS) is one of the most devastating diseases of current century which is caused by the human immunodeficiency virus (HIV). Although great efforts have been done to fight the virus, the need of new therapeutics candidates of any kind still remains. This process needs huge time and experimental endeavor. However, Computer-aided techniques and can speed up the procedure. Currently, cheminformatics tools have proven to be extremely valuable in pharmaceutical research. In the past few decades, a huge number of different molecular descriptors were designed to describe chemical molecules in a quantitative way to make it easy to use them for computational studies. Herein, we present a computational study of anti-HIV small molecules test by the National Cancer Institute (NCI) to introduce the most efficient molecular descriptors for anti-HIV activity. In this regard a dataset of 199 highly active anti-HIV and 174 inactive compounds were defined by 905 molecular descriptors. Data were classified using Random Forest algorithm and the most important molecular descriptors were introduced as the parameters responsible for representing anti-HIV activity. Applying the mentioned computational and cheminformatics methods, it is possible to predict the anti-HIV activity of any given small molecule with high accuracy.

By binding to and activating the G-protein coupled μ−, Κ− and δ−opioid receptors in the central nervous system, opiates are known to induce analgesic and sedative effects. In particular, non-peptide opioid ligands are often used in clinical applications to induce these therapeutically beneficial effects, due to their superior pharmacokinetics and bioavailability in comparison to endogenous neuropeptides. However, since opioid alkaloids are highly addictive substances, it is necessary to understand the exact mechanisms of their actions, specifically the ligand-binding properties of the target receptors, in order to safely apply opiates for therapeutic purposes. Using an in silico molecular docking approach (AutoDock Vina) combined with two-step cluster analysis, we have computationally obtained the docking scores and the ligand-binding pockets of twelve representative non-peptide nonendogenous agonists and antagonists at the crystallographically identified μ-opioid receptor. Our study predicts the existence of two main binding sites that are congruently present in all opioid receptor types. Interestingly, in terms of the agonist or antagonist properties of the substances on the receptors, the clustering analysis suggests a relationship with the position of the ligand-binding pockets, particularly its depth within the receptor structure. Furthermore, the binding affinity of the substances is directly correlated to the proximity of the binding pockets to the extracellular space. In conclusion, the results provide further insights into the structural features of the functional pharmacology of opioid receptors, suggesting the importance of the binding position of non-peptide agonists and antagonists- specifically the distance and the level of exposure to the extracellular space- to their dissociation kinetics and subsequent potency.

Dengue and chikungunya virus infections are one of the major causes of morbidity and mortality in tropical and sub-tropical regions of the world. These two viruses belong to two different families with many similarities and dissimilarities. Both are enveloped viruses and the mode of transmission is also by the same mosquito species. Especially in case of symptom expression, there is confusion between these two viruses. Reports indicate the overlapping endemic areas and co-infections of both viruses in a single patient. The above factors indicate that there is a need for developing a single drug/vaccine for both the viruses. As a first report in this direction, we have used the bioinformatics tools to identify a common target in both the viruses for a single inhibitor molecule. Phylogenetic and distance based analyses using the nucleotide sequences of arthropod and non-arthropod borne viruses indicated a common origin of evolutionary point for mosquito borne viruses, irrespective of their families. Similarly, the amino acid sequences of non-structural protein-4B (NS4B) of dengue virus and non-structural protein-P4 (nsP4) of chikungunya virus showed a common evolutionary origin. Modeled and superimposed 3D-structures of above two proteins showed a common alpha helix. Virtual screening of selected molecules was done to identify the molecules which can bind to the identified common helix and found that N-(p-tolylmethyl)-3-[(3-pyridylmethylamino)methyl]benzamide (TPB) has significant binding characteristics to the common helix. Molecular simulations indicated that both the protein-TPB complexes were stable. Therefore, we propose that TPB or its analogues could act as antiviral agents against both the viruses.

A novel approach is developed for modeling situations in which the modeled property is an algebraically transformed version of the original experimental data. In many cases such a transformation results in a data set with a significantly smaller data range. Here we explore the effects of range-of-data on modeling statistics. We illustrate a twostep method using data on the mass spectrometry collision energy (CE) that is required to decompose 50% of precursor ions to fragments (CE50). Earlier we showed that a nonlinear center-of-mass transformation, yielding Ecom50, produces values less dependent on the specific mass spectrometric experimental conditions. For this data set the Ecom50 range is 13.5% of the CE50 range. We propose a two-step modeling method. First, the original experimental data, CE50, (larger range-of-data) is modeled by a standard modeling method (PLS). Second, the calculated dependent variable resulting from the modeling is algebraically transformed (not modeled) according to the center-of-mass transformation, providing the generally more useful data, Ecom50. As shown here, use of this two-step method for predicting Ecom50 (from previously published data) produces a standard error 21% smaller and correspondingly reduces the confidence interval for prediction. Some specific implications for prediction are given for a published data set. This work is part of the ongoing development of a system of models to assist in the development of human metabolites.

Tuberculosis continues to become a major threat and wide spreading disease though out the world. Therefore it is required to identify the new drugs for the treatment of tuberculosis with better activity profile than the prevalent compounds. In present study we have screened and modified the antitubercular compounds from commercial chemical database using the interaction-based pharmacophore and molecular docking studies. In the first step different pharmacophores of cocrystal structures of enyol acyl carrier reductase (also known as InhA) proteins (2B36 and 3FNG) were generated and employed for screening of ChemDiv database. Four different pharmacophore hypothesis retrieved 3456 hits from approximately 0.67 million compounds. In the second filter, these hit molecules were subjected to the molecular docking studies in 2NSD and 3FNG crystal structures. On the basis of high fit values, GScore, structural diversity and visual inspection, one hundred compounds were selected, purchased and subjected to experimental validation for antitubercular activity against H37Rv Mycobacterium tuberculosis (MTB) strain. Three compounds showed the minimal inhibitory concentration (MIC) value at 16 μg/mL and one compound VH04 showed the value at 1 μg/mL. Then a more active amidoethylamine compound was developed by chemical modifications of the virtual hit VH04 against the MTB strain. We believe that this newly identified scaffold could be useful for the optimization of lead from hit compounds of new antitubercular agents.