QSAR analyses on avian influenza virus neuraminidase inhibitors using CoMFA, CoMSIA, and HQSAR

QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations Studies of Lysine-Specific Demethylase 1 (LSD1) Inhibitors as Anticancer Agents

BACKGROUND

Histone Lysine Demetylases1 (LSD1) is a promising medication to treat cancer, which plays a crucial role in epigenetic modulation of gene expression. Inhibition of LSD1with small molecules has emerged as a vital mechanism to treat cancer.

OBJECTIVE

In the present research, molecular modeling investigations, such as CoMFA, CoMFA-RF, CoMSIA and HQSAR, molecular docking and Molecular Dynamics (MD) simulations were carried out on some tranylcypromine derivatives as LSD1 inhibitors.

METHODS

The QSAR models were carried out on a series of Tranylcypromine derivatives as data set via the SYBYL-X2.1.1 program. Molecular docking and MD simulations were carried out by the MOE software and the SYBYL program, respectively. The internal and external predictability performances related to the generated models for these LSD1 inhibitors were justified by evaluating cross-validated correlation coefficient (q²), noncross- validated correlation coefficient (r²_ncv) and predicted correlation coefficient (r²_pred) of the training and test set molecules, respectively.

RESULTS

The CoMFA (q², 0.670; r²_ncv, 0.930; r²_pred, 0.968), CoMFA-RF (q², 0.694; r²_ncr, 0.926; r²_pred, 0.927), CoMSIA (q², 0.834; r²_ncv, 0.956; r²_pred, 0.958) and HQSAR models (q², 0.854; r²_ncv, 0.900; r²_pred, 0.728) for training as well as the test set of LSD1 inhibition resulted in significant findings.

CONCLUSION

These QSAR models were found to be perfect and strong with better predictability. Contour maps of all models were generated and it was proven by molecular docking studies and molecular dynamics simulation that the hydrophobic, electrostatic and hydrogen bonding fields are crucial in these models for improving the binding affinity and determining the structure-activity relationship. These theoretical results are possibly beneficial to design new strong LSD1 inhibitors with enhanced activity to treat cancer.

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.

Applying high-performance computing in drug discovery and molecular simulation

In recent decades, high-performance computing (HPC) technologies and supercomputers in China have significantly advanced, resulting in remarkable achievements. Computational drug discovery and design, which is based on HPC and combines pharmaceutical chemistry and computational biology, has become a critical approach in drug research and development and is financially supported by the Chinese government. This approach has yielded a series of new algorithms in drug design, as well as new software and databases. This review mainly focuses on the application of HPC to the fields of drug discovery and molecular simulation at the Chinese Academy of Sciences, including virtual drug screening, molecular dynamics simulation, and protein folding. In addition, the potential future application of HPC in precision medicine is briefly discussed.

Flavonoids promoting HaCaT migration: I. Hologram quantitative structure-activity relationships

Cell migration plays an important role in multicellular development and preservation. Because wound healing requires cell migration, compounds promoting cell migration can be used for wound repair therapy. Several plant-derived polyphenols are known to promote cell migration, which improves wound healing. Previous studies of flavonoids on cell lines have focused on their inhibitory effects and not on wound healing. In addition, studies of flavonoids on wound healing have been performed using mixtures. In this study, individual flavonoids were used for cellular migration measurements. Relationships between the cell migration effects of flavonoids and their structural properties have never been reported. Here, we investigated the migration of keratinocytes caused by 100 flavonoids and examined their relationships using hologram quantitative structure-activity relationships. The structural conditions responsible for efficient cell migration on keratinocyte cell lines determined from the current study will facilitate the design of flavonoids with improved activity.

Genetic characterization of H5N1 influenza viruses isolated from chickens in Indonesia in 2010

Since 2003, highly pathogenic H5N1 avian influenza viruses have caused outbreaks among poultry in Indonesia every year, producing the highest number of human victims worldwide. However, little is known about the H5N1 influenza viruses that have been circulating there in recent years. We therefore conducted surveillance studies and isolated eight H5N1 viruses from chickens. Phylogenic analysis of their hemagglutinin and neuraminidase genes revealed that all eight viruses belonged to clade 2.1.3. However, on the basis of nucleotide differences, these viruses could be divided into two groups. Other viruses genetically closely related to these two groups of viruses were all Indonesian isolates, suggesting that these new isolates have been evolving within Indonesia. Among these viruses, two distinct viruses circulated in the Kalimantan islands during the same season in 2010. Our data reveal the continued evolution of H5N1 viruses in Indonesia.

Drug design for ever, from hype to hope

In its first 25 years JCAMD has been disseminating a large number of techniques aimed at finding better medicines faster. These include genetic algorithms, COMFA, QSAR, structure based techniques, homology modelling, high throughput screening, combichem, and dozens more that were a hype in their time and that now are just a useful addition to the drug-designers toolbox. Despite massive efforts throughout academic and industrial drug design research departments, the number of FDA-approved new molecular entities per year stagnates, and the pharmaceutical industry is reorganising accordingly. The recent spate of industrial consolidations and the concomitant move towards outsourcing of research activities requires better integration of all activities along the chain from bench to bedside. The next 25 years will undoubtedly show a series of translational science activities that are aimed at a better communication between all parties involved, from quantum chemistry to bedside and from academia to industry. This will above all include understanding the underlying biological problem and optimal use of all available data.

Molecular dynamics directed CoMFA studies on carbocyclic neuraminidase inhibitors

Zanamivir is the known potent anti-influenza agent targeting the key enzyme neuraminidase that cleaves sialic acid from cell receptors allowing release of newly formed virions. Molecular dynamics simulation was carried out to determine the dynamic behavior of Zanamivir upon its binding to flexible loops of neuraminidase and to analyse its interactions in the bioactive state. Neuraminidase exhibits wide range of affinity with structurally similar compounds. CoMFA study was used to determine quantitative structure-activity relationship for 36 carbocyclic Neuraminidase inhibitors (NIs). The CoMFA model was also successfully built using cross-validated r²cv = 0.580 and r²pred = 0.638.

Docking of sialic acid analogues against influenza A hemagglutinin: a correlational study between experimentally measured and computationally estimated affinities

A molecular docking tool of AutoDock3.05 was evaluated for its ability to reproduce experimentally determined affinities of various sialic acid analogues toward hemagglutinin of influenza A virus. With the exception of those with a C6-modified glycerol side chain, the experimental binding affinities of most sialic acid analogues (C2, C4 and C5-substituted) determined by viral hemadsorption inhibition assay, hemagglutination inhibition assay and nuclear magnetic resonance correlated well with the computationally estimated free energy of binding. Sialic acid analogues with modified glycerol side chains showed only poor correlation between the experimentally determined hemagglutinin inhibitor affinities and AutoDock3.05 scores, suggesting high mobility of the glutamic acid side chain at the glycerol binding pocket, which is difficult to simulate using a flexi-rigid molecular docking approach. In conclusion, except for some glycerol-substituted sialic acid analogues, the results showed the effectiveness of AutoDock3.05 searching and scoring functions in estimating affinities of sialic acid analogues toward influenza A hemagglutinin, making it a reliable tool for screening a database of virtually designed sialic acid analogues for hemagglutinin inhibitors.

A proline-based neuraminidase inhibitor: DFT studies on the zwitterion conformation, stability and formation

The designs of potent neuraminidase (NA) inhibitors are an efficient way to deal with the recent "2009 H1N1" influenza epidemic. In this work, density functional calculations were employed to study the conformation, stability and formation of the zwitterions of 5-[(1R,2S)-1-(acetylamino)-2-methoxy-2-methylpentyl]-4-[(1Z)-1-propenyl]-(4S,5R)-d-proline (BL), a proline-based NA inhibitor. Compared to proline, the zwitterion stability of BL is enhanced by 1.76 kcal mol(-1) due to the introduction of functional groups. However, the zwitterion of BL will not represent a local minimum on the potential energy surface until the number of water molecules increases up to two (n = 2). With the addition of two and three water molecules, the energy differences between the zwitterions and corresponding canonical isomers were calculated at 3.13 and -1.54 kcal mol(-1), respectively. The zwitterions of BL are mainly stabilized by the H-bonds with the water molecules, especially in the case of three water molecules where the carboxyl-O atoms are largely coordination-saturated by three H-bonds of medium strengths, causing the zwitterion stability even superior to the canonical isomer. With the presence of two and three water molecules, the energy barriers for the conversion processes from the canonical isomers to the zwitterions are equal to 4.96 and 3.13 kcal mol(-1), respectively. It indicated that the zwitterion formation is facile to take place with addition of two molecules and further facilitated by more water molecules. Besides, the zwitterion formation of BL is finished in a single step, different from other NA inhibitors. Owing to the above advantages, BL is a good NA inhibitor candidate and more attention should be paid to explorations of BL-based drugs.

Combining crystallographic information and an aspherical-atom data bank in the evaluation of the electrostatic interaction energy in an enzyme-substrate complex: influenza neuraminidase inhibition

Although electrostatic interactions contribute only a part of the interaction energies between macromolecules, unlike dispersion forces they are highly directional and therefore dominate the nature of molecular packing in crystals and in biological complexes and contribute significantly to differences in inhibition strength among related enzyme inhibitors. In the reported study, a wide range of complexes of influenza neuraminidases with inhibitor molecules (sialic acid derivatives and others) have been analyzed using charge densities from a transferable aspherical-atom data bank. The strongest interactions of the residues are with the acidic group at the C2 position of the inhibitor ( approximately -300 kJ mol(-1) for -COO(-) in non-aromatic inhibitors, approximately -120-210 kJ mol(-1) for -COO(-) in aromatic inhibitors and approximately -450 kJ mol(-1) for -PO(3)(2-)) and with the amino and guanidine groups at C4 ( approximately -250 kJ mol(-1)). Other groups contribute less than approximately 100 kJ mol(-1). Residues Glu119, Asp151, Glu227, Glu276 and Arg371 show the largest variation in electrostatic energies of interaction with different groups of inhibitors, which points to their important role in the inhibitor recognition. The Arg292-->Lys mutation reduces the electrostatic interactions of the enzyme with the acidic group at C2 for all inhibitors that have been studied (SIA, DAN, 4AM, ZMR, G20, G28, G39 and BCZ), but enhances the interactions with the glycerol group at C6 for inhibitors that contain it. This is in agreement with the lower level of resistance of the mutated virus to glycerol-containing inhibitors compared with the more hydrophobic derivatives.

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

Molecular characterization of the glycoprotein genes of H5N1 influenza A viruses isolated in Israel and the Gaza Strip during 2006 outbreaks

Highly pathogenic H5N1 avian influenza A viruses (AIV) have caused outbreaks among domestic poultry and wild aquatic birds in many Asian, European, and African countries since 1997. In March 2006 an avian H5N1 influenza A virus was isolated from poultry in Israel. In the present study we molecularly characterized the hemagglutinin (HA) and neuraminidase (NA) genes of eleven H5N1 viruses isolated from domestic poultry in Israel and Gaza in March-April 2006. Phylogenetic analysis of the HA and NA genes showed that the Israeli and Gazian viruses were closely related to viruses isolated in Egypt in 2006.