Recent Advances in Computer-Aided Drug Design as Applied to Anti-Influenza Drug Discovery

Abstract

Influenza is a seasonal and serious health threat, and the recent outbreak of H7N9 following the pandemic spread of H1N1 in 2009 has served to emphasize the importance of anti-influenza drug discovery. Zanamivir (Relenza™) and oseltamivir (Tamiflu(®)) are two antiviral drugs currently recommended by the CDC for treating influenza. Both are examples of the successful application of structure-based drug design strategies. These strategies have combined computer- based approaches, such as docking- and pharmacophore-based virtual screening with X-ray crystallographic structural analyses. Docking is a routinely used computational method to identify potential hits from large compound libraries. This method has evolved from simple rigid docking approaches to flexible docking methods to handle receptor flexibility and to enhance hit rates in virtual screening. Virtual screening approaches can employ both ligand-based and structurebased pharmacophore models depending on the available information. The exponential growth in computing power has increasingly facilitated the application of computer-aided methods in drug discovery, and they now play significant roles in the search for novel therapeutics. An overview of these computational tools is presented in this review, and recent advances and challenges will be discussed. The focus of the review will be anti-influenza drug discovery and how advances in our understanding of viral biology have led to the discovery of novel influenza protein targets. Also discussed will be strategies to circumvent the problem of resistance emerging from rapid mutations that has seriously compromised the efficacy of current anti-influenza therapies.

Exploring the Inhibitory Mechanism of Approved Selective Norepinephrine Reuptake Inhibitors and Reboxetine Enantiomers by Molecular Dynamics Study

“…These methods were applied (1) to elucidate binding mechanisms of substrates and inhibitors to monoamine transporter (MAT) 29 30 31 32 33 34 35 36 (2) to discover novel scaffolds of MAT inhibitors by virtual screening 37 38 39 , and (3) to distinguish various molecular mechanisms of enantiomers binding to MAT 40 41 . As one of these powerful computational methods, the molecular dynamics (MD) providing atomic description of protein dynamics and flexibility 42 43 44 45 was employed to simulate the large scale motions of MAT 27 46 47 . However, MD simulation has not yet been carried out to explore the binding of sNRIs to hNET.…”

Size and Flexibility Define the Inhibition of the H3N2 Influenza Endonuclease Enzyme by Calix[n]arenes

“…It is interesting to note that the end to end distance at 2.0-2.2 nm is almost constant for all the Then, blind docking method has been carried out to predict where the calix[n]arene interacted with endonuclease PA, as we expected interaction at the DNA holding site and not the active cleavage site. As stated by Mallipeddi [28], computational screening may offer a hit rate that is 10 times higher (1-5%) than random screening. Therefore, blind docking has been found out here particularly appropriated in our methodology to suggest the DNA binding groove site as one of the possible target sites.…”

Computer Aided Drug Designing

“…[14]  It is hoped that in case of certain diseases like Influenza, Computational Drug Designing will play an important role in reducing the chances of drug resistance and thus would lead to production of lead compounds which would target the causative factor. [14]  Taking advantage of computational methods, potent hits can be obtained in a  Matter of weeks.CADD has also led to construction of high quality datasets and libraries that can be optimised for high molecular diversity or similarity. [15] Limitations in CADD:  Lack of accurate experimental data that restricts further advancement of CADD.…”