Design of the influenza virus inhibitors targeting the PA endonuclease using 3D-QSAR modeling, side-chain hopping, and docking

QSAR study of N-substituted oseltamivir derivatives as potent avian influenza virus H5N1 inhibitors using quantum chemical descriptors and statistical methods

In silico modelling studies were executed on thirty two N-substituted oseltamivir derivatives as inhibitors of influenza virus H5N1.

Characterization of the endonuclease activity of the replication-associated protein of beak and feather disease virus

Beak and feather disease virus (BFDV) belongs to the family Circoviridae. A rolling-circle replication strategy based on a replication-associated protein (Rep) has been proposed for BFDV. The Rep gene of BFDV was expressed and purified, and it was shown to cleave short oligonucleotides containing the conserved nonanucleotide sequence found in the replication origin of circoviruses. This endonuclease activity was most efficient in the presence of the divalent metal ions Mg²⁺ and Mn²⁺. Rep proteins containing mutation in the ATPase/GTPase motifs and the ¹⁴FTLNN¹⁸, ⁶¹KKRLS⁶⁵, ⁸⁹YCSK⁹², and ¹⁷⁰GKS¹⁷² motifs lacked endonuclease activity. The endonuclease activity was not affected by ATPase inhibitors, with the exception of N-ethylmaleimide (NEM), or by GTPase inhibitors, but it was decreased by treatment with the endonuclease inhibitor L-742001. Both the ATPase and GTPase activities were decreased by site-directed mutagenesis and deletion of the ATPase/GTPase and endonuclease motifs. The Rep protein was able to bind a double-stranded DNA fragment of P36 (dsP36) containing the stem-loop structure of the replication origin of BFDV. All of the Rep mutant proteins showed reduced ability to bind this fragment, suggesting that all the ATPase/GTPase and endonuclease motifs are involved in the binding. Other than NEM, all ATPase, GTPase, and endonuclease inhibitors inhibited the binding of the Rep protein to the dsP36 fragment. This is the first report describing the endonuclease activity of the Rep protein of BFDV.

Inhibitors of Influenza A Virus Polymerase

The propensity of influenza virus to develop resistance to commonly prescribed drugs highlights the need for continuing development of new therapeutics. Biological and structural investigations of the enzymatic and interaction domains among influenza A virus polymerase subunits have broadened the target reservoir for drug screening. With the wealth of knowledge from these studies, identification of small-molecule and peptidic inhibitors that specifically abrogate polymerase activity or disrupt the polymerase assembly has emerged as an innovative and promising approach. Importantly, those domains are highly conserved among influenza subtypes and thus minimize the emergence of drug resistant mutants. An overview of the reported enzymatic inhibitors and protein-protein disruptors has been provided, in our effort to facilitate the development of next-generation anti-influenza therapeutics.

Influenza A virus polymerase: an attractive target for next-generation anti-influenza therapeutics

The influenza RNA-dependent RNA polymerase (RdRP) is conserved among different types of influenza virus, playing an important part in transcription and replication. In this regard, influenza RdRP is an attractive target for novel anti-influenza drug discovery. Herein, we will introduce the structural and functional information of influenza polymerase; and an overview of inhibitors targeting the PA endonuclease and PB2 cap-binding site is provided, along with the approaches utilized for identification of these inhibitors. The protein-protein interactions (PPIs) of the three polymerase subunits: PA, PB1 and PB2, are described based on the published crystal structures, and inhibitors targeting the PA-PB1 interaction are introduced briefly.

AVCpred: an integrated web server for prediction and design of antiviral compounds

Viral infections constantly jeopardize the global public health due to lack of effective antiviral therapeutics. Therefore, there is an imperative need to speed up the drug discovery process to identify novel and efficient drug candidates. In this study, we have developed quantitative structure-activity relationship (QSAR)-based models for predicting antiviral compounds (AVCs) against deadly viruses like human immunodeficiency virus (HIV), hepatitis C virus (HCV), hepatitis B virus (HBV), human herpesvirus (HHV) and 26 others using publicly available experimental data from the ChEMBL bioactivity database. Support vector machine (SVM) models achieved a maximum Pearson correlation coefficient of 0.72, 0.74, 0.66, 0.68, and 0.71 in regression mode and a maximum Matthew's correlation coefficient 0.91, 0.93, 0.70, 0.89, and 0.71, respectively, in classification mode during 10-fold cross-validation. Furthermore, similar performance was observed on the independent validation sets. We have integrated these models in the AVCpred web server, freely available at http://crdd.osdd.net/servers/avcpred. In addition, the datasets are provided in a searchable format. We hope this web server will assist researchers in the identification of potential antiviral agents. It would also save time and cost by prioritizing new drugs against viruses before their synthesis and experimental testing.

A novel small-molecule inhibitor of influenza A virus acts by suppressing PA endonuclease activity of the viral polymerase

The RNA-dependent RNA polymerase of influenza A virus comprises conserved and independently-folded subdomains with defined functionalities. The N-terminal domain of the PA subunit (PA_N) harbors the endonuclease function so that it can serve as a desired target for drug discovery. To identify a class of anti-influenza inhibitors that impedes PA_N endonuclease activity, a screening approach that integrated the fluorescence resonance energy transfer based endonuclease inhibitory assay with the DNA gel-based endonuclease inhibitory assay was conducted, followed by the evaluation of antiviral efficacies and potential cytotoxicity of the primary hits in vitro and in vivo. A small-molecule compound ANA-0 was identified as a potent inhibitor against the replication of multiple subtypes of influenza A virus, including H1N1, H3N2, H5N1, H7N7, H7N9 and H9N2, in cell cultures. Combinational treatment of zanamivir and ANA-0 exerted synergistic anti-influenza effect in vitro. Intranasal administration of ANA-0 protected mice from lethal challenge and reduced lung viral loads in H1N1 virus infected BALB/c mice. In summary, ANA-0 shows potential to be developed to novel anti-influenza agents.

Virtual Screening and Biological Validation of Novel Influenza Virus PA Endonuclease Inhibitors

The influenza virus RNA-dependent RNA polymerase complex (RdRp), a heterotrimeric protein complex responsible for viral RNA transcription and replication, represents a primary target for antiviral drug development. One particularly attractive approach is interference with the endonucleolytic "cap-snatching" reaction by the RdRp subunit PA, more precisely by inhibiting its metal-dependent catalytic activity which resides in the N-terminal part of PA (PA-Nter). Almost all PA inhibitors (PAIs) thus far discovered bear pharmacophoric fragments with chelating motifs able to bind the bivalent metal ions in the catalytic core of PA-Nter. More recently, the availability of crystallographic structures of PA-Nter has enabled rational design of original PAIs with improved binding properties and antiviral potency. We here present a coupled pharmacophore/docking virtual screening approach that allowed us to identify PAIs with interesting inhibitory activity in a PA-Nter enzymatic assay. Moreover, antiviral activity in the low micromolar range was observed in cell-based influenza virus assays.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R²) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.