Quercetin derivatives as non-nucleoside inhibitors for dengue polymerase: molecular docking, molecular dynamics simulation, and binding free energy calculation

<p>Dengue is an important public health problem in tropical and subtropical regions of the world. Neither vaccine nor an antiviral medication is available to treat dengue. This insists the need of drug discovery for dengue. In order to find a potent lead molecule, RNA-dependent RNA polymerase which is essential for dengue viral replication is chosen as a drug target. As Quercetin showed antiviral activity against several viruses, quercetin derivatives developed by combinatorial library synthesis and mined from PubChem databases were screened for a potent anti-dengue viral agent. Our study predicted Quercetin 3-(6″-(E)-p-coumaroylsophoroside)-7-rhamnoside as a dengue polymerase inhibitor. The results were validated by molecular dynamics simulation studies which reveal water bridges and hydrogen bonds as major contributors for the stability of the polymerase-lead complex. Interactions formed by this compound with residues Trp795, Arg792 and Glu351 are found to be essential for the stability of the polymerase-lead complex. Our study demonstrates Quercetin 3-(6″-(E)-p-coumaroylsophoroside)-7-rhamnoside as a potent non-nucleoside inhibitor for dengue polymerase.</p

Comparison between ensemble method and best methods in different datasets.

<p>Average accuracies (%) are given in parentheses.</p

Typical examples of best and least predicted folds, superfamilies and families.

<p>Accuracy1  =  (TP + TN)/(TP + TN + FP + FN).</p><p>Accuracy2  =  (sensitivity + specificity)/2.</p

Prediction performance of computational methods in various folds, superfamilies and families.

<p>Prediction performance of computational methods in various folds, superfamilies and families.</p

Prediction performance of computational methods in disordered regions.

<p>Prediction performance of computational methods in disordered regions.</p

Prediction accuracy of binding sites in different RNA types (3.5 Å cutoff).

<p>Accuracy1  =  (TP + TN)/(TP + TN + FP + FN).</p><p>Accuracy2  =  (sensitivity + specificity)/2.</p

Prediction of RNA Binding Residues: An Extensive Analysis Based on Structure and Function to Select the Best Predictor

<div><p>Protein-RNA complexes play key roles in several cellular processes by the interactions of amino acids with RNA. To understand the recognition mechanism, it is important to identify the specific amino acids involved in RNA binding. Various computational methods have been developed for predicting RNA binding residues from protein sequence. However, their performances mainly depend on the training dataset, feature selection for developing a model and learning capacity of the model. Hence, it is important to reveal the correspondence between the performance of methods and properties of RNA-binding proteins (RBPs). In this work, we have collected all available RNA binding residues prediction methods and revealed their performances on unbiased, stringent and diverse datasets for RBPs with less than 25% sequence identity based on structural class, fold, superfamily, family, protein function, RNA type, RNA strand and RNA conformation. The best methods for each type of RBPs and the type of RBPs, which require further refinement in prediction, have been brought out. We also analyzed the performance of these methods for the disordered regions, structures which are not included in the training dataset and recently solved structures. The reliability of prediction is better than randomly choosing any method or combination of methods. This approach would be a valuable resource for biologists to choose the best method based on the type of RBPs for designing their experiments and the tool is freely accessible online at <a href="http://www.iitm.ac.in/bioinfo/RNA-protein/" target="_blank">www.iitm.ac.in/bioinfo/RNA-protein/</a>.</p></div

Web application to provide the best methods based on the type of RBPs.

<p>Web application to provide the best methods based on the type of RBPs.</p

Prediction accuracy (%) of binding sites in different structural classes.

<p>Accuracy  =  (sensitivity+specificity)/2.</p

Identification of dengue viral RNA-dependent RNA polymerase inhibitor using computational fragment-based approaches and molecular dynamics study

<p>Dengue is a major public health concern in tropical and subtropical countries of the world. There are no specific drugs available to treat dengue. Even though several candidates targeted both viral and host proteins to overcome dengue infection, they have not yet entered into the later stages of clinical trials. In order to design a drug for dengue fever, newly emerged fragment-based drug designing technique was applied. RNA-dependent RNA polymerase, which is essential for dengue viral replication is chosen as a drug target for dengue drug discovery. A cascade of methods, fragment screening, fragment growing, and fragment linking revealed the compound [2-(4-carbamoylpiperidin-1-yl)-2-oxoethyl]8-(1,3-benzothiazol-2-yl)naphthalene-1-carboxylate as a potent dengue viral polymerase inhibitor. Both strain energy and binding free energy calculations predicted that this could be a better inhibitor than the existing ones. Molecular dynamics simulation studies showed that the dengue polymerase–lead complex is stable and their interactions are consistent throughout the simulation. The hydrogen-bonded interactions formed by the residues Arg792, Thr794, Ser796, and Asn405 are the primary contributors for the stability and the rigidity of the polymerase–lead complex. This might keep the polymerase in closed conformation and thus inhibits viral replication. Hence, this might be a promising lead molecule for dengue drug designing. Further optimization of this lead molecule would result in a potent drug for dengue.</p