Efficient in silico assay of inhibitors of hepatitis C Virus RNA-dependent RNA polymerase by structure-based virtual screening and in vitro evaluation

The IQA Energy Partition in a Drug Design Setting: A Hepatitis C Virus RNA-Dependent RNA Polymerase (NS5B) Case Study

The interaction of the thumb site II of the NS5B protein of hepatitis C virus and a pair of drug candidates was studied using a topological energy decomposition method called interacting quantum atoms (IQA). The atomic energies were then processed by the relative energy gradient (REG) method, which extracts chemical insight by computation based on minimal assumptions. REG reveals the most important IQA energy contributions, by atom and energy type (electrostatics, sterics, and exchange–correlation), that are responsible for the behaviour of the whole system, systematically from a short-range ligand–pocket interaction until a distance of approximately 22 Å. The degree of covalency in various key interatomic interactions can be quantified. No exchange–correlation contribution is responsible for the changes in the energy profile of both pocket–ligand systems investigated in the ligand–pocket distances equal to or greater than that of the global minimum. Regarding the hydrogen bonds in the system, a “neighbour effect” was observed thanks to the REG method, which states that a carbon atom would rather not have its covalent neighbour oxygen form a hydrogen bond. The combination of IQA and REG enables the automatic identification of the pharmacophore in the ligands. The coarser Interacting Quantum Fragments (IQF) enables the determination of which amino acids of the pocket contribute most to the binding and the type of energy of said binding. This work is an example of the contribution topological energy decomposition methods can make to fragment-based drug design.

Identifying new liver X receptor alpha modulators and distinguishing between agonists and antagonists by crystal ligand pocket screening

Background: Modulators of LXRα are of high pharmacological interest as LXRα regulates fatty acid metabolism, inflammatory processes and cancer. We aim to identify new LXRα modulators and to recognize a distinguishable feature of agonists. Results&methodology: The ligand self-dock and largest-cavity-size searching purposely located two appropriate ligand-binding sites to reach the two aims. One is identifying the new modulators from Maybridge library. 20 new compounds are confirmed by the in vitro reporter gene assay. The other is denoting an agonist by at least one best docking pose having one hydrogen bond to LXRα Helix12 His421. Conclusion: Based on the quality x-ray binding pocket, we can identify new LXRα modulators and distinguish between agonists and antagonists by molecular docking.

Inhibiting two cellular mutant epidermal growth factor receptor tyrosine kinases by addressing computationally assessed crystal ligand pockets

Aim: Blocking receptor tyrosine kinases is a useful strategy for inhibiting the overexpression of EGFR. However, the quality of crystal pocket is an essential issue for virtually identifying new leads for surviving resistance cancer cells. Results: With the examinating crystal pocket quality by the self-docking root-mean-square deviation (RMSD) calculation, we used the two best kinase pockets of mutant EGFR kinases, T790M/L858R and G719S, for virtual screening. After sorting all the docking poses of the 57,177 library compounds by consensus scores, three evidently blocked cellular EGFR phosphorylation in the H1975 and SW48 cell lines. Conclusion: The computationally assessed qualities of crystal pockets of crystal EGFR kinases can help identify new cellular active and target-specific ligands rapidly and at low cost.

A study of the allosteric inhibition of HCV RNA-dependent RNA polymerase and implementing virtual screening for the selection of promising dual-site inhibitors with low resistance potential

Structure-based pharmacophores were generated and validated using the bioactive conformations of different co-crystallized enzyme-inhibitor complexes for allosteric palm-1 and thumb-2 inhibitors of NS5B. Two pharmacophore models were obtained, one for palm-1 inhibitors with sensitivity = 0.929 and specificity = 0.983, and the other for thumb-2 inhibitors with sensitivity = 1 and specificity = 0.979. In addition, a quantitative structure activity relationship (QSAR) models were developed based on using the values of different scoring functions as descriptors predicting the activity on both allosteric binding sites (palm-1 and thumb-2). QSAR studies revealed good predictive and statistically significant two descriptor models (r²= .837, r²_adjusted = .792 and r²_prediction = .688 for palm-1 model and r²= .927, r²_adjusted = .908 and r²_prediction = .779 for thumb-2 model). External validation for the QSAR models assured their prediction power with r²_ext= .72 and .89 for palm-1 and thumb-2, respectively. Different docking protocols were examined for their validity to predict the correct binding poses of inhibitors inside their respective binding sites. Virtual screening was carried out on ZINC database using the generated pharmacophores, the selected valid docking algorithms and QSAR models to find compounds that could theoretically bind to both sites simultaneously.

In silico virtual screening approaches for anti-viral drug discovery

Despite the considerable advances in medical and pharmaceutical research during the past years, diseases caused by viruses have remained a major burden to public health. Virtual in silico screening has repeatedly proven to be useful to meet the special challenges of antiviral drug discovery. Large virtual compound libraries are filtered by different computational screening methods such as docking, ligand-based similarity searches or pharmacophore-based screening, reducing the number of candidate molecules to a smaller set of promising candidates that are then tested biologically. This rational approach makes the drug discovery process more goal-oriented and saves resources in terms of time and money. In this review we discuss how different virtual screening techniques can be applied to antiviral drug discovery, present recent success stories in this field and finally address the main differences between the methods.: