Optimization of Structure Based Virtual Screening Protocols Against Thymidine Monophosphate Kinase Inhibitors as Antitubercular Agents

Relative assessment of different statistical instruments and measures for the prediction of promising outcomes using docking, virtual screening and ADMET analysis against HIV-RT

Reverse transcriptase is the most therapeutic target for the discovery of novel, potent, and non-toxic new anti-retroviral drugs. In the present work, various docking software such as Sybyl Surflex-Dock, OpenEye FRED, and Hermes GOLD were evaluated for their efficiency to reproduce known cognate inhibitors' conformations. Three metrics were used and compared to assess the performance of the applied scoring functions, i.e. enrichment factor, receiver operating characteristic (ROC) curves, and Bedroc analysis. Twelve different scoring functions of three softwares were used to assess their ability to rank the cognate ligand within the active site of its proteins. The extensive virtual screening task was performed on eight crystal structures, and the performance of docking and scoring was assessed by their ability to efficiently detect known active compounds enriched in the top-ranked of the list among a randomly selected dataset of the ten thousand compounds of the NCI database. The effectiveness of post-docking relaxation in Surflex was also evaluated. The top 20, 50, and 100 compounds were selected based on consensus scoring functions from all 48 proteins with different ligand complexes. Further, the shortlisted leads were subjected to ADMET via using Discovery Studio. The results further implicate the importance of various statistical tools that should be followed before large-scale virtual screening for the drug discovery process. In silico results demonstrating the experiment was successful. The study of the research covers the combinatorial in silico techniques such as benchmarking of the softwares and scoring functions, statistical tools applied for screening and different conformations of HIV-RT crystal structures for virtual screening with rigid and flexible molecular docking and molecular dynamics simulation approach. This study reveals a clear roadmap to identify novel scaffolds against HIV-RT for antiretroviral therapy, thus providing the remedial solutions of HIV related infections and other diseases caused by malfunctioning of the target protein.Communicated by Ramaswamy H. Sarma.

Computational Overview of Mycobacterial Thymidine Monophosphate Kinase

Tuberculosis (TB) ranks among the diseases with the highest morbidity rate with significantly high prevalence in developing countries. Globally, tuberculosis poses the most substantial burden of mortality. Further, a partially treated tuberculosis patient is worse than untreated; they may lead to standing out as a critical obstacle to global tuberculosis control. The emergence of multi-drug resistant (MDR) and extremely drug-resistant (XDR) strains, and co-infection of HIV further worsen the situation. The present review article discusses validated targets of the bacterial enzyme thymidine monophosphate kinase (TMPK). TMPKMTB enzyme belongs to the nucleoside monophosphate kinases (NMPKs) family. It is involved in phosphorylation of TMP to TDP, and TDP is phosphorylated to TTP. This review highlights structure elucidation of TMP enzymes and their inhibitors study on TMP scaffold, and it also discusses different techniques; including molecular docking, virtual screening, 3DPharmacophore, QSAR for finding anti-tubercular agents.

Exploring Novel N-Myristoyltransferase Inhibitors: A Molecular Dynamics Simulation Approach

N-Myristoyltransferase (NMT) is a cytosolic monomeric enzyme involved in the allocation of the myristoyl group to the aminoterminal of glycine in several viral and eukaryotic cellular proteins. NMT has been validated as a potential drug target against kinetoplastid for parasitic protozoa. A multistep virtual screening protocol based on the pharmacophore modeling, molecular docking, and molecular dynamics simulation was carried out. Initially, Maybridge database was virtually screened via a validated pharmacophore model. The effective pharmacophore models were accompanied with exclusion volumes to improve their receiver operating characteristic curve to identify potential NMT inhibitors. The hits identified as actives based on the 3D-pharmacophore model were evaluated by molecular docking studies. In stepwise screening, six compounds were shortlisted for the dynamic simulation to get insights into their binding mode. In conclusion, this study provides fundamental information about the architecture of the binding site and some crucial residues that may provide insights into the development of new antiparasitic agents.

An exhaustive yet simple virtual screening campaign against Sortase A from multiple drug resistant Staphylococcus aureus

Methicillin resistant Staphylococcus aureus (MRSA) is one of the challenging bacterial pathogen due to its acquired resistance to the β lactam antibiotics. The Sortase A is an enzyme of Gram-positive bacteria including S. aureus to anchor surface proteins to the cell wall. Sortase A is well studied enzyme and considered as the drug target against MRSA. Sortase A plays active role in anchoring the virulence proteins on the cell wall of the Gram-positive bacteria. The inhibition of Sortase A activity results in the separation of S. aureus from the host cells and ultimately alleviation of the infection. Here, we adapted a structure-based virtual screening protocol which helped in identification of novel potential inhibitors of Sortase A. The protocol involved the docking of a chemical library of druglike compounds with the Sortase A binding site represented by multiple crystal structures. The compounds were ranked by multiple scoring functions and shortlisted for future experimental screening. The method resulted in shortlisting of three compounds as potential novel inhibitors of Sortase A out of a large chemical library. The high rankings of shortlisted compounds estimated by multiple scoring functions showed their binding potential with Sortase A. The results are proved to be a simple yet efficient choice of structure-based virtual screening. The identified compounds are druglike and show high rankings among all set protocols of the virtual screening. We hope that the study would eventually help to expedite the discovery of novel drug candidates against MRSA.