Vasanthanathan Poongavanam

Virtual Screening Models for Prediction of HIV-1 RT Associated RNase H Inhibition

The increasing resistance to current therapeutic agents for HIV drug regiment remains a major problem for effective acquired immune deficiency syndrome (AIDS) therapy. Many potential inhibitors have today been developed which inhibits key cellular pathways in the HIV cycle. Inhibition of HIV-1 reverse transcriptase associated ribonuclease H (RNase H) function provides a novel target for anti-HIV chemotherapy. Here we report on the applicability of conceptually different in silico approaches as virtual screening (VS) tools in order to efficiently identify RNase H inhibitors from large chemical databases. The methods used here include machine-learning algorithms (e.g. support vector machine, random forest and kappa nearest neighbor), shape similarity (rapid overlay of chemical structures), pharmacophore, molecular interaction fields-based fingerprints for ligands and protein (FLAP) and flexible ligand docking methods. The results show that receptor-based flexible docking experiments provides good enrichment (80–90%) compared to ligand-based approaches such as FLAP (74%), shape similarity (75%) and random forest (72%). Thus, this study suggests that flexible docking experiments is the model of choice in terms of best retrieval of active from inactive compounds and efficiency and efficacy schemes. Moreover, shape similarity, machine learning and FLAP models could also be used for further validation or filtration in virtual screening processes. The best models could potentially be use for identifying structurally diverse and selective RNase H inhibitors from large chemical databases. In addition, pharmacophore models suggest that the inter-distance between hydrogen bond acceptors play a key role in inhibition of the RNase H domain through metal chelation.

Targeting VEGF with LNA-stabilized G-rich oligonucleotide for efficient breast cancer inhibition

In this study, we investigated the efficacy of an LNA (locked nucleic acid)-modified DNA aptamer named RNV66 targeting VEGF against various breast cancer cell lines. Our results demonstrate that RNV66 efficiently inhibits breast cancer cell proliferation both in vitro and in vivo. Introduction of LNA nucleotides were crucial for higher efficacy. Furthermore, the binding interaction of RNV66 with VEGF was investigated using molecular dynamic simulations leading to the first computational model of the LNA aptamer-VEGF complex blocking its interaction with VEGF-receptor.

Inhibitor ranking through QM based chelation calculations for virtual screening of HIV-1 RNase H inhibition.

Quantum mechanical (QM) calculations have been used to predict the binding affinity of a set of ligands towards HIV-1 RT associated RNase H (RNH). The QM based chelation calculations show improved binding affinity prediction for the inhibitors compared to using an empirical scoring function. Furthermore, full protein fragment molecular orbital (FMO) calculations were conducted and subsequently analysed for individual residue stabilization/destabilization energy contributions to the overall binding affinity in order to better understand the true and false predictions. After a successful assessment of the methods based on the use of a training set of molecules, QM based chelation calculations were used as filter in virtual screening of compounds in the ZINC database. By this, we find, compared to regular docking, QM based chelation calculations to significantly reduce the large number of false positives. Thus, the computational models tested in this study could be useful as high throughput filters for searching HIV-1 RNase H active-site molecules in the virtual screening process.

Development of an Efficient G-Quadruplex-Stabilised Thrombin-Binding Aptamer Containing a Three-Carbon Spacer Molecule

The thrombin‐binding aptamer (TBA), which shows anticoagulant properties, is one of the most studied G‐quadruplex‐forming aptamers. In this study, we investigated the impact of different chemical modifications such as a three‐carbon spacer (spacer‐C3), unlocked nucleic acid (UNA) and 3′‐amino‐modified UNA (amino‐UNA) on the structural dynamics and stability of TBA. All three modifications were incorporated at three different loop positions (T3, T7, T12) of the TBA G‐quadruplex structure to result in a series of TBA variants and their stability was studied by thermal denaturation; folding was studied by circular dichroism spectroscopy and thrombin clotting time. The results showed that spacer‐C3 introduction at the T7 loop position (TBA‐SP7) significantly improved stability and thrombin clotting time while maintaining a similar binding affinity as TBA to thrombin. Detailed molecular modelling experiments provided novel insights into the experimental observations, further supporting the efficacy of TBA‐SP7. The results of this study could provide valuable information for future designs of TBA analogues with superior thrombin inhibition properties.

The KNIME Based Classification Models for Yellow Fever Virus Inhibition

Yellow fever is one of the virus-infected diseases spread through mosquitoes and kills more than thirty thousand people every year. Although a large number of compounds have been reported, none of the drugs have yet been approved for clinical use. In the process of drug development against yellow fever virus (YFV), in the present investigation, we have developed efficient classification models based on a large dataset (309 compounds) compiled from the ChEMBL database. The Naive Bayes method as implemented in the KNIME platform was used for the classification analysis. The best models obtained using the combined dataset show accuracy of >90% on the test set prediction (Matthews correlation coefficients of >0.7). All the models developed in this study could be applicable for virtual screening of yellow fever virus inhibition.

Computational Investigation of Locked Nucleic Acid (LNA) Nucleotides in the Active Sites of DNA Polymerases by Molecular Docking Simulations

Aptamers constitute a potential class of therapeutic molecules typically selected from a large pool of oligonucleotides against a specific target. With a scope of developing unique shorter aptamers with very high biostability and affinity, locked nucleic acid (LNA) nucleotides have been investigated as a substrate for various polymerases. Various reports showed that some thermophilic B-family DNA polymerases, particularly KOD and Phusion DNA polymerases, accepted LNA-nucleoside 5′-triphosphates as substrates. In this study, we investigated the docking of LNA nucleotides in the active sites of RB69 and KOD DNA polymerases by molecular docking simulations. The study revealed that the incoming LNA-TTP is bound in the active site of the RB69 and KOD DNA polymerases in a manner similar to that seen in the case of dTTP, and with LNA structure, there is no other option than the locked C3′-endo conformation which in fact helps better orienting within the active site.

Computational Analysis of Sterol Ligand Specificity of the Niemann Pick C2 Protein

Transport of cholesterol derived from hydrolysis of lipoprotein associated cholesteryl esters out of late endosomes depends critically on the function of the Niemann Pick C1 (NPC1) and C2 (NPC2) proteins. Both proteins bind cholesterol but also various other sterols and both with strongly varying affinity. The molecular mechanisms underlying this multiligand specificity are not known. On the basis of the crystal structure of NPC2, we have here investigated structural details of NPC2-sterol interactions using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations. We found that an aliphatic side chain in the sterol ligand results in strong binding to NPC2, while side-chain oxidized sterols gave weaker binding. Estradiol and the hydrophobic amine U18666A had the lowest affinity of all tested ligands and at the same time showed the highest flexibility within the NPC2 binding pocket. The binding affinity of all ligands correlated highly with their calculated partitioning coefficient (logP) between octanol/water phases and with the potential of sterols to stabilize the protein backbone. From molecular dynamics simulations, we suggest a general mechanism for NPC2 mediated sterol transfer, in which Phe66, Val96, and Tyr100 act as reversible gate keepers. These residues stabilize the sterol in the binding pose via π-π stacking but move transiently apart during sterol release. A computational mutation analysis revealed that the binding of various ligands depends critically on the same specific amino acid residues within the binding pocket providing shape complementary to sterols, but also on residues in distal regions of the protein.

Dual mechanism of HIV-1 integrase and RNase H inhibition by diketo derivatives – a computational study

Development of novel therapeutics for treatment of HIV infections is a very challenging process due to the high rate of viral mutation. On this basis, inhibition of more than one HIV replication pathway is a potential efficient way to obtain control over the HIV progression. In the present study we have performed computational analyses in order to investigate the dual inhibitory action of a set of diketo derivatives (carboxylic acid and esters) against RNase H (RNH) and integrase (IN). Docking studies performed with these compounds revealed that the interaction between the ligands and magnesium ions and the surrounding amino acids/water within the protein are important for the dual inhibitory activity of these compounds. Moreover, from a binding mode analysis, the carboxylic acid (series 8) and ester (series 7) derivatives showed distinct binding patterns in RNH and IN, meaning that all compounds bind with magnesium ions through oxygen atoms of the ligands (either enol or carboxylate); however, the orientation of the hydrophobic tail of the ligand is quite different in both systems. Additional validation using a small dataset also strengthens this binding mode hypothesis. The results reported here could be useful for design or screening of small molecules against IN and RNH activity for the development of effective drugs for HIV treatment.

Synthesis, biological evaluation and molecular modelling studies of 4-anilinoquinazoline derivatives as protein kinase inhibitors

A series of novel 4-anilinoquinazoline derivatives (3a-3j) has been synthesized and evaluated as potential inhibitors for protein kinases implicated in Alzheimers disease. Among all the synthesized compounds, compound 3e (N-(3,4-dimethoxyphenyl)-6,7-dimethoxyquinazolin-4-amine) exhibited the most potent inhibitory activity against CLK1 and GSK-3α/β kinase with IC₅₀ values of 1.5 μM and 3 μM, respectively. Docking studies were performed to elucidate the binding mode of the compounds to the active site of CLK1 and GSK-3β. The results of our study suggest that compound 3e may serve as a valuable template for the design and development of dual inhibitors of CLK1 and GSK-3α/β enzymes with potential therapeutic application in Alzheimers disease.

HIV-1 Non-Nucleoside Reverse Transcriptase Inhibitors: SAR and Lead Optimization Using CoMFA and CoMSIA Studies (1995-2016)

BACKGROUND Design of inhibitors for HIV-1 reverse transcriptase inhibition (HIV-1 RT) is one of the successful chemotherapies for the treatment of HIV infection. Among the inhibitors available for HIV-1 RT, non-nucleoside reverse transcriptase inhibitors (NNRTIs) have shown to be very promising and clinically approved drugs. However, the efficiency of many of these drugs has been reduced by the drug-resistant variants of HIV-1 RT. The aim of the current review is to provide a summary of lead optimization strategies from the 3D-QSARs studies on NNRTI class from the past 21 years (1995 to 2016). METHODS The conformation dependent-alignment based (CoMFA and CoMSIA) methods have been proven very successful ligand based strategy in the drug design. Here, CoMFA and CoMSIA studies reported for structurally distinct NNRTIs including thiazolobenzimidazole, dipyridodiazepinone, 1,1,3-trioxo [1,2,4]-thiadiazine, formimidoester disulfides, thiocarbamate, thiazolidinone derivatives, etc. have been discussed in detail. In addition, we explore the position of the functional groups that drive the protein-ligand interaction. RESULTS The structure-activity relationship (SAR) revealed from CoMFA and CoMSIA studies of these drug classes is not only in agreement with the structure-based method but also provides an efficient way of lead optimization. In addition to molecular docking experiments, protein-ligand interaction fingerprints were calculated in order to understand the common binding mode of NNRTI compounds. CONCLUSION Overall, this review enlightens the protein-ligand interactions with a detailed SAR discussion for chemotypes. Such discussion will help medicinal chemist to gain a better understanding for the design of novel and promising NNRTI candidates.