Structure-based and ligand-based virtual screening of novel methyltransferase inhibitors of the dengue virus.

The dengue virus is the most significant arthropod-borne human pathogen, and an increasing number of cases have been reported over the last few decades. Currently neither vaccines nor drugs against the dengue virus are available. NS5 methyltransferase (MTase), which is located on the surface of the dengue virus and assists in viral attachment to the host cell, is a promising antiviral target. In order to search for novel inhibitors of NS5 MTase, we performed a computer-aided virtual screening of more than 5 million commercially available chemical compounds using two approaches: i) structure-based screening using the crystal structure of NS5 MTase and ii) ligand-based screening using active ligands of NS5 MTase. Structure-based screening was performed using the LIDAEUS (LIgand Discovery At Edinburgh UniverSity) program. The ligand-based screening was carried out using the EDULISS (EDinburgh University LIgand Selection System) program. The selection of potential inhibitors of dengue NS5 MTase was based on two criteria: the compounds must bind to NS5 MTase with a higher affinity than that of active NS5 MTase ligands, such as ribavirin triphosphate (RTP) and S-adenosyl-L-homocysteine (SAH); and the compounds must interact with residues that are catalytically important for the function of NS5 MTase. We found several compounds that bind strongly to the RNA cap site and the S-adenosyl-L-methionine (SAM) binding site of NS5 MTase with better binding affinities than that of RTP and SAH. We analyzed the mode of binding for each compound to its binding site, and our results suggest that all compounds bind to their respective binding sites by interacting with, and thus blocking, residues that are vital for maintaining the catalytic activity of NS5 MTase. We discovered several potential compounds that are active against dengue virus NS5 MTase through virtual screening using structure-based and ligand-based methods. These compounds were predicted to bind into the SAM binding site and the RNA cap site with higher affinities than SAH and RTP. These compounds are commercially available and can be purchased for further biological activity tests

Computational drug discovery for the Zika virus

Few Zika virus (ZIKV) outbreaks had been reported since its first detection in 1947, until the recent epidemics occurred in South America (2014/2015) and expeditiously became a global public health emergency. This arbovirus reached 0.5-1.3 million cases of ZIKV infection in Brazil in 2015 and rapidly spread in new geographic areas such as the Americas. Despite the mild symptoms of the Zika fever, the major concern is related to the related severe neurological disorders, especially microcephaly in newborns. Advances in ZIKV drug discovery have been made recently and constitute promising approaches to ZIKV treatment. In this review, we summarize current computational drug discovery efforts and their applicability to discovery of anti-ZIKV drugs. Lastly, we present successful examples of the use of computational approaches to ZIKV drug discovery

The methyltransferase and helicase enzymes as therapeutic targets of Zika virus : a bio- computational analysis of interactions with potential inhibitors.

Doctoral of Philosophy in Pharmaceutical Sciences. University of KwaZulu-Natal, Westville, 2019.The rampant Zika virus has received worldwide attention after becoming a global crisis following the Brazilian epidemic in 2015. From an obscure and neglected pathogen, Zika virus is now a notorious virus associated with neurological disorders in infants and adults. Since 2016, the rapid research response from the global scientific community have led to the discovery of numerous potential small molecule inhibitors and vaccines against the Zika virus. Although, in spite of this massive research initiative, there is still no effective antiviral nor vaccine that has made it out of clinical trials. The design and development of new chemical entities demands excessive cost, time and resources. Therefore, this study applies computer-aided drug design techniques, which accelerates the rational drug design process. Computational approaches including molecular docking, virtual screening, molecular modeling and molecular dynamics facilitate the filtration of large databases of compounds to sift out potential lead compounds. Furthermore, research has dedicated several resources toward FDA-approved drug repurposing. Generally, drugs have similar effects on viruses of the same family; hence drugs that have previously been effective in treating other flaviviruses, such as Dengue virus and West Nile virus, are being tested for its potential inhibition of Zika virus. However, the ability of these drugs to pass the bloodbrain barrier to treat infected neurons poses a challenge to anti-Zika virus drug discovery. This study proposes innovative strategies to design drugs that are capable of passing the blood-brain barrier, and to be able to use drugs that are impermeable via drug delivery mechanisms. This study also assesses the bioavailability and blood-brain barrier permeability of screened drugs to scrutinize the list of potential Zika virus inhibitors. Apart from identifying potential inhibitors, understanding the structural dynamics of viral targets and molecular mechanisms underlying potential inhibition of the virus is imperative. This study explores the structural and molecular dynamics of key targets of the Zika virus, the NS3 helicase and the NS5 methyltransferase enzymes, using computational approaches mentioned above and several others elaborated in this thesis. These computational methods also allowed the identification of precise interactions, amino acid residues, inhibitory mechanisms and pharmacophoric features involved in binding of lead compounds to these enzymes. IX Chapter 4 represents the first study of this thesis, which presents a concise literature background of Zika virus and identifies blood-brain barrier permeability as a core challenge in anti-Zika virus drug development. This study also provides approaches that may enable researchers to create effective anti-Zika virus drugs. Chapter 5 is the subsequent study of this thesis, which applies molecular dynamics to comparatively investigate the mechanism of inhibition and binding mode of two potential inhibitors, sinefungin and compound 5, to the NS5 methyltransferase. The specific pharmacophoric moieties of the most stable inhibitor are also identified in this study. Chapter 6 is the final study of this thesis, which examines the structural dynamics of the Zika virus NS3 helicase enzyme upon binding of ATPase inhibitor and flavivirus lead compound, resveratrol, and reports the key interactions and amino acid residues of the NS3 helicase that contribute highly to binding of resveratrol. This thesis presents an all-inclusive in silico assessment to advance research in drug design and development of Zika virus inhibitors, thus providing a greater understanding of the structural dynamics that occur in unbound and inhibitor-bound Zika virus target enzymes. Therefore, the constituents of this thesis are considered an essential platform in the progression of research toward anti-ZIKV drug design, discovery and delivery against Zika virus

Virtual screening for potential inhibitors of human hexokinase II for the development of anti-dengue therapeutics

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Hu-man hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Al-pha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we pro-posed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity

A COMPUTATIONAL APPROACH ON UNDERSTANDING STRUCTURAL INTERACTIONS OF ENVELOPE PROTEIN OF DENGUE VIRUS BOUND WITH SQUALENE, A PROTOTYPE ANTI-VIRAL COMPOUND

Objective: The objective of the work was to validate the structural binding affinity of Squalene with the envelope protein of Dengue virus by means of molecular simulations. Methods: Three-dimensional (3D) structure of dengue 2 virus envelope protein was retrieved from Protein Data Bank PDB and Squalene compound from the ZINC database. Molecular docking between the E protein and Squalene were carried out by means of Auto Dock 4.2. Results: Based on the study, it was observed that the binding/docking energy for the complex structure was calculated to be-5.55 kcal/mol. Critical residues to interact with E protein were scrutinized by analyzing the interface of the complex within 4 Å proximity. Residues such as Thr 48, Glu49, Ala 50, Val 130, Leu 135, Ser 186, Pro 187, Thr 189, Gly 190, Leu 191, Phe 193, Leu 198, Leu 207, Thr 268, Phe 279, Thr 280, Gly 281, His 282 and Leu 283 were found to be non-covalently located around the squalene. Conclusion: Scopes to design de novo anti-viral compounds to the dengue viruses by using squalene as a new class of template structure have also been concisely brought into fore

Virtual screening for potential inhibitors of human hexoki-nase ii for the development of anti-dengue therapeutics

Dengue fever, which is a disease caused by the dengue virus (DENV), is a major unsolved issue in many tropical and sub-tropical regions of the world. The absence of treatment that effectively prevent further viral propagation inside the human’s body resulted in a high number of deaths globally each year. Thus, novel anti-dengue therapies are required for effective treatment. Hu-man hexokinase II (HKII), which is the first enzyme in the glycolytic pathway, is an important drug target due to its significant impact on viral replication and survival in host cells. In this study, 23.1 million compounds were computationally-screened against HKII using the Ultrafast Shape Recognition with a CREDO Atom Types (USRCAT) algorithm. In total, 300 compounds with the highest similarity scores relative to three reference molecules, known as Al-pha-D-glucose (GLC), Beta-D-glucose-6-phosphate (BG6), and 2-deoxyglucose (2DG), were aligned. Of these 300 compounds, 165 were chosen for further structure-based screening, based on their similarity scores, ADME analysis, the Lipinski’s Rule of Five, and virtual toxicity test results. The selected analogues were subsequently docked against each domain of the HKII structure (PDB ID: 2NZT) using AutoDock Vina programme. The three top-ranked compounds for each query were then selected from the docking results based on their binding energy, the number of hydrogen bonds formed, and the specific catalytic residues. The best docking results for each analogue were observed for the C-terminus of Chain B. The top-ranked analogues of GLC, compound 10, compound 26, and compound 58, showed predicted binding energies of −7.2, −7.0, and −6.10 kcal/mol and 7, 5, and 2 hydrogen bonds, respectively. The analogues of BG6, compound 30, compound 36, and compound 38, showed predicted binding energies of −7.8, −7.4, and −7.0 kcal/mol and 11, 9, and 5 hydrogen bonds, while the top three analogues of 2DG, known as compound 1, compound 4, and compound 31, showed predicted binding energies of −6.8, −6.3, and −6.3 kcal/mol and 4, 3, and 1 hydrogen bonds, sequentially. The highest-ranked compounds in the docking analysis were then selected for molecular dynamics simulation, where compound 10, compound 30, and compound 1, which are the analogues of GLC, BG6, and 2DG, have shown strong protein-ligand stability with an RMSD value of ±5.0 A° with a 5 H bond, ±4.0 A° with an 8 H bond, and ±0.5 A° with a 2 H bond, respectively, compared to the reference molecules throughout the 20 ns simulation time. Therefore, by using the computational studies, we pro-posed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity

The in silico investigation of pharmacological targets of the zika virus : insights into the structural characteristics of the NS5 and NS3 proteins from atomistic molecular simulations.

Doctor of Philosophy in Pharmacological Science. University of KwaZulu-Natal, Durban 2017.The re-emerging Zika virus has evolved into a catastrophic epidemic during the past year, with an estimated 1.5 million reported cases of Zika infections worldwide, since the 2015 outbreak in Brazil. The virus has received considerable attention during 2016 with a flood of new discoveries, from evolving modes of viral transmission to viral-linked neurological disorders, unique specificity to host cells and increasing mutation rates. However, prior to the devastating 2015 outbreak in Brazil, the virus was classified as a neglected pathogen similar to Dengue and the West Nile virus. Despite the wide-scale research initiative, there is still no cure for the virus. There are currently vaccine clinical trials that are on-going but there has not been a breakthrough with regard to small molecule inhibitors. A lot of experimental resources have been allocated to repuposing FDA-approved drugs as possible inhibitors, however, even some of the most potent flavivirus inhibitors have adverse toxic effects. The first crystal structure of the zika virus was released in May 2016 and since then, six viral protein structures have been made available. Due to this lack in structural information, there is little known regarding the structural dynamics, active binding sites and the mechanism of inhibition of ZIKV enzymes. This study delves into the structural characteristics of three of the most crucial enzymatic targets of the zika virus, the NS5 RNA-dependent RNA polymerase and Methyltransferase as well as the NS3 Helicase. With emerging diseases, such as ZIKV, computational techniques including molecular modeling and docking, virtual screening and molecular dynamic simulations have allowed chemists to screen millions of compounds and thus funnel out possible lead drugs. These in silico approaches have warranted Computer-Aided Drug Design as a cost-effective strategy to fast track the drug discovery process. The The above techniques, amongst numerous other computational tools were employed in this study to provide insights into conformational changes that elucidate potential inhibitory mechanisms, active site identification and characterization and pharmacophoric features leading to promising small molecule inhibitor cadidates. The first study (Chapter 4), provided a comprehensive review on potential host/viral targets as well as provided a concise route map depicting the steps taken toward identifying potential inhibitors of drug targets when no crystal structure is available. A homology model case study, of the NS5 viral protein, was also demonstrated. The second study (Chapter 5) used the validated NS5 homology model to investigate the active sites at both the RNA-dependent RNA polymerase and Methyltransferase domains and subsequently employ a generated pharmacophore model to screen for potential inhibitors. Chapter 6 reports the third study, which investigates the structural dynamics and in turn, the possible mechanism of inhibition of the ZIKV NS3 Helicase enzyme when bound to ATP-competitive inhibitor, NITD008. The study also provides insight on the binding mode at the ATPase active site, thus assisting in the design of effective inhibitors against this detrimental viral target. Chapter 7 maps out the binding landscape of the ATPase and ssRNA site by demonstrating the chemical characteristics of potent flavivirus lead compounds, Lapachol, HMC-HO1α and Ivermectin at the respective NS3 Helicase binding sites. This study offers a comprehensive in silico perspective to fill the gap in drug design research against the Zika virus, thus giving insights toward the structural characteristics of pivotal targets and describing promising drug candidates. To this end, the work presented in this study is considered to be a fundamental platform in the advancements of research toward targeted drug design/delivery against ZIKV

THE COMPUTATION OF CYCLIC PEPTIDE WITH PROLIN-PROLIN BOND AS FUSION INHIBITOR OF DENV ENVELOPE PROTEIN THROUGH MOLECULAR DOCKING AND MOLECULAR DYNAMICS SIMULATION

A disease that caused by dengue virus (DENV) has become the major health problem of the world. Nowadays, no effective treatment is available to overcome the disease due to the level of dengue virus pathogeneses. A novel treatment method such as antiviral drug is highly necessary for coping with the dengue disease. Envelope protein is one of the non-structural proteins of DENV, which engaged in the viral fusion process. It penetrates into the host cell to transfer its genetic material into the targeted cell followed by replication and establishment of new virus. Thus, the envelope protein can be utilized as the antiviral inhibitor target. The fusion process is mediated by the conformational change in the protein structure from dimer to trimer state. The previous research showed the existing cavity on the dimer structure of the envelope protein. The existing ligand could get into cavity of the envelope protein, stabilize the dimer structure or hamper the transition of dimer protein into trimer. In this fashion, the fusion process can be prevented. The aim of this research is designing the cyclic peptide with prolin-prolin bond as fusion inhibitor of DENV envelope protein through molecular docking and molecular dynamics simulation. The screening of 3,883 cyclic peptides, each of them connected by prolin-prolin bond, through molecular docking resulted in five best ligands. The result showed that PYRRP was the best ligand. PAWRP was also chosen as the best ligand because it showed good affinity with protein cavity. Stability of ligand-protein complex was analyzed by molecular dynamics simulation. The result showed that PYRRP ligand was able to support the stability of DENV envelope protein dimer structure at 310 K and 312 K. While PAWRP ligand actively formed complex with the DENV envelope protein at 310 K compared to 312 K. Thus the PYRRP ligand has a potential to be developed as DENV fusion inhibitor.Keywords: dengue, envelope protein, fusion process, cavity, cyclic peptide, molecular docking, molecular dynamic

In silico Strategies to Support Fragment-to-Lead Optimization in Drug Discovery.

Fragment-based drug (or lead) discovery (FBDD or FBLD) has developed in the last two decades to become a successful key technology in the pharmaceutical industry for early stage drug discovery and development. The FBDD strategy consists of screening low molecular weight compounds against macromolecular targets (usually proteins) of clinical relevance. These small molecular fragments can bind at one or more sites on the target and act as starting points for the development of lead compounds. In developing the fragments attractive features that can translate into compounds with favorable physical, pharmacokinetics and toxicity (ADMET-absorption, distribution, metabolism, excretion, and toxicity) properties can be integrated. Structure-enabled fragment screening campaigns use a combination of screening by a range of biophysical techniques, such as differential scanning fluorimetry, surface plasmon resonance, and thermophoresis, followed by structural characterization of fragment binding using NMR or X-ray crystallography. Structural characterization is also used in subsequent analysis for growing fragments of selected screening hits. The latest iteration of the FBDD workflow employs a high-throughput methodology of massively parallel screening by X-ray crystallography of individually soaked fragments. In this review we will outline the FBDD strategies and explore a variety of in silico approaches to support the follow-up fragment-to-lead optimization of either: growing, linking, and merging. These fragment expansion strategies include hot spot analysis, druggability prediction, SAR (structure-activity relationships) by catalog methods, application of machine learning/deep learning models for virtual screening and several de novo design methods for proposing synthesizable new compounds. Finally, we will highlight recent case studies in fragment-based drug discovery where in silico methods have successfully contributed to the development of lead compounds

Delving into dengue virus drug discovery- insights into the structural characteristics of the RNA-dependent RNA polymerase.

Masters Degrees (Pharmaceutical Sciences). University of KwaZulu-Natal. Westville, 2017.A precipitous increase in the number of flaviviral infections has been noted over the last five years. The present study sought to investigate a notorious flavivirus that has been in circulation for over 30 years. Over the last few decades, DENV has re-emerged in various serotypes and is causing mayhem in the lives of many. Dengue is dreaded for the severe fever it causes in its advanced stage. Dengue has the reputation of what is known as Dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS). Dengue remains an unmet medical need that demands prompt attention. There remains no cure or preventative therapy due to the intransigence nature of this flavivirus. Its tenacity to resist antiviral therapy has left the scientific community with the burden of finding new and accelerated techniques to curb this virus. The onus is on scientists to probe further into understanding the Dengue virus by the use of cheminformatics and bioinformatics tools in the pursuit for an inhibitor against this pernicious virus. Of the Dengue structural and non-structural enzymes, the NS5 RNA-dependent RNA polymerase has been established as a promising target due to its conserved structure amongst all serotypes and its lack of an enzymatic counterpart in mammalian cells. Attempts have been made to design vaccines and small drug molecules as potential inhibitors against DENV. The virus however is resilient, and exists in 5 serotypes with numerous strains under them, thwarting the efforts of researchers to curb its spread. This prompted us to design a study that would address the above challenges by use of CADD tools, which elaborated on the design of target-specific inhibitors of DENV from an atomistic perspective. This included a pharmacophoric approach, which utilized computational software to map out a pharmacophore model against multiple flaviviruses, as well as a focused review on DENV serotype 2 and 3, which included a route map toward the design of target-specific DENV RdRp inhibitors. We believe that these findings will aid in mitigating the effects of the DENV in the lives of compromised individuals, as well as prevent the transmission of DENV from patients to healthy individuals