Targeting Dengue Virus NS-3 Helicase by Ligand based Pharmacophore Modeling and Structure based Virtual Screening

3D-QSAR and molecular docking studies of peptide-hybrids as dengue virus NS2B/NS3 protease inhibitors

Global warming and climate change have made dengue disease a global health issue. More than 50 % of the world's population is at danger of dengue virus (DENV) infection, according to the World Health Organization (WHO). Therefore, a clinically approved dengue fever vaccination and effective treatment are needed. Peptide medication development is new pharmaceutical research. Here we intend to recognize the structural features inhibiting the DENV NS2B/NS3 serine protease for a series of peptide-hybrid inhibitors (R1-R2-Lys-R3-NH2) by the 3D-QSAR technique. Comparative molecular field analysis (q2 = 0.613, r2 = 0.938, r2pred = 0.820) and comparative molecular similarity indices analysis (q2 = 0.640, r2 = 0.928, r2pred = 0.693) were established, revealing minor, electropositive, H-bond acceptor groups at the R1 position, minor, electropositive, H-bond donor groups at the R2 position, and bulky, hydrophobic groups at the R3 position for higher inhibitory activity. Docking studies revealed extensive H-bond and hydrophobic interactions in the binding of tripeptide analogues to the NS2B/NS3 protease. This study provides an insight into the key structural features for the design of peptide-based inhibitors of DENV NS2B/NS3 protease.

Discovery of a potent inhibitor, D-132, targeting AsfvPolX, via protein-DNA complex-guided pharmacophore screening and in vitro molecular characterizations

The heightened transmissibility and capacity of African swine fever virus (ASFV) induce fatal diseases in domestic pigs and wild boars, posing significant economic repercussions and global threats. Despite extensive research efforts, the development of potent vaccines or treatments for ASFV remains a persistent challenge. Recently, inhibiting the AsfvPolX, a key DNA repair enzyme, emerges as a feasible strategy to disrupt viral replication and control ASFV infections. In this study, a comprehensive approach involving pharmacophore-based inhibitor screening, coupled with biochemical and biophysical analyses, were implemented to identify, characterize, and validate potential inhibitors targeting AsfvPolX. The constructed pharmacophore model, Phar-PolX-S, demonstrated efficacy in identifying a potent inhibitor, D-132 (IC50 = 2.8 ± 0.2 µM), disrupting the formation of the AsfvPolX-DNA complex. Notably, D-132 exhibited strong binding to AsfvPolX (KD = 6.9 ± 2.2 µM) through a slow-on-fast-off binding mechanism. Employing molecular modeling, it was elucidated that D-132 predominantly binds in-between the palm and finger domains of AsfvPolX, with crucial residues (R42, N48, Q98, E100, F102, and F116) identified as hotspots for structure-based inhibitor optimization. Distinctively characterized by a 1,2,5,6-tetrathiocane with modifications at the 3 and 8 positions involving ethanesulfonates, D-132 holds considerable promise as a lead compound for the development of innovative agents to combat ASFV infections.

Al-Dosari M, Sinha N, Parvez MK, Sehgal D. Methotrexate, an anti-inflammatory drug, inhibits Hepatitis E viral replication

Hepatitis E Virus (HEV) is a positively oriented RNA virus having a 7.2 kb genome. HEV consists of three open reading frames (ORF1-3). Of these, ORF1 codes for the enzymes Methyltransferase (Mtase), Papain-like cysteine protease (PCP), RNA helicase, and RNA-dependent RNA polymerase (RdRp). Unavailability of a vaccine or effective drug against HEV and considering the side effects associated with the off-label use of ribavirin (RBV) and pegylated interferons, an alternative approach is required by the modulation of specific enzymes to prevent the infection. HEV helicase is involved in unwinding the double-stranded RNA, RNA processing, transcriptional regulation, and pre-mRNA processing. Therefore, we screened FDA-approved compounds from the ZINC15 database against the modelled 3D structure of HEV helicase and found that methotrexate and compound A (Pubchem ID BTB07890) inhibit the NTPase and dsRNA unwinding activity leading to inhibition of HEV RNA replication. This may be further authenticated by in vivo study.

Effect of Sauropus androgynus L. Merr. on dengue virus-2: An in vitro and in silico study

ETHNOPHARMACOLOGICAL RELEVANCE

Sauropus androgynus L. Merr. (Euphorbiaceae) commonly known as "multigreen" and "multivitamin" is consumed as a vegetable and used in traditional medicine to relieve fever.

AIM OF THE STUDY

This in vitro study is aimed to explore the activities of the lipophilic fraction of the leaves of S. androgynus (LFSA) against dengue (DENV), chikungunya (CHIKV) viruses and malaria (P. falciparum strain 3D7) parasite.

MATERIALS AND METHODS

The LFSA was analyzed by using GC-FID and GC-MS. The antiviral activity of LFSA was studied using the Vero CCL-81 cell line. The cytotoxicity assay was performed using 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT). Focus forming unit (FFU), cell-based immunofluorescence (IFA) assays, and quantitative RT-PCR, were used to determine and confirm antiviral activity against DENV and CHIKV. The antiparasitic activity of LFSA was carried out against P. falciparum strain 3D7 grown in fresh O+ human erythrocytes culture.

RESULTS

Twelve compounds were identified in LFSA using GC/MS. The most abundant compound was squalene (36.9%), followed by vitamin E (12.5%) and linolenic acid (10.2%). Significant reduction in DENV titre was observed under pre- and post-infection treatment conditions at a concentration of 31.25 μg/ml, but no anti-malarial and anti-CHIKV activity was observed. The Autodock-Vina-based in-silico docking study revealed that β-sitosterol could form a strong interaction with the DENV E glycoprotein.

CONCLUSION

Our findings suggest that LFSA can inhibit DENV infection and might act as a potent prophylactic/therapeutic agent against DENV-2. In-silico results suggested that β-sitosterol may block the viral entry by inhibiting the fusion process.

Virtual screening of bioactive anti-SARS-CoV natural products and identification of 3β,12-diacetoxyabieta-6,8,11,13-tetraene as a potential inhibitor of SARS-CoV-2 virus and its infection related pathways by MD simulation and network pharmacology

Since the first prevalence of COVID-19 in 2019, it still remains the most devastating pandemic throughout the world. The current research aimed to find potential natural products to inhibit the novel coronavirus and associated infection by MD simulation and network pharmacology approach. Molecular docking was performed for 39 natural products having potent anti-SARS-CoV activity. Five natural products showed high binding interaction with the viral main protease for the SARS-CoV-2 virus, where 3β,12-diacetoxyabieta-6,8,11,13 tetraene showed stable binding in MD simulation until 100 ns. Both 3β,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A targeted 11 common genes that are related to COVID-19 and interact with each other. Gene ontology development analysis further showed that all these 11 genes are attached to various biological processes. The KEGG pathway analysis also showed that the proteins that are targeted by 3β,12-diacetoxyabieta-6,8,11,13 tetraene and tomentin A are associated with multiple pathways related to COVID-19 infection. Furthermore, the ADMET and MDS studies reveals 3β,12-diacetoxyabieta-6,8,11,13 as the best-suited compound for oral drug delivery.Communicated by Ramaswamy H. Sarma.

Pharmacophore-based virtual screening and in-silico study of natural products as potential DENV-2 RdRp inhibitors

Dengue fever is a significant public health concern throughout the world, causing an estimated 500,000 hospitalizations and 20,000 deaths each year, despite the lack of effective therapies. The DENV-2 RdRp has been identified as a potential target for the development of new and effective dengue therapies. This research's primary objective was to discover an anti-DENV inhibitor using in silico ligand- and structure-based approaches. To begin, a ligand-based pharmacophore model was developed, and 130 distinct natural products (NPs) were screened. Docking of the pharmacophore-matched compounds were performed to the active site of DENV-2 RdRp protease . Eleven compounds were identified as potential DENV-2 RdRp inhibitors based on docking energy and binding interactions. ADMET and drug-likeness were done to predict their pharmacologic, pharmacokinetic, and drug-likeproperties . Compounds ranked highest in terms of pharmacokinetics and drug-like appearances were then subjected to additional toxicity testing to determine the leading compound. Additionally, MD simulation of the lead compound was performed to confirm the docked complex's stability and the binding site determined by docking. As a result, the lead compound (compound-108) demonstrated an excellent match to the pharmacophore, a strong binding contact and affinity for the RdRp enzyme, favourable pharmacokinetics, and drug-like characteristics. In summary, the lead compound identified in this study could be a possible DENV-2 RdRp inhibitor that may be further studied on in vitro and in vivo models to develop as a drug candidate.Communicated by Ramaswamy H. Sarma.

Pharmacophore-driven identification of human glutaminyl cyclase inhibitors from foods, plants and herbs unveils the bioactive property and potential of Azaleatin in the treatment of Alzheimer's disease

Alzheimer's disease (AD) is the leading cause of disabilities in old age and a rapidly growing condition in the elderly population. AD brings significant burden and has a devastating impact on public health, society and the global economy. Thus, developing new therapeutics to combat AD is imperative. Human glutaminyl cyclase (hQC), which catalyzes the formation of neurotoxic pyroglutamate (pE)-modified β-amyloid (Aβ) peptides, is linked to the amyloidogenic process that leads to the initiation of AD. Hence, hQC is an essential target for developing anti-AD therapeutics. Here, we systematically screened and identified hQC inhibitors from natural products by pharmacophore-driven inhibitor screening coupled with biochemical and biophysical examinations. We employed receptor-ligand pharmacophore generation to build pharmacophore models and Phar-MERGE and Phar-SEN for inhibitor screening through ligand-pharmacophore mapping. About 11 and 24 hits identified from the Natural Product and Traditional Chinese Medicine databases, respectively, showed diverse hQC inhibitory abilities. Importantly, the inhibitors TCM1 (Azaleatin; IC₅₀ = 1.1 μM) and TCM2 (Quercetin; IC₅₀ = 4.3 μM) found in foods and plants exhibited strong inhibitory potency against hQC. Furthermore, the binding affinity and molecular interactions were analyzed by surface plasmon resonance (SPR) and molecular modeling/simulations to explore the possible modes of action of Azaleatin and Quercetin. Our study successfully screened and characterized the foundational biochemical and biophysical properties of Azaleatin and Quercetin toward targeting hQC, unveiling their bioactive potential in the treatment of AD.

Development of a database of RNA helicase inhibitors (VHIMDB) of pathogenic viruses and in silico screening for the potential drug molecules

The pathogenic RNA virus that infects human beings contains the RNA helicase enzyme, responsible for the replication of the viral genome. The enzyme is used as a suitable target against which the drug molecule acts. Therefore, the identification and proposal the novel compounds that can be targeted toward the helicase enzymes to stop the functioning of the enzyme is desirable. Although many viral helicase inhibitor molecules have been identified, still yet no unique database is available for these compounds. This research work envisages developing a curated database of RNA helicase inhibitors. The database contains in total of 353 entries that are computationally predicted and experimentally verified RNA helicase inhibitors. The database contains information like compound name, chemical properties, chemical format, and name of the target virus to which it acts against it with a user-friendly menu-driven search engine. Presently, the database is freely available at: https://vhimdb.rsatpathy.in/. Further, in silico screening of the whole database by drug-likeness and toxicity resulted in 14 potential drug molecules. The selected molecules were analyzed for their effectiveness in binding by using molecular docking score and interaction with the helicase enzymes of three categories of pathogenic viruses (SARS-CoV-2, SARS-CoV, and MERS-CoV).

Clinical Correlation of Dengue Strains on the Basis of Seroprevalence in a Tertiary Care Hospital

Dengue viruses are icosahedral in structure and contain a single-stranded positive- sense RNA sequence of 11kb inside their capsid protein, which belongs to Flaviviridae family, genera Flavivirus. DENV a vector dependent viral virus which presents a severe health danger worldwide. Objective: To study the different strains of dengue  on the basis of serotypes Methods: A cross sectional study was conducted at a tertiary care hospital Lahore, Primary and Secondary Health Department Lahore. The blood samples of 103 patients were collected from non-random sampling technique to check out the data of different parameters such as WBC’s & Platelets through Complete Blood Count (CBC), NS1 from ELISA and nature of dengue strains through RT- PCR. Results: Of 103 positive dengue patients, there were n= 58 (33.63 ± 16.54) males and n=45 (40.64± 16.00) females. Data for the total patients is subjected to statistical differences by Paired t-test (*p<0.05). The overall percentage of dengue strains within the sample population was DENV-2 (96%), DENV-3 (2%), DENV-1 & DENV-2 (2% in females, nil in males) DENV-2 & DENV-3 (2% both in males and females). According to clinical parameters the correlation of ELISA results with WBC’s was significant (<0.0001) as well as ELISA and platelets of patients had no correlation with each other. Conclusions: Leukopenia and Thrombocytopenia is found particularly in DENV-2 strain as well as we found two different strains in two patients. So, our research work is helpful for the identification in genetic similarity of dengue strains.

Synthetic Approaches to Biologically Active C-2-Substituted Benzothiazoles

Numerous benzothiazole derivatives are used in organic synthesis, in various industrial and consumer products, and in drugs, with a wide spectrum of biological activity. As the properties of the benzothiazole moiety are strongly affected by the nature and position of substitutions, in this review, covering the literature from 2016, we focus on C-2-substituted benzothiazoles, including the methods of their synthesis, structural modification, reaction mechanisms, and possible pharmacological activity. The synthetic approaches to these heterocycles include both traditional multistep reactions and one-pot atom economy processes using green chemistry principles and easily available reagents. Special attention is paid to the methods of the thiazole ring closure and chemical modification by the introduction of pharmacophore groups.

Virtual Screening of Drug-Like Compounds as Potential Inhibitors of the Dengue Virus NS5 Protein

Dengue virus (DENV) is the causative agent of dengue fever. Annually, there are about 400 million new cases of dengue worldwide, and so far there is no specific treatment against this disease. The NS5 protein is the largest and most conserved viral protein among flaviviruses and is considered a therapeutic target of great interest. This study aims to search drug-like compounds for possible inhibitors of the NS5 protein in the four serotypes of DENV. Using a virtual screening from a ∼642,759-compound database, we suggest 18 compounds with NS5 binding and highlight the best compound per region, in the methyltransferase and RNA-dependent RNA polymerase domains. These compounds interact mainly with the amino acids of the catalytic sites and/or are involved in processes of protein activity. The identified compounds presented physicochemical and pharmacological properties of interest for their use as possible drugs; furthermore, we found that some of these compounds do not affect cell viability in Huh-7; therefore, we suggest evaluating these compounds in vitro as candidates in future research.

Discovery of 8-prenylnaringenin from hop (Humulus lupulus L.) as a potent monoacylglycerol lipase inhibitor for treatments of neuroinflammation and Alzheimer's disease

Monoacylglycerol lipase (MAGL), a serine hydrolase, converts endocannabinoid 2-arachidonoylglycerol (2-AG) to arachidonic acid (AA) and glycerol in the brain and plays a bidirectional role in controlling nueroinflammation. MAGL, involved in Alzheimer's and Parkinson's diseases, is a promising target for treatment of neurodegenerative disorders. However, the irreversible inhibitors of MAGL lead to the desensitization of CB1 receptors further impairing the benefits associated with the indirect CB1 stimulation. Therefore, development of potent reversible inhibitors from natural products (NPs) and traditional chinese medicines (TCMs) are safer and free from adverse side effects and feasible to avoid drawbacks which irreversible inhibitors cause. Here, we employed pharmacophore-based screening of drug candidates coupled with molecular docking, biochemical assay and Ligplot analyses to identify and characterize inhibitors targeting human MAGL (hMAGL). The built pharmacophore model, Phar-MAGL successfully identified inhibitors NP-2 (IC₅₀ = 9.5 ± 1.2 μM), NP-5 (IC₅₀ = 14.5 ± 1.3 μM), and NP-3 (IC₅₀ = 15.2 ± 1.4 μM), which apparently attenuated the activities of hMAGL in vitro. The evident activities of the identified inhibitors against hMAGL showed that the pharmacophore model, Phar-MAGL is reliable and efficient in screening inhibitors against hMAGL. Our study successfully identified a natrual product inhibitor, NP-2 (8-PN), from the plant Humulus lupulus L. (hops) and its positive effects in neurogenesis and neurodifferentiation along with the evident inhibitory potency against hMAGL revealed the potential for further optimizing and developing into drugs to treat neuroinflammation, Alzheimer's and Parkinson's diseases.

Discovery of natural product inhibitors against human monoacylglycerol lipase by pharmacophore-based drug screening, LibDock molecular docking and in vitro biochemical examinations.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.

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. Human 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 Alpha-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 proposed novel compounds, which may act as potential drugs against DENV by inhibiting HKII’s activity.

Benzothiazoles as potential antiviral agents

OBJECTIVES

The recent viral pandemic poses a unique challenge for healthcare providers. Despite the remarkable progress, the number of novel antiviral agents in the pipeline is woefully inadequate against the evolving virulence and drug resistance of current viruses. This highlights the urgent need for new and improved vaccines, diagnostics and therapeutic agents to obviate the viral pandemic.

KEY FINDINGS

Benzothiazole plays a pivotal role in the design and development of antiviral drugs. This is evident from the fact that it comprises many clinically useful agents. The current review is aimed to provide an insight into the recent development of benzothiazole-based antiviral agents, with a special focus on their structure-activity relationships and lead optimisation. One hundred and five articles were initially identified, and from these studies, 64 potential novel lead molecules and main findings were highlighted in this review.

SUMMARY

We hope this review will provide a logical perspective on the importance of improving the future designs of novel broad-spectrum benzothiazole-based antiviral agents to be used against emerging viral diseases.

Integrating Ligand and Target-Driven Based Virtual Screening Approaches With in vitro Human Cell Line Models and Time-Resolved Fluorescence Resonance Energy Transfer Assay to Identify Novel Hit Compounds Against BCL-2

Antiapoptotic members of B-cell leukemia/lymphoma-2 (BCL-2) family proteins are one of the overexpressed proteins in cancer cells that are oncogenic targets. As such, targeting of BCL-2 family proteins raises hopes for new therapeutic discoveries. Thus, we used multistep screening and filtering approaches that combine structure and ligand-based drug design to identify new, effective BCL-2 inhibitors from a small molecule database (Specs SC), which includes more than 210,000 compounds. This database is first filtered based on binary “cancer-QSAR” model constructed with 886 training and 167 test set compounds and common 26 toxicity quantitative structure-activity relationships (QSAR) models. Predicted non-toxic compounds are considered for target-driven studies. Here, we applied two different approaches to filter and select hit compounds for further in vitro biological assays and human cell line experiments. In the first approach, a molecular docking and filtering approach is used to rank compounds based on their docking scores and only a few top-ranked molecules are selected for further long (100-ns) molecular dynamics (MD) simulations and in vitro tests. While docking algorithms are promising in predicting binding poses, they can be less prone to precisely predict ranking of compounds leading to decrease in the success rate of in silico studies. Hence, in the second approach, top-docking poses of each compound filtered through QSAR studies are subjected to initially short (1 ns) MD simulations and their binding energies are calculated via molecular mechanics generalized Born surface area (MM/GBSA) method. Then, the compounds are ranked based on their average MM/GBSA energy values to select hit molecules for further long MD simulations and in vitro studies. Additionally, we have applied text-mining approaches to identify molecules that contain “indol” phrase as many of the approved drugs contain indole and indol derivatives. Around 2700 compounds are filtered based on “cancer-QSAR” model and are then docked into BCL-2. Short MD simulations are performed for the top-docking poses for each compound in complex with BCL-2. The complexes are again ranked based on their MM/GBSA values to select hit molecules for further long MD simulations and in vitro studies. In total, seven molecules are subjected to biological activity tests in various human cancer cell lines as well as Time-Resolved Fluorescence Resonance Energy Transfer (TR-FRET) assay. Inhibitory concentrations are evaluated, and biological activities and apoptotic potentials are assessed by cell culture studies. Four molecules are found to be limiting the proliferation capacity of cancer cells while increasing the apoptotic cell fractions.

In Silico Modeling of Crimean Congo Hemorrhagic Fever Virus Glycoprotein-N and Screening of Anti Viral Hits by Virtual Screening

Crimean-Congo hemorrhagic fever (CCHF) is a widespread zoonotic viral disease, caused by a tick-born virus Crimean-Congo hemorrhagic fever virus (CCHFV). This disease is endemic in Middle East, Asia, Africa and South-Eastern Europe with the mortality rate of 5–30%. CCHFV genome is composed of three segments: large, medium and small segments. M segment encodes a polyprotein (glycoprotein) so called glycoprotein N (Gn) which is considered as a potential druggable target for the effective therapy of CCHF. The complete structure of Gn is still not characterized. The aim of the current study is to predict the complete three-dimensional (3D-) structure of CCHFV Gn protein via threading-based modeling and investigate the residues crucial for binding with CCHFV envelop. The developed model displayed excellent stereo-chemical and geometrical properties. Subsequently structure based virtual screening (SBVS) was applied to discover novel inhibitors of Gn protein. A library of > 1300 anti-virals was selected from PubChem database and directed to the predicted binding site of Gn. The SBVS results led to the identification of thirty-seven compounds that inhibit the protein in computational analysis. Those 37 hits were subject to pharmacokinetic profiling which demonstrated that 30/37 compound possess safer pharmacokinetic properties. Thus, by specifically targeting Gn, less toxic and more potent inhibitors of CCHFV were identified in silico.

Prediction and validation of potent peptides against herpes simplex virus type 1 via immunoinformatic and systems biology approach

The human herpes simplex virus type 1 (HSV-1) is an extremely rampant human pathogen, and its infection could cause life-long diseases, including the central nervous system disorders. The glycoproteins of HSV-1 such as glycoprotein B, glycoprotein C, glycoprotein D, glycoprotein H, and glycoprotein L are highly involved in mediating the viral attachment and infection of the host cell. Therefore, immunoinformatic approaches followed by molecular dynamics simulation and systems biology has been used to analyze these glycoproteins in order to propose effective peptide-based vaccine candidates against the HSV-1 infection. The ElliPro and NetCTL.1.2 online tools were employed to forecast the B- and T-lymphocyte (CTL) epitopes for gB, gC, gD, gH, and gL. The 3D coordinates of these epitopes were modeled and docked against the human major histocompatibility complex molecule-1. The outcomes obtained from postdocking analysis along with TAP (Transporter associated with antigen processing), MHC binding, and C-terminal cleavage score assisted in the selection of potential epitopes. These epitopes were further subjected to molecular dynamics simulation and systems biology approach which showed significant results. On the basis of these substantial outcomes, peptides are proposed that could be used to provoke immunity against the HSV-1 infection.

A Multi-Perspective Review on Dengue Research

Dengue fever is a disease which is caused by a family of viruses named Flaviviridae which are transmitted by female Aedes mosquitoes. Today, this is endemic in more than 100 nations in the World Health Organization's African, Americas, Eastern Mediterranean, South-East Asia and Western Pacific locales. The treatment of typical dengue is focused on relieving the symptoms and signs. Carica papaya is a very common plant whose leaf extract is used in the treatment of this disease. Despite extensive research on Dengue, not a single vaccine or anti-viral drug was available until 2016 (a partially effective Chimeric Yellow fever virus treated by DENV-Tetravalent Dengue Vaccine for dengue fever made by Sanofi Pasteur). This review highlights dengue fever's current situation and explains the importance of Natural chemical moieties like methionine-proline anilides, tetrapeptide aldehyde uncovered via Structure Activity Relationship studies. Also, we have reviewed the drug candidates currently in the clinical trials that have the potential to solve these issues. Important patents in the past 20 years have been outlined in this review. An in depth Protein Data Bank analysis of the different possible target proteins that can potentially have a major role in curing Dengue fever has been conducted.

Homology Model and Docking-Based Virtual Screening for Ligands of Human Dyskerin as New Inhibitors of Telomerase for Cancer Treatment

Immortality is one of the main features of cancer cells. Tumor cells have an unlimited replicative potential, principally due to the holoenzyme telomerase. Telomerase is composed mainly by dyskerin (DKC1), a catalytic retrotranscriptase (hTERT) and an RNA template (hTR). The aim of this work is to develop new inhibitors of telomerase, selecting the interaction between hTR⁻DKC1 as a target. We designed two models of the human protein DKC1: homology and ab initio. These models were evaluated by different procedures, revealing that the homology model parameters were the most accurate. We selected two hydrophobic pockets contained in the PUA (pseudouridine synthase and archaeosine transglycosylase) domain, using structural and stability analysis. We carried out a docking-based virtual screen on these pockets, using the reported mutation K314 as the center of the docking. The hDKC1 model was tested against a library of 450,000 drug-like molecules. We selected the first 10 molecules that showed the highest affinity values to test their inhibitory activity on the cell line MDA MB 231 (Monroe Dunaway Anderson Metastasis Breast cancer 231), obtaining three compounds that showed inhibitory effect. These results allowed us to validate our design and set the basis to continue with the study of telomerase inhibitors for cancer treatment.