Structure based virtual screening, 3D-QSAR, molecular dynamics and ADMET studies for selection of natural inhibitors against structural and non-structural targets of Chikungunya

Virtual screening and molecular dynamics investigations using natural compounds against autotaxin for the treatment of chronic pain

Chronic pain is a common and debilitating condition with a huge social and economic burden worldwide. Currently, available drugs in clinics are not adequately effective and possess a variety of severe side effects leading to treatment withdrawal and poor quality of life. Recent findings highlight the potential role of autotaxin (ATX) as a promising novel target for chronic pain management, extending beyond its previously established involvement in arthritis and other neurological disorders, such as Alzheimer's disease. In the present study, we used a virtual screening strategy by targeting ATX against commercially available natural compounds (enamine- phenotypic screening library) to identify the potential inhibitors for the treatment of chronic pain. After initial identification using molecular docking based virtual screening, molecular mechanics (MM/GBSA), ADMET profiling and molecular dynamics simulation were performed to verify top hits. The computational screening resulted in the identification of fifteen top scoring structurally diverse hits that have free energy of binding (ΔG) values in the range of -25.792 (for compound Enamine_1850) to -74.722 Kcal/mol (for compound Enamine_1687). Moreover, the top-scoring hits have favourable ADME properties as calculated using in-silico algorithms. Additionally, the molecular dynamics simulation revealed the stable nature of protein-ligand interaction and provided information about amino acid residues involved in binding. This study led to the identification of potential autotaxin inhibitors with favourable pharmacokinetic properties. Identified hits may further be investigated for their safety and efficacy potential using in-vitro and in-vivo models of chronic pain.Communicated by Ramaswamy H. Sarma.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs.Communicated by Ramaswamy H. Sarma.

Computational Prediction of 3,5-Diaryl-1H-Pyrazole and spiropyrazolines derivatives as potential acetylcholinesterase inhibitors for alzheimer disease treatment by 3D-QSAR, molecular docking, molecular dynamics simulation, and ADME-Tox

The efficacy of 40 synthesized variants of 3,5-diaryl-1H-pyrazole and spiropyrazoline' derivatives as acetylcholinesterase inhibitors is verified using a quantitative three-dimensional structure-activity relationship (3D-QSAR) by comparative molecular field analysis (CoMFA) and molecular similarity index analysis (CoMSIA) models. In this research, different field models proved that CoMSIA/SE model is the best model with high predictive power compared to several models (Qved2 = O.65; R2 = 0.980; R2test = 0.727). Also, contour maps produced by CoMSIA/SE model have been employed to prove the key structural needs of the activity. Consequently, six new compounds have been generated. Among these compounds, M4 and M5 were the most active but remained toxic and had poor absorption capacities. While the M1, M2, M3 and M6 remained highly active while respecting ADMET's characteristics. Molecular docking results showed compound M2 better with acetylcholinesterase than compound 22. The interactions are classical hydrogen bonding with residues TYR:124, TYR:72, and SER:293, which play a critical role in the biological activity as AChE inhibitors. MD results confirmed the docking results and showed that compound M2 had satisfactory stability with (ΔGbinding = -151.225 KJ/mol) in the active site of AChE receptor compared with compound 22 (ΔGbinding = -133.375 KJ/mol). In addition, both compounds had good stability regarding RMSD, Rg, and RMSF. The previous results show that the newly designed compound M2 is more active in the active site of AChE receptor than compound 22.Communicated by Ramaswamy H. Sarma.

Pan-Genomics of Escherichia albertii for Antibiotic Resistance Profiling in Different Genome Fractions and Natural Product Mediated Intervention: In Silico Approach

Escherichia albertii is an emerging, enteric pathogen of significance. It was first isolated in 2003 from a pediatric diarrheal sample from Bangladesh. In this study, a comprehensive in silico strategy was followed to first list out antibiotic-resistant genes from core, accessory and unique genome fractions of 95 available genomes of E. albertii. Then, 56 drug targets were identified from the core essential genome. Finally, ZipA, an essential cell division protein that stabilizes the FtsZ protofilaments by cross-linking them and serves as a cytoplasmic membrane anchor for the Z ring, was selected for further downstream processing. It was computationally modeled using a threading approach, followed by virtual screening of two phytochemical libraries, Ayurvedic (n = 2103 compounds) and Traditional Chinese Medicine (n = 36,043 compounds). ADMET profiling, followed by PBPK modeling in the central body compartment, in a population of 250 non-diseased, 250 cirrhotic and 250 renally impaired people was attempted. ZINC85624912 from Chinese medicinal library showed the highest bioavailability and plasma retention. This is the first attempt to simulate the fate of natural products in the body through PBPK. Dynamics simulation of 20 ns for the top three compounds from both libraries was also performed to validate the stability of the compounds. The obtained information from the current study could aid wet-lab scientists to work on the scaffold of screened drug-like compounds from natural resources and could be useful in our quest for therapy against antibiotic-resistant E. albertii.

Drug Repurposing for Identification of S1P1 Agonists with Potential Application in Multiple Sclerosis Using In Silico Drug Design Approaches

Purpose: Drug repurposing is an approach successfully used for discovery of new therapeutic applications for the existing drugs. The current study was aimed to use the combination of in silico methods to identify FDA-approved drugs with possible S1P₁ agonistic activity useful in multiple sclerosis (MS). Methods: For this, a 3D-QSAR model for the known 21 S1P₁ agonists were generated based on 3D-QSAR approach and used to predict the possible S1P₁ agonistic activity of FDA-approved drugs. Then, the selected compounds were screened by docking into S1P₁ and S1P₃ receptors to select the S1P₁ potent and selective compounds. Further evaluation was carried out by molecular dynamics (MD) simulation studies where the S1P₁ binding energies of selected compounds were calculated. Results: The analyses resulted in identification of cobicistat, benzonatate and brigatinib as the selective and potent S1P₁ agonists with the binding energies of -85.93, -69.77 and -67.44 kcal. mol^-1, calculated using MM-GBSA algorithm based on 50 ns MD simulation trajectories. These values are better than that of siponimod (-59.35 kcal mol^-1), an FDA approved S1P₁ agonist indicated for MS treatment. Furthermore, similarity network analysis revealed that cobicistat and brigatinib are the most structurally favorable compounds to interact with S1P₁. Conclusion: The findings in this study revealed that cobicistat and brigatinib can be evaluated in experimental studies as potential S1P₁ agonist candidates useful in the treatment of MS.

Structure-based virtual screening and molecular dynamics simulation for the identification of sphingosine kinase-2 inhibitors as potential analgesics

Neuropathic pain is due to an injury or disease of the somatosensory nervous system, which accounts for a significant economical and health burden to society. Due to poor understanding of their underlying mechanisms, the available treatments merely provide symptomatic relief and precipitates a variety of adverse effects. This suggests that there is an unmet medical need that must be addressed with effective strategies for the development of novel therapeutics. Sphingosine kinase 2 (SphK2) is an oncogenic lipid kinase that has emerged as a promising target for chronic pain and other diseases. In the present study, we have explored the structure-based virtual high-throughput screening of the Nuclei of Bioassays, Ecophysiology, and Biosynthesis of Natural Products Database (NuBBE) to identify potent natural products as inhibitors of SphK2. A molecular docking study was performed to calculate binding affinities and specificity to identify potential leads against SphK2. Initially, hits were selected by the implementation of absorption, distribution, metabolism, excretion and toxicity properties, Lipinski rule, and PAINS filters. The top-scoring hits also exhibiting an optimal ADMET profile were subjected to MM/GBSA free binding free energy calculation and molecular dynamics simulation. The results from molecular dynamics simulation revealed a stable ligand -SphK2 complex with protein and ligand RMSD within reasonable limits. Overall, we identified compounds, NuBBE_972 and NuBBE_1107 as potential inhibitors of SphK2 with optimal pharmacokinetic properties which have the potential to be developed as novel therapeutics for the management of chronic pain.Communicated by Ramaswamy H. Sarma.

Virtual Combinatorial Chemistry and Pharmacological Screening: A Short Guide to Drug Design

Traditionally, drug development involved the individual synthesis and biological evaluation of hundreds to thousands of compounds with the intention of highlighting their biological activity, selectivity, and bioavailability, as well as their low toxicity. On average, this process of new drug development involved, in addition to high economic costs, a period of several years before hopefully finding a drug with suitable characteristics to drive its commercialization. Therefore, the chemical synthesis of new compounds became the limiting step in the process of searching for or optimizing leads for new drug development. This need for large chemical libraries led to the birth of high-throughput synthesis methods and combinatorial chemistry. Virtual combinatorial chemistry is based on the same principle as real chemistry—many different compounds can be generated from a few building blocks at once. The difference lies in its speed, as millions of compounds can be produced in a few seconds. On the other hand, many virtual screening methods, such as QSAR (Quantitative Sturcture-Activity Relationship), pharmacophore models, and molecular docking, have been developed to study these libraries. These models allow for the selection of molecules to be synthesized and tested with a high probability of success. The virtual combinatorial chemistry–virtual screening tandem has become a fundamental tool in the process of searching for and developing a drug, as it allows the process to be accelerated with extraordinary economic savings.

Identification of lactoferrin-derived peptides as potential inhibitors against the main protease of SARS-CoV-2

COVID-19 is a global health emergency that causes serious concerns. A global effort is underway to identify drugs for the treatment of COVID-19. One possible solution to the present problem is to develop drugs that can inhibit SARS-CoV-2 main protease (M^pro), a coronavirus protein that been considered as one among many drug targets. In this work, lactoferrin from Bos taurus L. was in silico hydrolyzed. The bioactivity, water solubility, and ADMET properties of the generated peptides were predicted using various online tools. The molecular interactions between M^pro and the peptides were studied using molecular docking and molecular dynamic simulation. The results demonstrated that peptide GSRY was predicted to have better physicochemical properties, and the value of '-C DOCKER interaction energy' between peptide GSRY and M^pro was 80.8505 kcal/mol. The interaction between the peptide GSRY and the native ligand N3 co-crystallized with M^pro had overlapped amino acids, i.e., HIS163, GlY143, GLU166, GLN189 and MET165. Molecular dynamic simulation revealed that M^pro/GSRY complexes were stable. Collectively, the peptide GSRY may be a potential candidate drug against M^pro of SARS-CoV-2.

Graph theoretical network analysis, in silico exploration, and validation of bioactive compounds from Cynodon dactylon as potential neuroprotective agents against α-synuclein

Introduction: Parkinson's disease (PD) is a chronic, devastating neurodegenerative disorder marked by the death of dopaminergic neurons in the midbrain's substantia nigra pars compacta (Snpc). In alpha-synuclein (α-Syn) self-aggregation, the existence of intracytoplasmic inclusion bodies called Lewy bodies (LBs) and Lewy neurites (LNs) causes PD, which is a cause of neuronal death. Methods: The present study is aimed at finding potential bioactive compounds from Cynodon dectylon that can degrade α-Syn aggregation in the brain, through in silico molecular docking investigations. Graph theoretical network analysis was used to identify the bioactive compounds that target α-Syn and decipher their network as a graph. From the data repository, twenty-nine bioactive chemicals from C. dactylon were chosen and their structures were retrieved from Pubchem. On the basis of their docking scores and binding energies, significant compounds were chosen for future investigation. The in silico prediction of chosen compounds, and their pharmacokinetic and physicochemical parameters were utilized to confirm their drug-likeness profile. Results: During molecular docking investigation the bioactive compounds vitexin (-7.3 kcal.mol^-1) and homoorientin (-7.1 kcal.mol^-1) showed significant binding energy against the α-Syn target protein. A computer investigation of molecular dynamics simulation study verifies the stability of the α-Syn-ligand complex. The intermolecular interactions assessed by the dynamic conditions indicate that the bioactive compound vitexin has the potency to prevent α-Syn aggregation. Conclusion: Interestingly, the observed results indicate that vitexin is a potential lead compound against α-Syn aggregation, and in vitro and in vivo studies are warranted to confirm the promising therapeutic capability.

Computational guided identification of a citrus flavonoid as potential inhibitor of SARS-CoV-2 main protease

Although vaccine development is being undertaken at a breakneck speed, there is currently no effective antiviral drug for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing COVID-19. Therefore, the present study aims to explore the possibilities offered by naturally available and abundant flavonoid compounds, as a prospective antiviral drug to combat the virus. A library of 44 citrus flavonoids was screened against the highly conserved Main Protease (Mpro) of SARS-CoV-2 using molecular docking. The compounds which showed better CDocker energy than the co-crystal inhibitor of Mpro were further revalidated by flexible docking within the active site; followed by assessment of drug likeness and toxicity parameters. The non-toxic compounds were further subjected to molecular dynamics simulation and predicted activity (IC50) using 3D-QSAR analysis. Subsequently, hydrogen bonds and dehydration analysis of the best compound were performed to assess the binding affinity to Mpro. It was observed that out of the 44 citrus flavonoids, five compounds showed lower binding energy with Mpro than the co-crystal ligand. Moreover, these compounds also formed H-bonds with two important catalytic residues His41 and Cys145 of the active sites of Mpro. Three compounds which passed the drug likeness filter showed stable conformation during MD simulations. Among these, the lowest predicted IC50 value was observed for Taxifolin. Therefore, this study suggests that Taxifolin, could be a potential inhibitor against SARS-CoV-2 main protease and can be further analysed by in vitro and in vivo experiments for management of the ongoing pandemic.

Targeting Chikungunya Virus Entry: alternatives for new inhibitors in drug discovery

Chikungunya virus (CHIKV) is an Alphavirus (Togaviridae) responsible for Chikungunya fever (CHIKF) that is mainly characterized by a severe polyarthralgia, in which it is transmitted by the bite of infected Aedes aegypti and Ae. albopictus mosquitoes. Nowadays, there no licensed vaccines or approved drugs to specifically treat this viral disease. Structural viral proteins participate in key steps of its replication cycle, such as viral entry, membrane fusion, nucleocapsid assembly, and virus budding. In this context, envelope E3-E2-E1 glycoproteins complex could be targeted for designing new drug candidates. In this review, aspects of the CHIKV entry process are discussed to provide insights to assist the drug discovery process. Moreover, several natural, nature-based and synthetic compounds, as well as repurposed drugs and virtual screening, are also explored as alternatives for developing CHIKV entry inhibitors. Finally, we provided a complimentary analysis of studies involving inhibitors that were not explored by in silico methods. Based on this, Phe118, Val179, and Lys181 were found to be the most frequent residues, being present in 89.6, 82.7, and 93.1% of complexes, respectively. Lastly, some chemical aspects associated with interactions of these inhibitors and mature envelope E3-E2-E1 glycoproteins' complex were discussed to provide data for scientists worldwide, supporting their search for new inhibitors against this emerging arbovirus.

Artificial Intelligence, Big Data and Machine Learning Approaches in Precision Medicine & Drug Discovery

Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short period. The present review is an overview based on some applications of Machine Learning based tools, such as GOLD, Deep PVP, LIB SVM, etc. and the algorithms involved such as support vector machine (SVM), random forest (RF), decision tree and Artificial Neural Network (ANN), etc. at various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure- based Virtual Screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intestinal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF models in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1, by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predict flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches have been evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery to model small-molecule drugs, gene biomarkers and identifying the novel drug targets for various diseases.

Multi-conformation representation of Mpro identifies promising candidates for drug repurposing against COVID-19

The COVID-19 main protease (Mpro), one of the conserved proteins of the novel coronavirus is crucial for its replication and so is a very lucrative drug target. Till now, there is no drug molecule that has been convincingly identified as the inhibitor of the function of this protein. The current pandemic situation demands a shortcut to quickly reach to a lead compound or a drug, which may not be the best but might serve as an interim solution at least. Following this notion, the present investigation uses virtual screening to find a molecule which is alraedy approved as a drug for some other disease but could be repurposed to inhibit Mpro. The potential of the present method of work to identify such a molecule, which otherwise would have been missed out, lies in the fact that instead of just using the crystallographically identified conformation of the receptor’s ligand binding pocket, molecular dynamics generated ensemble of conformations has been used. It implicitly included the possibilities of “induced-fit” and/or “population shift” mechanisms of ligand fitting. As a result, the investigation has not only identified antiviral drugs like ribavirin, ritonavir, etc., but it has also captured a wide variety of drugs for various other diseases like amrubicin, cangrelor, desmopressin, diosmin, etc. as the potent possibilities. Some of these ligands are versatile to form stable interactions with various different conformations of the receptor and therefore have been statistically surfaced in the investigation. Overall the investigation offers a wide range of compounds for further testing to confirm their scopes of applications to combat the COVID-19 pandemic.

Antiviral therapeutics for chikungunya virus

Introduction: Chikungunya virus (CHIKV), a reemerging human arthropod borne virus, can causes global epidemic outbreaks and has become a serious health concern due to the unavailability of any antiviral therapy/vaccine. Extensive research has been conducted to target different proteins from CHIKV to curtail the spread of virus.Areas covered: This review provides an overview of the granted patents including the current status of antiviral strategies targeting CHIKV.Expert opinion: Under the current scenario, potential molecules and different approaches have been utilized to suppress CHIKV infection. MV-CHIKV and VRC-CHKVLP059-00-VP vaccine candidates have successfully completed phase I clinical trials and ribavirin (inhibitor) has shown significant inhibition of CHIKV replication and could be the most promising candidates. The drug resistance and toxicity can be modulated by using the inhibitors/drugs in combination. Moreover, nanoparticle formulations can improve the efficacy and bioavailability of drugs.

Hit identification and drug repositioning of potential non-nucleoside reverse transcriptase inhibitors by structure-based approach using computational tools (part II)

AIDS is a global infection involving several complications and its increasing prevalence every year has prioritized our study. Therapy associated with HIV has led to emergence of multidrug resistance and toxicity. Thus, the development of a potent, affordable and safe anti-HIV drug is a global concern. Among the different targets developed, inhibition of non-nucleoside reverse transcriptase (NNRT) is found to be effective and promising. Etravirine, efavirenz, nevirapine, rilpivirine and delavirdine are the marketed NNRTIs available. This study is focused on computational prediction of hit molecules as well as repurposing of various FDA-approved drugs as potential NNRTIs. A synthetic database from ZINCpharmer, publicly available natural databases of coumarins, chromones and chalcones, and two databases of FDA-approved drugs for repurposing were screened to check for the possibility of these compounds to possess anti-HIV activity. Study utilizes a structure-based approach with the generated pharmacophore of target protein (PDB ID: 3MEC), screening of selected datasets is carried out using the Phase tool of Schrodinger. The top filtered compounds with good fitness score were proceeded to molecular docking studies to study their binding affinity to the target. Energy-based calculations using Prime MM-GBSA of Schrodinger was performed to determine free binding energy of the complexes. Prediction of pharmacokinetic parameters of top compounds is further carried out and reported. All the results obtained from different databases are compiled, interpreted and five molecules were subjected to molecular dynamic studies to further confirm the prediction and identified hit molecules for in vitro screening as potential NNRTIs.Communicated by Ramaswamy H. Sarma.

Pharmacophore modeling, molecular docking and molecular dynamics simulation for screening and identifying anti-dengue phytocompounds

Dengue is a fast spreading mosquito borne viral disease that poses a serious threat to human health. Lack of therapeutic drugs and vaccines signify that more resources need to be explored. Accumulated evidence has suggested that plants offer a vast reservoir for antiviral drug discovery which are safe for human consumption. Plant-based drug discovery is a complex and time-consuming process as plants possess rich repository of chemically diverse compounds. Various in silico methods can make this process simple and economic. We, therefore, performed pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations and ADME (absorption, distribution, metabolism, excretion) prediction to screen potential candidates against dengue. In particular, combined pharmacophore mapping and molecular docking were used to prioritize the potentially active ligands from a ligand library. Biological activities of plant based ligands were predicted using 3D-QSAR pharmacophore modeling. Interaction between proteins, namely, envelope G protein, NS2B/NS3 protease, NS5 methyltransferase, NS1, NS5 polymerase and active plant-based ligands (pIC50 > 5.1) were analyzed using molecular docking. Best docked complex, namely, envelope G protein-mulberroside C, NS2B-NS3 protease-curcumin, NS5 methyltransferase-chebulic acid, NS1-mulberroside A, NS5 methyltransferase-punigluconin and NS5 methyltransferase-chebulic acid were further subjected to MD simulations study to assess the fluctuation and conformational changes during protein-ligand interaction. ADME studies were performed to assess their drug-likeness properties. Collectively, these in silico results helped to identify the potential plant-based hits against the various receptors of dengue virus which can be further validated by bioactivity-based experiments.Communicated by Ramaswamy H. Sarma.

Characterization of medicinal plant-associated biocontrol Bacillus subtilis (SSL2) by liquid chromatography-mass spectrometry and evaluation of compounds by in silico and in vitro methods

This study explores the antimicrobial properties of bioactive secondary metabolites extracted from the medicinal plant (Solanum surattense)-associated Bacillus subtilis strain SSL2. The secondary metabolites were extracted from B. subtilis (SSL2) using ethyl acetate, acetone, butanol, chloroform and methanol solvents. The crude extract was tested against two wilt causing pathogens: Ralstonia solanacearum and Fusarium oxysporum. The results revealed that the ethyl acetate extract has maximum inhibition against both the pathogens tested in this study. Furthermore, liquid chromatography-mass spectrometry (LC-MS) analysis of ethyl acetate extract identified 80 different compounds based on mass-to-charge ratio, database difference, resolution of mass spectrum and so on. Among the 80 compounds, citrulline (m/z = 158.0917), chloramphenicol (m/z = 195.075) and carnitine (m/z 162.11) were further selected based on m/z ratio for in silico and in vitro analyses. The in silico analysis revealed that citrulline, chloramphenicol and carnitine inhibited the virulent genes phcA (R. solanacearum) and ste12 (F. oxysporum). Further, under in vitro condition, citrulline and chloramphenicol were found to inhibit the growth of R. solanacearum and F. oxysporum. On the basis of the biocontrol activity of B. subtilis (SSL2) in in silico and in vitro conditions, the bacteria could be used as a biocontrol agent against both bacterial and fungal wilt-causing pathogens. However, this needs to be tested in pot studies or field conditions before being used as biocontrol agents.Communicated by Ramaswamy H. Sarma.

Structure based virtual screening, 3D-QSAR, molecular dynamics and ADMET studies for selection of natural inhibitors against structural and non-structural targets of Chikungunya

The transmission of mosquito-borne Chikungunya virus (CHIKV) has large epidemics worldwide. Till date, there are neither anti-viral drugs nor vaccines available for the treatment of Chikungunya. Accumulated evidences suggest that some natural compounds i.e., Epigallocatechin gallate, Harringtonine, Apigenin, Chrysin, Silybin, etc. have the capability to inhibit CHIKV replication in vitro. Natural compounds are known to possess less or no side effects. Therefore, natural compound in its purified or crude extracts form could be the preeminent and safe mode of therapies for Chikungunya. Wet lab screening and identification of natural compounds against Chikungunya targets is a time consuming and expensive exercise. In the present study, we used in silico techniques like receptor-ligand docking, Molecular dynamic (MD), Three Dimensional Quantitative Structure Activity Relation (3D-QSAR) and ADME properties to screen out potential compounds. Aim of the study is to identify potential lead/s from natural sources using in silico techniques that can be developed as a drug like molecule against Chikungunya infection and replication. Three softwares were used for molecular docking studies. Potential ligands selected by docking studies were subsequently subjected 3D-QSAR studies to predict biological activity. Based on docking scores and pIC₅₀ value, potential anti-Chikungunya compounds were identified. Best docked receptor-ligands were also subjected to MD for more accurate estimation. Lipinski's rule and ADME studies of the identified compounds were also studied to assess their drug likeness properties. Results of in silico findings, led to identification of few best fit compounds of natural origin against targets of Chikungunya virus which may lead to discovery of new drugs for Chikungunya. Communicated by Ramaswamy H. Sarma.

Antimicrobial cell penetrating peptides with bacterial cell specificity: pharmacophore modelling, quantitative structure activity relationship and molecular dynamics simulation

Current research has shown cell-penetrating peptides and antimicrobial peptides (AMPs) as probable vectors for use in drug delivery and as novel antibiotics. It has been reported that the higher the therapeutic index (TI) the higher would be the bacterial cell penetrating ability. To the best of our knowledge, no in-silico study has been performed to determine bacterial cell specificity of the antimicrobial cell penetrating peptides (aCPP's) based on their TI. The aim of this study was to develop a quantitative structure activity relationship (QSAR) model, which can estimate antimicrobial potential and cell-penetrating ability of aCPPs against S. aureus, to confirm the relationship between the TI and aCPPs and to identify specific descriptors responsible for aCPPs penetrating ability. Molecular dynamics (MD) simulation was also performed to confirm the membrane insertion of the most active aCPPs obtained from the QSAR study. The most appropriate pharmacophore was identified to predict the aCPP's activity. The statistical results confirmed the validity of the model. The QSAR model was successful in identifying the optimal aCPP with high activity prediction and provided insights into the structural requirements to correlate their TI to cell penetrating ability. MD simulation of the best aCPP with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer confirmed its interaction with the membrane and the C-terminal residues of the aCPP played a key role in membrane penetration. The strategy of combining QSAR and molecular dynamics, allowed for optimal estimation of ligand-target interaction and confirmed the importance of Trp and Lys in interacting with the POPC bilayer. Communicated by Ramaswamy H. Sarma.