Human indoleamine-2,3-dioxygenase 2 cofactor lability and low substrate affinity explained by homology modeling, molecular dynamics and molecular docking.

The human indoleamine-2,3-dioxygenase 2 (hIDO2) protein is growing of interest as it is increasingly implicated in multiple diseases (cancer, autoimmune diseases, COVID-19). However, it is only poorly reported in the literature. Its mode of action remains unknown because it does not seem to catalyze the reaction for which it is attributed: the degradation of the L-Tryptophan into N-formyl-kynurenine. This contrasts with its paralog, the human indoleamine-2,3-dioxygenase 1 (hIDO1), which has been extensively studied in the literature and for which several inhibitors are already in clinical trials. Yet, the recent failure of one of the most advanced hIDO1 inhibitors, the Epacadostat, could be caused by a still unknown interaction between hIDO1 and hIDO2. In order to better understand the mechanism of hIDO2, and in the absence of experimental structural data, a computational study mixing homology modeling, Molecular Dynamics, and molecular docking was conducted. The present article highlights an exacerbated lability of the cofactor as well as an inadequate positioning of the substrate in the active site of hIDO2, which might bring part of an answer to its lack of activity.Communicated by Ramaswamy H. Sarma.

In silico screening of chalcones and flavonoids as potential inhibitors against yellow head virus 3C-like protease.

Yellow head virus (YHV) is one of the most important pathogens in prawn cultivation. The outbreak of YHV could potentially result in collapses in aquaculture industries. Although a flurry of development has been made in searching for preventive and therapeutic approaches against YHV, there is still no effective therapy available in the market. Previously, computational screening has suggested a few cancer drugs to be used as YHV protease (3CLpro) inhibitors. However, their toxic nature is still of concern. Here, we exploited various computational approaches, such as deep learning-based structural modeling, molecular docking, pharmacological prediction, and molecular dynamics simulation, to search for potential YHV 3CLpro inhibitors. A total of 272 chalcones and flavonoids were in silico screened using molecular docking. The bioavailability, toxicity, and specifically drug-likeness of hits were predicted. Among the hits, molecular dynamics simulation and trajectory analysis were performed to scrutinize the compounds with high binding affinity. Herein, the four selected compounds including chalcones cpd26, cpd31 and cpd50, and a flavonoid DN071_f could be novel potent compounds to prevent YHV and GAV propagation in shrimp. The molecular mechanism at the atomistic level is also enclosed that can be used to further antiviral development.

Targeting the I7L Protease: A Rational Design for Anti-Monkeypox Drugs?

The latest monkeypox virus outbreak in 2022 showcased the potential threat of this viral zoonosis to public health. The lack of specific treatments against this infection and the success of viral protease inhibitors-based treatments against HIV, Hepatitis C, and SARS-CoV-2, brought the monkeypox virus I7L protease under the spotlight as a potential target for the development of specific and compelling drugs against this emerging disease. In the present work, the structure of the monkeypox virus I7L protease was modeled and thoroughly characterized through a dedicated computational study. Furthermore, structural information gathered in the first part of the study was exploited to virtually screen the DrugBank database, consisting of drugs approved by the Food and Drug Administration (FDA) and clinical-stage drug candidates, in search for readily repurposable compounds with similar binding features as TTP-6171, the only non-covalent I7L protease inhibitor reported in the literature. The virtual screening resulted in the identification of 14 potential inhibitors of the monkeypox I7L protease. Finally, based on data collected within the present work, some considerations on developing allosteric modulators of the I7L protease are reported.

Whole genome analysis and homology modeling of SARS-CoV-2 Indian isolate reveals potent FDA approved drug choice for treating COVID-19.

Coronaviruses have caused enough devastation in the last two decades. These viruses have some rare features while sharing some common features. Novel coronavirus disease (nCoV-19) caused an outbreak with a fatality rate of 5%. It emerged from China and spread into many countries. The present research focused on genome analysis of Indian nCoV-19 Isolate and its translational product subjected to homology modeling and its subsequent molecular simulations to find out potent FDA approved drug for treating COVID-19. Phylogenetic analysis of SARS-CoV-2 Indian isolate shows close resemblance with 17 countries SARS-CoV-2 isolates. Homology modeling of four non-structural proteins translational product of Indian SARS-CoV-2 genome shows high similarity and allowed regions with the existing PDB deposited SARS-CoV-2 target proteins. Finally, these four generated proteins show more affinity with cobicistat, remdesivir and indinavir out of 14 screened FDA approved drugs in molecular docking which is further proven by molecular dynamics simulation and MMGBSA analysis of target ligand complex with best simulation trajectories. Overall our present research findings is that three proposed drugs namely cobicistat, remdesivir and indinavir showed higher interaction with the model SARS-CoV-2 viral target proteins from the Indian nCoV-19 isolate. These compounds could be used as a starting point for the creation of active antiviral drugs to combat the deadly COVID-19 virus during global pandemic and its subsequent viral infection waves across the globe.Communicated by Ramaswamy H. Sarma.

Computational Study Reveals the Inhibitory Effects of Chemical Constituents from Azadirachta indica (Indian Neem) Against Delta and Omicron Variants of SARS-CoV-2

Background: The newly emerged delta and omicron variants of severe acute respiratory syn-drome coronavirus (SARS-CoV-2) have affected millions of individuals globally with increased transmis-sible and infectivity rates. Although, numerous vaccines are available or under clinical trials to combat the SARS-CoV-2 and its variant, still, a therapeutic agent is awaited. Objective(s): The present work is focused on rigorous screening of chemical constituents of Azadirachta indica (A. indica) against delta and omicron variants of SARS-CoV-2 via inhibition of S-glycoprotein. Method(s): Total, 10 compounds of A. indica were subjected to molecular docking and pharmacophore modeling studies against the S-glycoprotein of delta and omicron variants of SARS-CoV-2. Furthermore, homology modeling was performed for omicron S-glycoprotein with the help of SWISS-MODEL and aligned by PyMOL software. Later on, the residues of protein were verified in the allowed region via Ramachandran plot. In addition, our docking results have also been validated by MMGBSA binding free energy calculations. Result(s): Our computed study demonstrated that nimbolinin B12-methyl ether and nimbidinin showed promising docking scores (>-6.0) as compared to docking scores (< 6.0) of reference drug 'camostat' against S-glycoproteins of both delta and omicron variants. Redocking by using MMGBSA calculation also reveals that both these compounds can effectively bind within the pockets of said protein receptors Conclusion(s): Nimbolinin B12-methyl ether and nimbidinin have potent anti-SARS-CoV activity against delta and omicron variants and thus, A. indica might be a useful source for developing novel anti-SARS-CoV-2 therapeutic agents.Copyright © 2022 Bentham Science Publishers.

Homology Modeling of Coronavirus Structural Proteins and Molecular Docking of Potential Drug Candidates for the Treatment of COVID-19

Background: The discovery of a novel strain of coronavirus in 2019 (COVID-19) has triggered a series of tragic events in the world with thousands of deaths recorded daily. Despite the huge resources committed to the discovery of vaccines against this highly pathogenic virus, scientists are still unable to find suitable treatments for the disease. Understanding the structure of coronavirus proteins could provide a basis for the development of cheap, potent and, less toxic vaccines. Objective(s): This study was therefore designed to model coronavirus spike (S) glycoprotein and envelope (E) protein as well as to carry out molecular docking of potential drugs to the homologs and coronavirus main protease (Mpro). Method(s): Homology modeling of coronavirus spike (S) glycoprotein and envelope (E) protein was car-ried out using sequence deposited in the Uniprot database. The topological features of the model's catalytic site were evaluated using the CASTp server. Compounds reported as potential drugs against COVID-19 were docked to S glycoprotein, E protein, and coronavirus main protease (Mpro) to determine the best ligands and the mode of interaction. Result(s): Homology modeling of the proteins revealed structures with 91-98% sequence similarity with PDB entries. The catalytic site of the modeled proteins contained conserved residue involved in ligand binding. In addition, remdesivir, lopinavir, and ritonavir have a high binding affinity for the three proteins studied interacting with key residues in the protein's catalytic domain. Conclusion(s): Results from the study revealed that remdesivir, lopinavir, and ritonavir are inhibitors of key coronavirus proteins and therefore qualify for further studies as a potential treatment for coronavi-rus.Copyright © 2021 Bentham Science Publishers.

Modeling of SARS-CoV-2 Virus Proteins: Implications on Its Proteome.

COronaVIrus Disease 19 (COVID-19) is a severe acute respiratory syndrome (SARS) caused by a group of beta coronaviruses, SARS-CoV-2. The SARS-CoV-2 virus is similar to previous SARS- and MERS-causing strains and has infected nearly six hundred and fifty million people all over the globe, while the death toll has crossed the six million mark (as of December, 2022). In this chapter, we look at how computational modeling approaches of the viral proteins could help us understand the various processes in the viral life cycle inside the host, an understanding of which might provide key insights in mitigating this and future threats. This understanding helps us identify key targets for the purpose of drug discovery and vaccine development.

Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics.

Since its outbreak in December 2019, the COVID-19 pandemic has caused the death of more than 6.5 million people around the world. The high transmissibility of its causative agent, the SARS-CoV-2 virus, coupled with its potentially lethal outcome, provoked a profound global economic and social crisis. The urgency of finding suitable pharmacological tools to tame the pandemic shed light on the ever-increasing importance of computer simulations in rationalizing and speeding up the design of new drugs, further stressing the need for developing quick and reliable methods to identify novel active molecules and characterize their mechanism of action. In the present work, we aim at providing the reader with a general overview of the COVID-19 pandemic, discussing the hallmarks in its management, from the initial attempts at drug repurposing to the commercialization of Paxlovid, the first orally available COVID-19 drug. Furthermore, we analyze and discuss the role of computer-aided drug discovery (CADD) techniques, especially those that fall in the structure-based drug design (SBDD) category, in facing present and future pandemics, by showcasing several successful examples of drug discovery campaigns where commonly used methods such as docking and molecular dynamics have been employed in the rational design of effective therapeutic entities against COVID-19.

In-silico approach to identify antiviral potent inhibitors against nsp4 of SARS-COV-2

In the absence of effective therapy till now millions of people are dying due to severe acute respiratory syndrome corona virus 2 (SARS-CoV-2). To combat with this highly pathogenic virus, potent and less toxic therapeutic drugs are needed. onstructural protein (NSP4) responsible for cytoplasmic rearrangements necessary for optimal SARS-CoV- 2 replication has been identified as one of the potential drug targets in the development of antiviral agents. To identify promising therapeutic compounds against the imminent danger of COVID-19, present study was designed to predict a 3D-model of NSP4 protein and recognize selective inhibitors, followed by molecular docking with reported antiviral photochemical compounds. Homology modeling was done by using deposited sequence of NSP4 in NCBI database. SWISS MODEL was used to identify best PDB template 3vcb with a sequence similarity of 61.36 percent. To validate 200 reported antiviral photochemical compounds were docked against developed3D-NSP4 model by using MoE software. Lowest binding energy candidates were chosen and screened for pharmacokinetics using the Admits server. NSP4 3D homology model showed potential binding interactions with all reported drugs. However, seven inhibitors were discovered with strongest binding energies ranging from - 9.4838 to -15.7308 Kcal/Mol. In conclusion, this study presents a 3D model of NSP4 and helps understanding the molecular interactions at atomic level. Hence, this model could be suggested as an antiviral target for the development of novel anti-viral agents against COVID-19.

In silico approaches in drug discovery for SARS-CoV-2

The ongoing 21st century SARS-CoV-2 pandemic has endangered global health and alarmed the scientific community at an unprecedented rate. Being a member of Coronaviridae family, the SARS-CoV-2 virus contains an enveloped, single-stranded, positive-sense RNA genome that typically affects the respiratory tract of mammals, including humans, leading to mild to severe respiratory tract infections. The ever costly and time-consuming process of drug discovery has led researchers to use in silico approaches to find potential drug candidates with reference to molecular and signaling pathways associated with the disease. Many studies on in silico tools are used to virtually screen small molecule databases for drug discovery against COVID-19. © 2022 Elsevier Inc. All rights reserved.

Homology Modeling and Molecular Dynamics-Driven Search for Natural Inhibitors That Universally Target Receptor-Binding Domain of Spike Glycoprotein in SARS-CoV-2 Variants.

The rapid spread of SARS-CoV-2 required immediate actions to control the transmission of the virus and minimize its impact on humanity. An extensive mutation rate of this viral genome contributes to the virus' ability to quickly adapt to environmental changes, impacts transmissibility and antigenicity, and may facilitate immune escape. Therefore, it is of great interest for researchers working in vaccine development and drug design to consider the impact of mutations on virus-drug interactions. Here, we propose a multitarget drug discovery pipeline for identifying potential drug candidates which can efficiently inhibit the Receptor Binding Domain (RBD) of spike glycoproteins from different variants of SARS-CoV-2. Eight homology models of RBDs for selected variants were created and validated using reference crystal structures. We then investigated interactions between host receptor ACE2 and RBDs from nine variants of SARS-CoV-2. It led us to conclude that efficient multi-variant targeting drugs should be capable of blocking residues Q(R)493 and N487 in RBDs. Using methods of molecular docking, molecular mechanics, and molecular dynamics, we identified three lead compounds (hesperidin, narirutin, and neohesperidin) suitable for multitarget SARS-CoV-2 inhibition. These compounds are flavanone glycosides found in citrus fruits - an active ingredient of Traditional Chinese Medicines. The developed pipeline can be further used to (1) model mutants for which crystal structures are not yet available and (2) scan a more extensive library of compounds against other mutated viral proteins.

QSAR, homology modeling, and docking simulation on SARS-CoV-2 and pseudomonas aeruginosa inhibitors, ADMET, and molecular dynamic simulations to find a possible oral lead candidate.

BACKGROUND: In seek of potent and non-toxic iminoguanidine derivatives formerly assessed as active Pseudomonas aeruginosa inhibitors, a combined mathematical approach of quantitative structure-activity relationship (QSAR), homology modeling, docking simulation, ADMET, and molecular dynamics simulations were executed on iminoguanidine derivatives. RESULTS: The QSAR method was employed to statistically analyze the structure-activity relationships (SAR) and had conceded good statistical significance for eminent predictive model; (GA-MLR: Q2LOO = 0.8027; R2 = 0.8735; R2ext = 0.7536). Thorough scrutiny of the predictive models disclosed that the Centered Broto-Moreau autocorrelation - lag 1/weighted by I-state and 3D topological distance-based autocorrelation-lag 9/weighted by I-state oversee the biological activity and rendered much useful information to realize the properties required to develop new potent Pseudomonas aeruginosa inhibitors. The next mathematical model work accomplished here emphasizes finding a potential drug that could aid in curing Pseudomonas aeruginosa and SARS-CoV-2 as the drug targets Pseudomonas aeruginosa. This involves homology modeling of RNA polymerase-binding transcription factor DksA and COVID-19 main protease receptors, docking simulations, and pharmacokinetic screening studies of hits compounds against the receptor to identify potential inhibitors that can serve to regulate the modeled enzymes. The modeled protein exhibits the most favorable regions more than 90% with a minimum disallowed region less than 5% and is simulated under a hydrophilic environment. The docking simulations of all the series to the binding pocket of the built protein model were done to demonstrate their binding style and to recognize critical interacting residues inside the binding site. Their binding constancy for the modeled receptors has been assessed through RMSD, RMSF, and SASA analysis from 1-ns molecular dynamics simulations (MDS) run. CONCLUSION: Our acknowledged drugs could be a proficient cure for SARS-CoV-2 and Pseudomonas aeruginosa drug discovery, having said that extra testing (in vitro and in vivo) is essential to explain their latent as novel drugs and manner of action.

Docking study of transmembrane serine protease type 2 inhibitors for the treatment of COVID-19

The recent pandemic development of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its quick national and international spread present a global health emergency. Entry of coronaviruses into the cell depends on binding of the viral spike (S) proteins to host cells receptors, which rely on cell proteases for activation. One of the proteases, transmembrane serine protease type 2 (TMPRSS2) was proven to be crucial for S protein priming. Our research emphasizes on identifying presupposing drug candidates for the TMPRSS2 inhibitors to combat coronavirus disease 2019 (COVID-19). TMPRSS2 homology model is generated by utilizing Modeller9.22, whereas we perform molecular docking with AutoDock Vina. Docking of peptidomimetic inhibitors (inhibitor "92" and inhibitor "50") and allosteric inhibitors (nafamostat and camostat mesilate) in this study is carried out at the active site of the TMPRSS2 homology model. Known active ligands have low docking score energies varying from -7.6 to -8.7 kcal/mol. The docking study has confirmed peptidomimetic inhibitors bind with the catalytic triad HIS 41 and ASP 90 by strong hydrogen bonding. Allosteric inhibitors block access to the catalytic triad (HIS 41, ASP 90, and SER 186) by forming hydrogen bonds with ASP 180, GLN 183, and GLY 209 in the S1 pocket. This investigation gives an insight into the design and identification of drug repurposing candidates for the management of COVID-19. © 2022 Elsevier Inc.

Exploring Schiff base ligand inhibitor for cancer and neurological cells, viruses and bacteria receptors by homology modeling and molecular docking

Due to theirinteresting hydrogen-bonding properties, Schiff bases are known for their variety of applications in chemistry and medicinal chemistry. In this work, the interaction between symmetrical Schiff base ligand (L: bis [4-hydroxy-6-methyl-3-{(1E)-N-[2 (ethylamino) ethyl] ethanimidoyl}-2H-pyran-2-one]) and cancer cells, neurological, viruses and bacteria receptors was studied theoretically. Density functional theory (DFT) was used to determine the geometry, reactivity and electronic properties of this ligand. Homology modeling and molecular docking were performed to check their biological and medicinal properties, including anticancer, antiviral, antibacterial and neurological activities. DFT revealed that the mulliken charges, the molecular orbitals (HOMO and LUMO) and MEP results are in a good agreement to the localization of electrophilic and nucleophilic attack sites. The theoretical study showed a high chemical reactivity and a low kinetic stability of the ligand. The docking study results revealed that the ligand exhibits a good biological activity against leukemia, breast cancer, Alzheimer and Covid-19 with binding energy values of -7.36 kcal/mol, -6.35 kcal/mol, -6.19 kcal/mol and -5.58 kcal/mol, respectively. These results are explained by the low values of binding energy and inhibition constant and multiple H-bonds.

Broad-Spectrum Activity of Small Molecules Acting against Influenza a Virus: Biological and Computational Studies.

Influenza still represents a problematic disease, involving millions of people every year and causing hundreds of thousands of deaths. Only a few drugs are clinically available. The search for an effective weapon is still ongoing. In this scenario, we recently identified new drug-like compounds with antiviral activity toward two A/H1N1 Influenza virus strains, which were demonstrated to interfere with the processes mediated by hemagglutinin (HA). In the present work, the compound's ability to act against the A/H3N2 viral strain has been evaluated in hemagglutination inhibition (HI) assays. Two of the five tested compounds were also active toward the A/H3N2 Influenza virus. To validate the scaffold activity, analogue compounds of two broad-spectrum molecules were selected and purchased for HI testing on both A/H1N1 and A/H3N2 Influenza viruses. Forty-three compounds were tested, and four proved to be active toward all three viral strains. A computational study has been carried out to depict the HA binding process of the most interesting compounds.

In Silico Multi-Target Approach Revealed Potential Lead Compounds as Scaffold for the Synthesis of Chemical Analogues Targeting SARS-CoV-2.

Severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) causes coronavirus disease 2019 (COVID-19), an infectious disease that spreads rapidly in humans. In March 2020, the World Health Organization (WHO) declared a COVID-19 pandemic. Identifying a multi-target-directed ligand approach would open up new opportunities for drug discovery to combat COVID-19. The aim of this work was to perform a virtual screening of an exclusive chemical library of about 1700 molecules containing both pharmacologically active compounds and synthetic intermediates to propose potential protein inhibitors for use against SARS-CoV-2. In silico analysis showed that our compounds triggered an interaction network with key residues of the SARS-CoV-2 spike protein (S-protein), blocking trimer formation and interaction with the human receptor hACE2, as well as with the main 3C-like protease (3CLpro), inhibiting their biological function. Our data may represent a step forward in the search for potential new chemotherapeutic agents for the treatment of COVID-19.

Computational Electrostatics Predict Variations in SARS-CoV-2 Spike and Human ACE2 Interactions

SARS-CoV-2 is a novel virus that crossed over into humans in 2019 and declared a pandemic in early 2020. To understand how the virus infects a new host, we need to understand the mechanistic functions involved with the binding process. To address this need, we generate homology models of SARS-CoV-2 spikes as monomer and trimer to determine the feasibility of reduced computational requirements by using monomer structures. We further generate homology models of the conserved region of SARS-CoV-2 spike subunit s1 noted as the receptor binding domain (RBD) and the human angiotensin-converting enzyme 2 (ACE2). To determine functional breadth of spike monomer, trimer and RBD in relation with ACE2, we apply Coulombs Law to determine an electric force between combinations with ACE2 across the range of pH from 3.0 to 9.0 in 0.1 increments. The results indicate that spike trimer should be used to determine mechanistic binding function and these data indicate that variations of spike sequence influence breadth of function. Our results also indicate the RBD has a broader range of function across pH compared to spike trimer, but is influenced by the range of function presented by the spike trimer. © 2021 IEEE.

Discovery of novel TMPRSS2 inhibitors for COVID-19 using in silico fragment-based drug design, molecular docking, molecular dynamics, and quantum mechanics studies.

The global expansion of COVID-19 and the mutations of severe acute respiratory syndrome coronavirus necessitate quick development of treatment and vaccination. Because the androgen-responsive serine protease TMPRSS2 is involved in cleaving the SARS-CoV-2 spike protein allowing the virus to enter the cell, therefore, direct TMPRSS2 inhibition will inhibit virus activation and disease progression which make it an important target for drug discovery. In this study, a homology model of TMPRSS2 protein was initially developed. Then, we used the fragment-based drug design (FBDD) technique to develop effective TMPRSS2 inhibitors. Over a half-million fragments from the enamine database were screened for their binding ability to target protein, and then best-scoring fragments were linked to building new molecules with a good binding affinity. XP docking and MM-GBSA studies revealed 10 new formed molecules with docking score ≤ -14.982 kcal/mol compared to ambroxol (control) with a docking score of -6.464 kcal/mol. Finally, molecular dynamics (MD) and density functional theory (DFT) were calculated for the top 3 molecules.

STRUCTURE PREDICTION AND EXAMINATION OF SPIKE PROTEIN FROM SARS-COV2 USING COMPUTATIONAL AND SIMULATION TOOLS

Recent emergence of COVID-19 corona virus has resulted in WHO-declared public health emergency of international concern. Research around the globe is working towards establishing a great understanding of this particular viruses and developing treatments and vaccines to prevent spread. Knowing structural properties of a protein provides an important resource for understanding how it functions. This paper provides structural details of Spike Protein(s) of novel corona virus. The Spike protein of nCOV2 is prominent for confining with a cell receptor which is act as a referee for the synthesis of virus and host membranes, and these activities being crucial for virus ingress in to host cell. This paper studies primary, secondary and tertiary structures of spike protein of SARS-COV2 predicted using Exapasy Program, PSIPRED, and Homology Modeling Methods respectively [1]. The predicted structure was validated using PROCHECK by Ramachandran plot and also validated through ProSAWeb tool. Finally, this predicted structure will helps to discovering efficient medicine against corona epidemic. In future, resultant structure of homology modeling would be energy minimized and can do MD (Molecular dynamics) simulations to examine how the expected model behave structurally, dynamically, using several computational and simulation tools.