Molecular dynamics simulation of docking structures of SARS-CoV-2 main protease and HIV protease inhibitors

Designing novel multiepitope mRNA vaccine targeting Hendra virus (HeV): An integrative approach utilizing immunoinformatics, reverse vaccinology, and molecular dynamics simulation

Human and animal health is threatened by Hendra virus (HeV), which has few treatments. This in-silico vaccine design study focuses on HeV G (glycoprotein), F (fusion protein), and M (matrix protein). These proteins were computationally assessed for B and T-cell epitopes after considering HeV strain conservation, immunogenicity, and antigenicity. To improve vaccination immunogenicity, these epitopes were selectively ligated into a multiepitope construct. To improve vaccination longevity and immunological response, adjuvants and linkers were ligated. G, F, and M epitopes were used to create an mRNA HeV vaccine. Cytotoxic, helper, and linear B-lymphocytes' epitopes are targeted by this vaccine. The population coverage analysis demonstrates that multi-epitope vaccination covers 91.81 percent of CTL and 98.55 percent of HTL epitopes worldwide. GRAVY evaluated the vaccine's well-characterized physicochemical properties -0.503, indicating solubility and functional stability. Structure analysis showed well-stabilized 2° and 3° structures in the vaccine, with alpha helix, beta sheet, and coil structures (Ramachandran score of 88.5% and Z score of -3.44). There was a strong affinity as shown by docking tests with TLR-4 (central score of -1139.4 KJ/mol) and TLR-2 (center score of -1277.9 KJ/mol). The coupled V-apo, V-TLR2, and V-TLR4 complexes were tested for binding using molecular dynamics simulation where extremely stable complexes were found. The predicted mRNA structures provided significant stability. Codon optimization for Escherichia. coli synthesis allowed the vaccine to attain a GC content of 46.83% and a CAI score of 1.0, which supports its significant expression. Immunological simulations indicated vaccine-induced innate and adaptive immune reactions. Finally, this potential HeV vaccine needs more studies to prove its efficacy and safety.

Computational exploration of novel ketoprofen derivatives: Molecular dynamics simulations and MM-PBSA calculations for COX-2 inhibition as promising anti-inflammatory drugs

Computer-aided drug design is widely employed to identify novel compounds for therapeutic applications. Ketoprofen (KTP), a commonly used and marketed nonsteroidal anti-inflammatory drug (NSAID), is effective in treating pain, fever, inflammation, and some cancers. In this research, we explored the behavior of six analogues designed by structurally modifying the KTP molecule. Specifically, KTP-A and KTP-B contain a -CN group at the ortho position, KTP-C and KTP-D have a -CN group at the meta position, and KTP-E and KTP-F feature a -CF3 group at the meta position. To assess these analogues, we conducted molecular dynamics simulations (MD) to study their inhibitory effects on human cyclooxygenase 2 (COX-2), providing detailed insights into the structure and dynamics of the protein both with and without ligands. MD simulation, enhanced by technological advances, has proven to be a powerful tool for new drug discovery. We further quantified the binding affinity of these drug molecules toward COX-2 using molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations. The dynamic properties were evaluated through analyses of root mean square deviations (RMSD), root mean square fluctuations (RMSF), radius of gyration (Rg), solvent-accessible surface area (SASA), covariance matrix, principal component analysis (PCA), and Gibbs free energy landscapes (FEL). Ultimately, this study confirms that the six KTP derivatives are promising candidates for the treatment of inflammation, with KTP-B standing out as particularly effective.

Widely-targeted in silico and in vitro evaluation of veratrum alkaloid analogs as FAK inhibitors and dual targeting of FAK and Hh/SMO pathways for cancer therapy: A critical analysis

Focal Adhesive Kinase (FAK), a key player in aggressive cancers, mediates signals crucial for progression, invasion, and metastasis. Despite advances in targeted therapies, drug resistance is still a challenge, and survival rates remain low, particularly for late-stage patients, emphasizing the need for innovative cancer therapeutics. Cyclopamine, a veratrum alkaloid, has shown promising anti-tumor properties, but the search for more potent analogs with enhanced affinity for the biological target continues. This study employs a hybrid virtual screening approach combining pharmacophore model-based virtual screening (PB-VS) and docking-based virtual screening (DB-VS) to identify potential inhibitors of the FAK catalytic domain. PB-VS on the PubChem database yielded a set of hits, which were then docked with the FAK catalytic domain in two stages (1st and 2nd DB-VS). Hits were ranked based on docking scores and interactions with the active site. The top three compounds underwent molecular dynamics simulations, alongside two control compounds (SMO inhibitor(s) and FAK inhibitor(s)), to assess stability through RMSD, RMSF, Rg, and SASA analyses. ADMET properties were evaluated, and compounds were filtered based on drug-likeness criteria. Molecular dynamics simulations demonstrated the stability of compounds when complexed with the FAK catalytic domain. Compounds 16 (-25 kcal/mol), 87 (-27.47 kcal/mol), and 88 (-18.94 kcal/mol) exhibited comparable docking scores, interaction profiles, stability, and binding energies, indicating their potential as lead candidates. However, further validation and optimization through quantitative structure-activity relationship (QSAR) studies are essential to refine their efficacy and therapeutic potential. The in vitro cell-based assay demonstrated that compound 101PF, a FAK inhibitor, significantly inhibited the proliferation and migration of A549 cells. However, the results regarding the combined effects of FAK and SMO inhibitors were inconclusive, highlighting the need for further investigation. This study contributes to developing more effective anti-cancer drugs by improving the understanding of potential cyclopamine-based veratrum alkaloid analogs with enhanced interactions with the FAK catalytic domain.

Breaking the Chain: Protease Inhibitors as Game Changers in Respiratory Viruses Management

Respiratory viral infections (VRTIs) rank among the leading causes of global morbidity and mortality, affecting millions of individuals each year across all age groups. These infections are caused by various pathogens, including rhinoviruses (RVs), adenoviruses (AdVs), and coronaviruses (CoVs), which are particularly prevalent during colder seasons. Although many VRTIs are self-limiting, their frequent recurrence and potential for severe health complications highlight the critical need for effective therapeutic strategies. Viral proteases are crucial for the maturation and replication of viruses, making them promising therapeutic targets. This review explores the pivotal role of viral proteases in the lifecycle of respiratory viruses and the development of protease inhibitors as a strategic response to these infections. Recent advances in antiviral therapy have highlighted the effectiveness of protease inhibitors in curtailing the spread and severity of viral diseases, especially during the ongoing COVID-19 pandemic. It also assesses the current efforts aimed at identifying and developing inhibitors targeting key proteases from major respiratory viruses, including human RVs, AdVs, and (severe acute respiratory syndrome coronavirus-2) SARS-CoV-2. Despite the recent identification of SARS-CoV-2, within the last five years, the scientific community has devoted considerable time and resources to investigate existing drugs and develop new inhibitors targeting the virus's main protease. However, research efforts in identifying inhibitors of the proteases of RVs and AdVs are limited. Therefore, herein, it is proposed to utilize this knowledge to develop new inhibitors for the proteases of other viruses affecting the respiratory tract or to develop dual inhibitors. Finally, by detailing the mechanisms of action and therapeutic potentials of these inhibitors, this review aims to demonstrate their significant role in transforming the management of respiratory viral diseases and to offer insights into future research directions.

Advanced molecular mechanisms of modified DRV compounds in targeting HIV-1 protease mutations and interrupting monomer dimerization

HIV-1 protease (PR) plays a crucial role in the treatment of HIV as a key target. The global issue of emerging drug resistance is escalating, and PR mutations pose a substantial challenge to the effectiveness of inhibitors. HIV-1 PR is an ideal model for studying drug resistance to inhibitors. The inhibitor, darunavir (DRV), exhibits a high genetic barrier to viral resistance, but with mutations of residues in the PR, there is also some resistance to DRV. Inhibitors can impede PR in two ways: one involves binding to the active site of the dimerization protease, and the other involves binding to the PR monomer, thereby preventing dimerization. In this study, we aimed to investigate the inhibitory effect of DRV with a modified inhibitor on PR, comparing the differences between wild-type and mutated PR, using molecular dynamics simulations. The inhibitory effect of the inhibitors on PR monomers was subsequently investigated. And molecular mechanics Poisson-Boltzmann surface area evaluated the binding free energy. The energy contribution of individual residues in the complex was accurately calculated by the alanine scanning binding interaction entropy method. The results showed that these inhibitors had strong inhibitory effects against PR mutations, with GRL-142 exhibiting potent inhibition of both the PR monomer and dimer. Improved inhibitors could strengthen hydrogen bonds and interactions with PR, thereby boosting inhibition efficacy. The binding of the inhibitor and mutation of the PR affected the distance between D25 and I50, preventing their dimerization and the development of drug resistance. This study could accelerate research targeting HIV-1 PR inhibitors and help to further facilitate drug design targeting both mechanisms.

Ensemble-based, high-throughput virtual screening of potential inhibitor targeting putative farnesol dehydrogenase of Metisa plana (Lepidoptera: Psychidae)

Metisa plana (Lepidoptera: Psychidae) bagworm is a leaf-eater caterpillar ubiquitously found as a damaging pest in oil palm plantations, specifically in Malaysia. Various strategies have been implemented, including the usage of chemical insecticides. However, the main challenges include the development of insecticide resistance and its detrimental effects on the environment and non-target organisms. Therefore, a biorational insecticide is introduced by targeting the juvenile hormone (JH) biosynthetic pathway, which is mainly present in the insect and vital for the insect's growth, diapause, metamorphosis, and adult reproduction. This study aimed to investigate the potential inhibitor for the rate-limiting enzyme involved in the JH pathway known as farnesol dehydrogenase. A 255 amino acids sequence encoded for the putative M. plana farnesol dehydrogenase (MpFolDH) open reading frame had been identified and isolated. The three-dimensional structure of MpFolDH was predicted to have seven β- sheets with α-helices at both sides, showing typical characteristics for classical short-chain dehydrogenase and associated with oxidoreductase activity. Then, the ensemble-based virtual screening was conducted based on the ZINC20 database, in which 43 768 compounds that fulfilled pesticide-likeness criteria were screened by site-specific molecular docking. After a short molecular dynamics simulation (5 ns) was conducted towards 102 compounds, only the top 10 compounds based on their most favourable binding energy were selected for a more extended simulation (100 ns). Based on the protein-ligand stability, protein compactness, residues rigidity, binding interaction, binding energy throughout the 100 ns simulation, and physicochemical analysis, ZINC000408743205 was selected as a potential inhibitor for this enzyme. Amino acids decomposition analysis indicates Ile18, Ala95, Val198 and Val202 were the critical contributor residues for MpFolDH-inhibitors(s) complex.

Effectivity of repurposed drugs against SARS-CoV-2 infections, A hope for COVID 19: inhibitor modelling studies by docking and molecular dynamics

In the present study, we have done a comparative study on the efficacy of some currently used repurposed drugs: Oseltamivir (O), Favipiravir (F) and Hydroxychloroquine (H) in individual and in their combinational mode against CoV-2 infections. The ADME analysis has helped us to identify the inhibitory possibility of the tested drugs towards receptor 3CLpro protein of SARS-CoV-2. Various thermodynamical parameters obtained from Molecular Docking, Molecular dynamics (MD) and MMPBSA simulations like binding affinity, potential energy (Epot), RMSD, RMSF, SASA energy, interaction energies, Gibbs free energy (ΔGbind) etc. also helped us to verify the effectivity of mentioned drugs against CoV-2 protease.

Self-Assembled Maslinic Acid Attenuates Doxorobucin Induced Cytotoxicity via Nrf2 Signaling Pathway: An In Vitro and In Silico Study in Human Healthy Cells

The clinical applications of some well-known chemotherapeutic drugs for cancer treatment have been restricted nowadays owing to their adverse effects on many physiological systems. In this experimental study, maslinic acid (MA) isolated from Olea europaea (Olive) fruit extract was used to mitigate the cytotoxicity induced by Doxorubicin (DOX) in human healthy peripheral blood mononuclear cells (hPBMCs). Self-assembled maslinic acid (SA-MA) was obtained in ethanol-water mixture (35.5 mM: 4:1 v/v). The morphology of SA-MA was analyzed by various physicochemical characterization techniques, which revealed its micro-metric vesicular architecture as well as nano-vesicular appearances. In this study, treatment of hPBMCs with DOX has been found to generate severe intracellular oxidative stress, which was significantly mitigated after pre-treatment with SA-MA. Alteration of hPBMC morphologies after DOX treatment was also restored notably by pre-treatment with SA-MA. Furthermore, pentoxifylline (TNF-α inhibitor) and indomethacin (COX-2 inhibitor) were used to investigate the responsible pathway by which SA-MA protected hPBMCs from DOX-induced cellular stress. Restoration of hPBMC viability above 92% in both cases confirmed that SA-MA protected the cells by inhibiting inflammatory pathways generated by DOX treatment. Subsequently, in molecular docking study, it was also evaluated that MA could successfully bind with the pocket region of Keap1, while Nrf2 was capable of upregulating cytoprotecting genes.

Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices

Molecular dynamics (MD) is a core methodology of molecular modeling and computational design for the study of the dynamics and temporal evolution of molecular systems. MD simulations have particularly benefited from the rapid increase of computational power that has characterized the past decades of computational chemical research, being the first method to be successfully migrated to the GPU infrastructure. While new-generation MD software is capable of delivering simulations on an ever-increasing scale, relatively less effort is invested in developing postprocessing methods that can keep up with the quickly expanding volumes of data that are being generated. Here, we introduce a new idea for sampling frames from large MD trajectories, based on the recently introduced framework of extended similarity indices. Our approach presents a new, linearly scaling alternative to the traditional approach of applying a clustering algorithm that usually scales as a quadratic function of the number of frames. When showcasing its usage on case studies with different system sizes and simulation lengths, we have registered speedups of up to 2 orders of magnitude, as compared to traditional clustering algorithms. The conformational diversity of the selected frames is also noticeably higher, which is a further advantage for certain applications, such as the selection of structural ensembles for ligand docking. The method is available open-source at https://github.com/ramirandaq/MultipleComparisons.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Molecular dynamics simulation approach for discovering potential inhibitors against SARS-CoV-2: A structural review

Since the commencement of the novel Coronavirus, the disease has quickly turned into a worldwide crisis so that there has been growing attention in discovering possible hit compounds for tackling this pandemic. Discovering standard treatment strategies is a serious challenge because little information is available about this emerged infectious virus. Regarding the high impact of time, applying computational procedures to choose promising drugs from a catalog of licensed medications provides a precious chance for combat against the life-threatening disorder of COVID-19. Molecular dynamics (MD) simulation is a promising approach for assessing the binding affinity of ligand-receptor as well as observing the conformational trajectory of docked complexes over time. Given that many computational studies are performed using MD along with the molecular docking on various candidates as antiviral inhibitors of COVID-19 protease, there is a demand to conduct a comprehensive review of the most important studies to reveal and compare the potential introduced agents that this study covers this defect. In this context, the present review intends to prepare an overview of these studies by considering RMSD, RMSF, radius of gyration, binding free energy, and Solvent-Accessible Surface Area (SASA) as effective parameters for evaluation. The outcomes will offer a road map for adjusting antiviral inhibitors, which can facilitate the selection and development of drug candidates for use in the medical therapy. Finally, the molecular modeling approaches rendered by this study may be valuable for future computational studies.

Investigating the novel acetonitrile derivatives as potential SARS-CoV-2 main protease inhibitor using molecular modeling approach

The COVID-19 is declared a pandemic by World Health Organization (WHO). It causes respiratory illness which leads to oxygen deficiency; it has affected millions of lives all around the globe. It has also been observed that people with diabetes condition are more likely to have severe symptoms when infected with the SARS-CoV2. So, continued efforts are being taken to design and discover potential anti-covid drugs. Earlier, a study reveals that the acetonitrile (2-phenyl-4H-benzopyrimedo [2,1-b]-thiazol-4-yliden) derivatives have potential anti-diabetic activity. Hence, drugs repurpose approach was used to identify the potential acetonitrile derivative targeting the main protease of SARS-CoV2. Here, ADMET, molecular docking, and molecular dynamics simulation techniques were employed, to identify potential acetonitrile compounds against the main protease. The acetonitrile compounds (A to M) show the drug-likeness properties. Next, the molecular docking and dynamics simulation study reveals that acetonitrile compounds A, F, G, and L show a higher binding affinity and have an effect on the structure and dynamics of the main protease. Furthermore, binding energy calculations reveal that the acetonitrile derivative F has a higher binding affinity with the main protease and derivative L has a lower binding affinity with the main protease. The binding affinity of acetonitrile derivatives decreases in the order of F > A > G > L with the main protease. Thus, our computational modeling study provides valuable structural and energetic information of interaction of potential acetonitrile derivatives with the main protease which could be further used as potential lead molecules against the SARS-CoV2.Communicated by Ramaswamy H. Sarma.

Molecular dynamics simulation and docking analysis of NF-κB protein binding with sulindac acid

It is of interest to document the Molecular Dynamics Simulation and docking analysis of NF-κB target with sulindac sodium in combating COVID-19 for further consideration. Sulindac is a nonsteroidal anti-inflammatory drug (NSAID) of the arylalkanoic acid class that is marketed by Merck under the brand name Clinoril. We show the binding features of sulindac sodium with NF-κB that can be useful in drug repurposing in COVID-19 therapy.

Synthesis, crystal structure, computational study and anti-virus effect of mixed ligand copper (II) complex with ONS donor Schiff base and 1, 10-phenanthroline

This work deals with the synthesis, crystal structure, computational study and antiviral potential of mixed ligand copper(II) complex [Cu(L)(phen)](1), (where, H₂L = (Z)-N'-((E)-2-hydroxy-3,5-diiodobenzylidene)-N,N-dimethylcarbamohydrazonothioic acid, phen = 1,10-phenanthroline). The Schiff base ligand (H₂L) is coordinated with Cu(II) ion in O, N, S-tridentate mode. The copper complex (1) crystallized in the monoclinic system of the space group P21/c with eight molecules in the unit cell and reveals a square pyramidal geometry. Furthermore, we also perform quantum chemical calculations to get insights into the structure-property relationship and functional properties of ligand (H₂L) and its copper (II) complex [Cu(L)(phen)](1). Complex [Cu(L)(phen)](1) was also virtually designed in-silico evaluation by Swiss-ADME. Additionally, inspiring by recent developments to find a potential inhibitor for the COVID-19 virus, we have also performed molecular docking study of ligand and its copper complex (1) to see if our compounds shows an affinity for the main protease (M^pro) of COVID-19 spike protein (PDB ID: 7C8U). Interestingly, the results are found quite encouraging where the binding affinity and inhibition constant were found to be -7.14 kcal/mol and 5.82 μM for ligand (H₂L) and -6.18 kcal/mol and 0.76 μM for complex [Cu(L)(phen)](1) with M^pro protein. This binding affinity is reasonably well as compared to recently known antiviral drugs. For instance, the binding affinity of ligand and complex was found to be better than docking results of chloroquine (-6.293 kcal/mol), hydroxychloroquine (-5.573 kcal/mol) and remdesivir (-6.352 kcal/mol) with M^pro protein. The present study may offer the technological solutions and potential inhibition to the COVID-19 virus in the ongoing and future challenges of the global community. In the framework of synthesis and characterization of mixed ligand copper (II) complex; the major conclusions can be drawn as follow.

Computational Simulation of HIV Protease Inhibitors to the Main Protease (Mpro) of SARS-CoV-2: Implications for COVID-19 Drugs Design

SARS-CoV-2 is highly homologous to SARS-CoV. To date, the main protease (Mpro) of SARS-CoV-2 is regarded as an important drug target for the treatment of Coronavirus Disease 2019 (COVID-19). Some experiments confirmed that several HIV protease inhibitors present the inhibitory effects on the replication of SARS-CoV-2 by inhibiting Mpro. However, the mechanism of action has still not been studied very clearly. In this work, the interaction mechanism of four HIV protease inhibitors Darunavir (DRV), Lopinavir (LPV), Nelfinavir (NFV), and Ritonavire (RTV) targeting SARS-CoV-2 Mpro was explored by applying docking, molecular dynamics (MD) simulations, and MM-GBSA methods using the broad-spectrum antiviral drug Ribavirin (RBV) as the negative and nonspecific control. Our results revealed that LPV, RTV, and NFV have higher binding affinities with Mpro, and they all interact with catalytic residues His41 and the other two key amino acids Met49 and Met165. Pharmacophore model analysis further revealed that the aromatic ring, hydrogen bond donor, and hydrophobic group are the essential infrastructure of Mpro inhibitors. Overall, this study applied computational simulation methods to study the interaction mechanism of HIV-1 protease inhibitors with SARS-CoV-2 Mpro, and the findings provide useful insights for the development of novel anti-SARS-CoV-2 agents for the treatment of COVID-19.

Identifying compounds that prevent the binding of the SARS-CoV-2 S-protein to ACE2

We investigated compounds selected by molecular docking to identify a specific treatment for COVID-19 that decreases the interaction between angiotensin-converting enzyme 2 (ACE2) and the receptor-binding domain (RBD) of SARS-CoV-2. Five compounds that interact with ACE2 amino acids Gln24, Asp30, His34, Tyr41, Gln42, Met82, Lys353, and Arg357 were evaluated using specific binding assays for their effects on the interaction between ACE2 with RBD. The compound labeled ED demonstrated favorable ACE2-binding, with an IC50 of 31.95 μM. ED cytotoxicity, evaluated using PC3 cells in an MTT assay, was consistent with the low theoretical toxicity previously reported. We propose that ED mainly interacts with His34, Glu37, and Lys353 in ACE2 and that it has an inhibitory effect on the interaction of ACE2 with the RBD of the S-protein. We recommend further investigation to develop ED into a potential drug or adjuvant in COVID-19 treatment.

Accelerating COVID-19 Research Using Molecular Dynamics Simulation

The COVID-19 pandemic has emerged as a global medico-socio-economic disaster. Given the lack of effective therapeutics against SARS-CoV-2, scientists are racing to disseminate suggestions for rapidly deployable therapeutic options, including drug repurposing and repositioning strategies. Molecular dynamics (MD) simulations have provided the opportunity to make rational scientific breakthroughs in a time of crisis. Advancements in these technologies in recent years have become an indispensable tool for scientists studying protein structure, function, dynamics, interactions, and drug discovery. Integrating the structural data obtained from high-resolution methods with MD simulations has helped in comprehending the process of infection and pathogenesis, as well as the SARS-CoV-2 maturation in host cells, in a short duration of time. It has also guided us to identify and prioritize drug targets and new chemical entities, and to repurpose drugs. Here, we discuss how MD simulation has been explored by the scientific community to accelerate and guide translational research on SARS-CoV-2 in the past year. We have also considered future research directions for researchers, where MD simulations can help fill the existing gaps in COVID-19 research.

Computational Chemistry to Repurposing Drugs for the Control of COVID-19

Thus far, in 2021, 219 countries with over 175 million people have been infected by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). SARS-CoV-2 is a positive sense, single-stranded RNA virus, and is the causal agent for coronavirus disease (COVID-19). Due to the urgency of the situation, virtual screening as a computational modeling method offers a fast and effective modality of identifying drugs that may be effective against SARS-CoV-2. There has been an overwhelming abundance of molecular docking against SARS-CoV-2 in the last year. Due to the massive volume of computational studies, this systematic review has been created to evaluate and summarize the findings of existing studies. Herein, we report on computational articles of drugs which target, (1) viral protease, (2) Spike protein-ACE 2 interaction, (3) RNA-dependent RNA polymerase, and (4) other proteins and nonstructural proteins of SARS-CoV-2. Based on the studies presented, there are 55 identified natural or drug compounds with potential anti-viral activity. The next step is to show anti-viral activity in vitro and translation to determine effectiveness into human clinical trials.

Molecular Docking Study of Antibiotics, Anti-Inflammatory Drugs and [Eu(TTA)3⋅AMX] Complex as COVID-19 Biomarker through Interaction of Its Main Protease (Mpro)

Coronavirus Acute Respiratory Syndrome (SARS-CoV-2) is a very recent viral infection and has generated one of the world’s biggest problems of all time. There is no scientific evidence and clinical trials to indicate that possible therapies have shown results in suspected or confirmed patients other than the use of immunizations. Given the above, some substances are being studied to be applied to contain their spread and further damage. This work aims to perform an in silico study of amoxicillin, widely known as an antibiotic and used to prevent bacterial infections and a possible biomarker made from a complex with Europium (Eu). It was shown to have the ability to interact with the COVID-19 protein in Mpro protease as ligands. The study was conducted using the AutoDock Vina with Lamarckian genetic model algorithm (GA) combined with the estimation of grid-based energy in rigid and flexible conformation. Compared to affinity energy, amoxicillin presented [Formula: see text][Formula: see text]kcal/mol, which was better than its co-crystallized ligand in the study. The Europium complex, where its synthesis was also demonstrated in this work, presented energy of [Formula: see text][Formula: see text]kcal/mol with hydrogen bonds and possible color change when UV light was applied. For the choice of the best poses in the simulation, the neural network parameter, NNScore2, was used. It can be affirmed that this study is still introductory but promising both in the treatment and identification of the virus.

New Approaches and Repurposed Antiviral Drugs for the Treatment of the SARS-CoV-2 Infection

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the virus that causes coronavirus disease 2019 (COVID-19). The outbreak of this coronavirus was first identified in Wuhan (Hubei, China) in December 2019, and it was declared as pandemic by the World Health Organization (WHO) in March 2020. Today, several vaccines against SARS-CoV-2 have been approved, and some neutralizing monoclonal antibodies are being tested as therapeutic approaches for COVID-19 but, one of the key questions is whether both vaccines and monoclonal antibodies could be effective against infections by new SARS-CoV-2 variants. Nevertheless, there are currently more than 1000 ongoing clinical trials focusing on the use and effectiveness of antiviral drugs as a possible therapeutic treatment. Among the classes of antiviral drugs are included 3CL protein inhibitors, RNA synthesis inhibitors and other small molecule drugs which target the ability of SARS-COV-2 to interact with host cells. Considering the need to find specific treatment to prevent the emergent outbreak, the aim of this review is to explain how some repurposed antiviral drugs, indicated for the treatment of other viral infections, could be potential candidates for the treatment of COVID-19.

In silico approach for identifying natural lead molecules against SARS-COV-2

The life challenging COVID-19 disease caused by the SARS-CoV-2 virus has greatly impacted smooth survival worldwide since its discovery in December 2019. Currently, it is one of the major threats to humanity. Moreover, any specific drug or vaccine unavailability against COVID-19 forces to discover a new drug on an urgent basis. Viral cycle inhibition could be one possible way to prevent the further genesis of this viral disease, which can be contributed by drug repurposing techniques or screening of small bioactive natural molecules against already validated targets of COVID-19. The main protease (M^pro) responsible for producing functional proteins from polyprotein is an important key step for SARS-CoV-2 virion replication. Natural product or herbal based formulations are an important platform for potential therapeutics and lead compounds in the drug discovery process. Therefore, here we have screened >53,500 bioactive natural molecules from six different natural product databases against M^pro (PDB ID: 6LU7) of COVID-19 through computational study. Further, the top three molecules were subjected to pharmacokinetics evaluation, which is an important factor that reduces the drug failure rate. Moreover, the top three screened molecules (C00014803, C00006660, ANLT0001) were further validated by a molecular dynamics study under a condition similar to the physiological one. Relative binding energy analysis of three lead molecules indicated that C00014803 possess highest binding affinity among all three hits. These extensive studies can be a significant foundation for developing a therapeutic agent against COVID-19 through vet lab studies.

Computational investigations of three main drugs and their comparison with synthesized compounds as potent inhibitors of SARS-CoV-2 main protease (Mpro): DFT, QSAR, molecular docking, and in silico toxicity analysis

In this study, we examined five previously synthesized compounds and checked their binding affinity towards the SARS-CoV-2 main protease (Mpro) by molecular docking study, and compared the data with three FDA approved drugs, i.e., Remdesivir, Ivermectine and Hydroxychlorochine. In addition, we have investigated the docking study against the main protease of SARS-CoV-2 (Mpro) by using Autodock 4.2 software package. The results suggested that the investigated compounds have property to bind the active position of the protein as reported in approved drugs. Hence, further experimental studies are required. The formation of intermolecular interactions, negative values of scoring functions, free binding energy and the calculated binding constants confirmed that the studied compounds have significant affinity for the specified biotarget. These studied compounds were passed the drug-likeness criteria as suggested by calculating ADME data by SwissADME server. Moreover, the ADMET properties suggested that the investigated compounds to be orally active compounds in human. Furthermore, density functional computations (DFT) were executed by applying GAUSSIAN 09 suit program. In addition, Quantitative Structure-Activity Relationship (QSAR) was studied by applying HyperChem Professional 8.0.3 program.

Synthesis and physicochemical studies of a series of mixed-ligand transition metal complexes and their molecular docking investigations against Coronavirus main protease

A novel series of mixed-ligand complexes of the type, [M(L1)(L2)Cl]·2H2O [L1 = 2-(α-methyl salicylidene hydrazine) benzimidazole (primary ligand), L2 = 2,2′-bipyridine (bipy; secondary ligand), M = Co(ii), Ni(ii), Cu(ii) and Zn(ii)], were based on the physicoanalytical studies. The spectroscopic findings revealed tridentate nature of the Schiff base ligand (L1) and its coordination to the metal ions via azomethine nitrogen, ring nitrogen and the deprotonated phenolic oxygen atoms. Furthermore, the synthesized compounds were evaluated for antimicrobial activity against Bacillus subtilis, Escherichia coli and Salmonella typhi microorganisms. In addition, molecular docking studies were carried out against Middle East respiratory syndrome coronavirus (PDB ID: 4ZS6) and severe acute respiratory syndrome coronavirus 2 main protease (PDB ID: 6W63).

Molecular Docking Study on Several Benzoic Acid Derivatives against SARS-CoV-2

Several derivatives of benzoic acid and semisynthetic alkyl gallates were investigated by an in silico approach to evaluate their potential antiviral activity against SARS-CoV-2 main protease. Molecular docking studies were used to predict their binding affinity and interactions with amino acids residues from the active binding site of SARS-CoV-2 main protease, compared to boceprevir. Deep structural insights and quantum chemical reactivity analysis according to Koopmans’ theorem, as a result of density functional theory (DFT) computations, are reported. Additionally, drug-likeness assessment in terms of Lipinski’s and Weber’s rules for pharmaceutical candidates, is provided. The outcomes of docking and key molecular descriptors and properties were forward analyzed by the statistical approach of principal component analysis (PCA) to identify the degree of their correlation. The obtained results suggest two promising candidates for future drug development to fight against the coronavirus infection.