Computational investigation of potential inhibitors of novel coronavirus 2019 through structure-based virtual screening, molecular dynamics and density functional theory studies

Identification of terpenoids as dihydropteroate synthase and dihydrofolate reductase inhibitors through structure-based virtual screening and molecular dynamic simulations

Bacterial infections are rising, and antimicrobial resistance (AMR) in bacteria has worsened the scenario, requiring extensive research to find alternative therapeutic agents. Terpenoids play an essential role in protecting plants from herbivores and pathogens. The present study was designed to focus on in silico evaluation of terpenoids for their affinity towards two necessary enzymes, i.e. DHFR and DHPS, which are involved in forming 5, 6, 7, 8-tetrahydrofolate, a key component in bacterial DNA synthesis proteins. Additionally, to account for activity against resistant bacteria, their affinity towards the L28R mutant of DHFR was also assessed in the study. The structure-based drug design approach was used to screen the compound library of terpenes for their interaction with active sites of DHFR and DHPS. Further, compounds were screened based on their dock score, pharmacokinetic properties, and binding affinities. A total of five compounds for each target protein were screened, having dock scores better than their respective standard drug molecules. CNP0169378 (-8.4 kcal/mol) and CNP0309455 (-6.5 kcal/mol) have been identified as molecules with a higher affinity toward the targets of DHFR and DHPS, respectively. At the same time, one molecule CNP0298407 (-5.8 kcal/mol for DHPS, -7.6 kcal/mol for DHFR, -6.1 kcal/mol for the L28R variant), has affinity for both proteins (6XG5 and 6XG4). All the molecules have good pharmacokinetic properties. We further validated the docking study by binding free energy calculations using the MM/GBSA approach and molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Hydroxamic acid derivatives as selective HDAC3 inhibitors: computer-aided drug design strategies

Histone deacetylases (HDACs) are critical epigenetic drug targets that have gained significant attention in the scientific community for the treatment of cancer. The currently marketed HDAC inhibitors lack selectivity for the various HDAC isoenzymes. Here, we describe our protocol for the discovery of novel potential hydroxamic acid based HDAC3 inhibitors through pharmacophore modeling, virtual screening, docking, molecular dynamics (MD) simulation and toxicity studies. The ten pharmacophore hypotheses were established, and their reliability was validated by different ROC (receiving operator curve) analysis. Among them, the best model (Hypothesis 9 or RRRA) was employed for searching SCHEMBL, ZINC and MolPort database to screen out hit molecules as selective HDAC3 inhibitors, followed by different docking stages. MD simulation (50 ns) and MMGBSA study were performed to study the stability of ligand binding modes and with the help of trajectory analysis, to calculate the ligand-receptor complex RMSD (root-mean-square deviation), RMSF (root-mean-square fluctuation) and H-bond distance, etc. Finally, in-silico toxicity studies were performed on top screened molecules and compared with reference drug SAHA and established structure-activity relationship (SAR). The results indicated that compound 31, with high inhibitory potency and less toxicity (probability value 0.418), is suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

Artificial intelligence: Machine learning approach for screening large database and drug discovery

Recent research in drug discovery dealing with many faces difficulties, including development of new drugs during disease outbreak and drug resistance due to rapidly accumulating mutations. Virtual screening is the most widely used method in computer aided drug discovery. It has a prominent ability in screening drug targets from large molecular databases. Recently, a number of web servers have developed for quickly screening publicly accessible chemical databases. In a nutshell, deep learning algorithms and artificial neural networks have modernised the field. Several drug discovery processes have used machine learning and deep learning algorithms, including peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modelling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Although there are presently a wide variety of data-driven AI/ML tools available, the majority of these tools have, up to this point, been developed in the context of non-communicable diseases like cancer, and a number of obstacles have prevented the translation of these tools to the discovery of treatments against infectious diseases. In this review various aspects of AI and ML in virtual screening of large databases were discussed. Here, with an emphasis on antivirals as well as other disease, offers a perspective on the advantages, drawbacks, and hazards of AI/ML techniques in the search for innovative treatments.

Flavonoids as P-glycoprotein inhibitors for multidrug resistance in cancer: an in-silico approach

Cancer has become a leading cause of mortality due to non-communicable diseases after cardiovascular disease worldwide and is increasing day by day at a daunting pace. According to an estimate by 2040 there will be 28.4 million cancer cases. Occurrence of multidrug resistance has further worsened the scenario of available cancer treatment. Among different mechanisms of multidrug resistance efflux of xenobiotics by ABC transporter is of prime importance. P-glycoprotein (P-gp) is the major factor behind occurrence of multidrug resistance due to its wide distribution and invariably big binding cavity. Various generations of chemical inhibitors for P-gp have been designed and tested are not devoid of major side effects. Thus, in present study flavonoids a major class of natural compounds was virtually screened in order to find molecules which can be used as selective P-gp inhibitors to be used along with chemotherapeutics. After screening 4275 molecules from different classes of flavonoids i.e. flavan, flavanol, flavonone, flavone, anthocyanins, and isoflavone, through Glide docking top ten hit molecules were selected based on their binding affinity, binding energy calculation and pharmacokinetic properties. All the hit molecules were found to have docking score within the range of -11.202 to -9.699 kcal/mol showing very strong interaction with the amino acid residues of binding pocket. Whereas, dock score of standard P-gp inhibitor verapamil was -4.984 kcal/mol. The ligand and protein complex were found to be quite stable while run through molecular dynamics simulations.Communicated by Ramaswamy H. Sarma.

Identification of potential inhibitors of Zika virus targeting NS3 helicase using molecular dynamics simulations and DFT studies

Despite the various research efforts towards the drug discovery program for Zika virus treatment, no antiviral drugs or vaccines have yet been discovered. The spread of the mosquito vector and ZIKV infection exposure is expected to accelerate globally due to continuing global travel. The NS3-Hel is a non-structural protein part and involved in different functions such as polyprotein processing, genome replication, etc. It makes an NS3-Hel protein an attractive target for designing novel drugs for ZIKV treatment. This investigation identifies the novel, potent ZIKV inhibitors by virtual screening and elucidates the binding pattern using molecular docking and molecular dynamics simulation studies. The molecular dynamics simulation results indicate dynamic stability between protein and ligand complexes, and the structures keep significantly unchanged at the binding site during the simulation period. All inhibitors found within the acceptable range having drug-likeness properties. The synthetic feasibility score suggests that all screened inhibitors can be easily synthesizable. Therefore, possible inhibitors obtained from this study can be considered a potential inhibitor for NS3 Hel, and further, it could be provided as a lead for drug development.

Al-tameemi Z, AL-Zobaidy MJ. In silico comparison between the mutated and wild-type androgen receptors and their influence on the selection of optimum androgenic receptor blockers for the treatment of prostate cancer

Background: Prostate cancer is a disease that occurs in men aged more than 50 years. In Iraq, 8.89 men per 100,000 population suffer from prostate cancer, with the incidence being 14,016 cases and mortality being 6,367 cases. Despite advances in treatment against prostate cancer, it can become resistant to drugs. Therefore, the aim of current study was to search and identify binding sites for the repositioning of drugs by computational methods (docking).  Methods: Based on the protein structure of the wild androgen receptor, the analysis parameters (22x22x22 on the X, Y, and Z axes) were established. Results: The interactions of the natural ligands with androgen receptor were 10.0 (testosterone) and 10.8 (dihydrotestosterone) while mutated androgen receptor (T877A) had a low affinity with testosterone and dihydrotestosterone (-5.3 and -6.7, respectively). In the interactions of both receptors with the reported inhibitors (antagonists), a decrease with Bicalutamide (-8.3 and -4.3, respectively) and an increase in affinity with Flutamide and Nilutamide (-7.7 and 8.6, wild AR; -8.7 and -9.3 AR T877A) were observed. As for Enzalutamide and Apalutamide (second-generation antagonists), the change was minimal between wild androgen receptor and T877A (-7.6 and -7.7; -7.3 and -7.3, respectively). The change in the affinity of the ligands with androgen receptor and androgen receptor T877A shows how a mutation alters the bonds between these molecules. Conclusion: The identification of key sites and potent inhibitors against abnormal androgen receptor functions will enrich prostate cancer treatments.

Spatial-thermodynamic understanding of stabilization mechanism using computational approaches and molecular-level elucidation of the mechanism of crystal transformation in polymorphic irbesartan nanosuspensions

Irbesartan polymorphisms possess low solubility properties, nanosuspensions represent a method for improving the dissolution. Stabilizers are significant constituents of nanosuspensions. Herein we presented computational research on screening stabilizers and exploring stabilization mechanisms. The crystal transformation mechanism was also investigated. Soluplus-P407 and TPGS-HPMCE5 were screened by spatial conformation and thermodynamic energy analyses. The prepared nanosuspensions improved the dissolution properties of bulk drugs at pH 1.2, 4.5, 6.8. The nanosuspensions stabilization mechanism was analyzed by Molecular docking, Molecular dynamics simulations, Fourier transform infrared spectroscopy and Raman spectroscopy. It might be relate to the decreased enthalpy and Gibbs free energy which were determined by the synergy of external and internal energy factors. The X-ray powder diffraction, differential scanning calorimetry, scanning electron microscopy and transmission electron microscopy showed the crystal structures. The irbesartan B form was transformed in a Soluplus-P407-B/TPGS-HPMCE5-B physical mixture, but not in an SDS (-OH free)-B physical mixture. The intra-proton transfer induced by -OH on the stabilizer might be the transformation mechanism.

Multi-directionally evaluating the formation mechanism of 1,4-dihydropyridine drug nanosuspensions through experimental validation and computer-aided drug design

The poor aqueous solubility of 1,4-dihydropyridine drugs needs to be solved urgently to improve the bioavailability. Nanotechnology can improve drug solubility and dissolution by reducing particle size, but usually a specific polymer or surfactant is required for stabilization. In this study, Poloxamer-407(P-407) was screened as the optimal stabilize through energy simulation, molecular docking and particle size. morphological study, X-ray diffraction, differential scanning calorimetry, Fourier transform infrared spectroscopy, Raman, in vitro dissolution test and molecular simulation of interactions were utilized to explore the formation mechanisms of four 1,4-dihydropyridine drugs/P-407 nanosuspensions. The result shows that the optimized nanosuspensions had the particle size in the nano-size range and maintained the original crystal state. The in vitro dissolution rate of the nanosuspension was 3-4 times higher than the corresponding API and could reduce the restriction of drug dissolution in different pH environments. Raman spectroscopy, FTIR and molecular docking simulations provided strong supporting evidence for the formation mechanism of 1,4-dihydropyridine drugs/P-407 nanosuspensions at the molecular level, which confirmed that the stable intermolecular hydrogen bond adsorption and hydrophobic interaction were formed between the drug and P-407. This research will provide practical concepts and technologies, which are helpful to develop nanosuspensions for the same class of drugs.

A survey on computational methods in discovering protein inhibitors of SARS-CoV-2

The outbreak of acute respiratory disease in 2019, namely Coronavirus Disease-2019 (COVID-19), has become an unprecedented healthcare crisis. To mitigate the pandemic, there are a lot of collective and multidisciplinary efforts in facilitating the rapid discovery of protein inhibitors or drugs against COVID-19. Although many computational methods to predict protein inhibitors have been developed [ 1– 5], few systematic reviews on these methods have been published. Here, we provide a comprehensive overview of the existing methods to discover potential inhibitors of COVID-19 virus, so-called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). First, we briefly categorize and describe computational approaches by the basic algorithms involved in. Then we review the related biological datasets used in such predictions. Furthermore, we emphatically discuss current knowledge on SARS-CoV-2 inhibitors with the latest findings and development of computational methods in uncovering protein inhibitors against COVID-19.

A Multidisciplinary Approach to Coronavirus Disease (COVID-19)

Since December 2019, humanity has faced an important global threat. Many studies have been published on the origin, structure, and mechanism of action of the SARS-CoV-2 virus and the treatment of its disease. The priority of scientists all over the world has been to direct their time to research this subject. In this review, we highlight chemical studies and therapeutic approaches to overcome COVID-19 with seven different sections. These sections are the structure and mechanism of action of SARS-CoV-2, immunotherapy and vaccine, computer-aided drug design, repurposing therapeutics for COVID-19, synthesis of new molecular structures against COVID-19, food safety/security and functional food components, and potential natural products against COVID-19. In this work, we aimed to screen all the newly synthesized compounds, repurposing chemicals covering antiviral, anti-inflammatory, antibacterial, antiparasitic, anticancer, antipsychotic, and antihistamine compounds against COVID-19. We also highlight computer-aided approaches to develop an anti-COVID-19 molecule. We explain that some phytochemicals and dietary supplements have been identified as antiviral bioproducts, which have almost been successfully tested against COVID-19. In addition, we present immunotherapy types, targets, immunotherapy and inflammation/mutations of the virus, immune response, and vaccine issues.

Epitope-Based Potential Vaccine Candidate for Humoral and Cell-Mediated Immunity to Combat Severe Acute Respiratory Syndrome Coronavirus 2 Pandemic

The emergence of severe acute respiratory syndrome from novel Coronavirus (SARS-CoV-2) has put an immense pressure worldwide where vaccination is believed to be an efficient way for developing hard immunity. Herein, we employ immunoinformatic tools to identify B-cell, T-cell epitopes associated with the spike protein of SARS-CoV-2, which is important for genome release. The results showed that the highly immunogenic epitopes located at the stalk part are mostly conserved compared to the receptor binding domain (RDB). Further, two vaccine candidates were computationally modeled from the linear B-cell, T-cell epitopes. Molecular docking reveals the crucial interactions of the vaccines with immune-receptors, and their stability is assessed by MD simulation studies. The chimeric vaccines showed remarkable binding affinity toward the immune cell receptors computed by the MM/PBSA method. van der Waals and electrostatic interactions are found to be the dominant factors for the stability of the complexes. The molecular-level interaction obtained from this study may provide deeper insight into the process of vaccine development against the pandemic of COVID-19.

A perspective on potential target proteins of COVID-19: Comparison with SARS-CoV for designing new small molecules

SARS-CoV-2 (COVID-19) epidemic has created an unprecedented medical and economic crisis all over the world. SARS-CoV-2 is found to have more contagious character as compared to MERS-CoV and is spreading in a very fast manner all around the globe. It has affected over 31 million people all over the world till date. This virus shares around 80% of genome similarity with SARS-CoV. In this perspective, we have explored three major targets namely; SARS-CoV-2 spike (S) protein, RNA dependent RNA polymerase, and 3CL or Mpro Protease for the inhibition of SARS-CoV-2. These targets have attracted attention of the medicinal chemists working on computer-aided drug design in developing new small molecules that might inhibit these targets for combating COVID-19 disease. Moreover, we have compared the similarity of these target proteins with earlier reported coronavirus (SARS-CoV). We have observed that both the coronaviruses share around 80% similarity in their amino acid sequence. The key amino acid interactions which can play a crucial role in designing new small molecule inhibitors against COVID-19 have been reported in this perspective. Authors believe that this study will help the medicinal chemists to understand the key amino acids essential for interactions at the active site of target proteins in SARS-CoV-2, based on their similarity with earlier reported viruses. In this review, we have also described the lead molecules under various clinical trials for their efficacy against COVID-19.

Is highly expressed ACE 2 in pregnant women "a curse" in times of COVID-19 pandemic?

Angiotensin-converting enzyme 2 (ACE 2) is a membrane-bound enzyme that cleaves angiotensin II (Ang II) into angiotensin (1-7). It also serves as an important binding site for SARS-CoV-2, thereby, facilitating viral entry into target host cells. ACE 2 is abundantly present in the intestine, kidney, heart, lungs, and fetal tissues. Fetal ACE 2 is involved in myocardium growth, lungs and brain development. ACE 2 is highly expressed in pregnant women to compensate preeclampsia by modulating angiotensin (1-7) which binds to the Mas receptor, having vasodilator action and maintain fluid homeostasis. There are reports available on Zika, H1N1 and SARS-CoV where these viruses have shown to produce fetal defects but very little is known about SARS-CoV-2 involvement in pregnancy, but it might have the potential to interact with fetal ACE 2 and enhance COVID-19 transmission to the fetus, leading to fetal morbidity and mortality. This review sheds light on a path of SARS-CoV-2 transmission risk in pregnancy and its possible link with fetal ACE 2.