Predicting and designing therapeutics against the Nipah virus

Evaluation of the interaction between potent small molecules against the Nipah virus Glycoprotein in Malaysia and Bangladesh strains, accompanied by the human Ephrin-B2 and Ephrin-B3 receptors; a simulation approach

Malaysia reported the first human case of Nipah virus (NiV) in late September 1998 with encephalitis and respiratory symptoms. As a result of viral genomic mutations, two main strains (NiV-Malaysia and NiV-Bangladesh) have spread around the world. There are no licensed molecular therapeutics available for this biosafety level 4 pathogen. NiV attachment glycoprotein plays a critical role in viral transmission through its human receptors (Ephrin-B2 and Ephrin-B3), so identifying small molecules that can be repurposed to inhibit them is crucial to developing anti-NiV drugs. Consequently, in this study annealing simulations, pharmacophore modeling, molecular docking, and molecular dynamics were used to evaluate seven potential drugs (Pemirolast, Nitrofurantoin, Isoniazid Pyruvate, Eriodictyol, Cepharanthine, Ergoloid, and Hypericin) against NiV-G, Ephrin-B2, and Ephrin-B3 receptors. Based on the annealing analysis, Pemirolast for efnb2 protein and Isoniazid Pyruvate for efnb3 receptor were repurposed as the most promising small molecule candidates. Furthermore, Hypericin and Cepharanthine, with notable interaction values, are the top Glycoprotein inhibitors in Malaysia and Bangladesh strains, respectively. In addition, docking calculations revealed that their binding affinity scores are related to efnb2-pem (- 7.1 kcal/mol), efnb3-iso (- 5.8 kcal/mol), gm-hyp (- 9.6 kcal/mol), gb-ceph (- 9.2 kcal/mol). Finally, our computational research minimizes the time-consuming aspects and provides options for dealing with any new variants of Nipah virus that might emerge in the future.

Pangenomic and immunoinformatics based analysis of Nipah virus revealed CD4+ and CD8+ T-Cell epitopes as potential vaccine candidates

Introduction: Nipah (NiV) is the zoonotic deadly bat-borne virus that causes neurological and respiratory infections which ultimately lead to death. There are 706 infected cases reported up till now especially in Asia, out of which 409 patients died. There is no vaccine and effective treatment available for NiV infections and we have to timely design such strategies as world could not bear another pandemic situation. Methods: In this study, we screened viral proteins of NiV strains based on pangenomics analysis, antigenicity, molecular weight, and sub-cellular localization. The immunoproteomics based approach was used to predict T-cell epitopes of MHC class-I and II as potential vaccine candidates. These epitopes are capable to activate CD4+, CD8+, and T-cell dependent B-lymphocytes. Results: The two surface proteins including fusion glycoprotein (F) and attachment glycoprotein (G) are antigenic with molecular weights of 60 kDa and 67 kDa respectively. Three epitopes of F protein (VNYNSEGIA, PNFILVRNT, and IKMIPNVSN) were ranked and selected based on the binding affinity with MHC class-I, and 3 epitopes (VILNKRYYS, ILVRNTLIS, and VKLQETAEK) with MHC-II molecules. Similarly, for G protein, 3 epitopes each for MHC-I (GKYDKVMPY, ILKPKLISY, and KNKIWCISL) and MHC-II (LRNIEKGKY, FLIDRINWI, and FLLKNKIWC) with substantial binding energies were predicted. Based on the physicochemical properties, all these epitopes are non-toxic, hydrophilic, and stable. Conclusion: Our vaccinomics and system-level investigation could help to trigger the host immune system to prevent NiV infection.

In silico identification of potential drug-like molecules against G glycoprotein of Nipah virus by molecular docking, DFT studies, and molecular dynamic simulation

Nipah virus (NiV) is a novel zoonotic pathogen that belongs to the Paramyxovirus family. The pathogen has infected a number of people in countries like Bangladesh, India, Singapore, and Malaysia with high mortality rates. Although the NiV has been classified as a biosafety level four pathogen (BSL-4), there is no drug approved for treatment against it. In this study, the G glycoprotein of the NiV was chosen as an antiviral target. Based on ADMET criteria, BBB- and BBB + group compounds were screened out of the Gold & platinum Asinex library containing 211620 compounds. After careful evaluation, the selected ligands were then virtually screened to identify the potential inhibitors against the G glycoprotein of the NiV through molecular docking, density functional theory (DFT), and molecular dynamic (MD) simulation studies. In our study we identified 5-(1,3-Benzodioxol-5-yl)-2-[(3-fluorobenzyl)sulfanyl]-5,8-dihydropyrido[2,3-d]pyrimidine-4,7(1H,6H)-dione (from BBB- group) and 7,7-Dimethyl-1-(4-methylphenyl)-3-(4-morpholinylcarbonyl)-7,8-dihydro-2,5(1H,6H)-quinolinedione) (from BBB + group) as potential compounds for the prevention and treatment of NiV related diseases.Communicated by Ramaswamy H. Sarma.

Computational Identification of Potential Multitarget Inhibitors of Nipah Virus by Molecular Docking and Molecular Dynamics

Nipah virus (NiV) is a recently emerged paramyxovirus that causes severe encephalitis and respiratory diseases in humans. Despite the severe pathogenicity of this virus and its pandemic potential, not even a single type of molecular therapeutics has been approved for human use. Considering the role of NiV attachment glycoprotein G (NiV-G), fusion glycoprotein (NiV-F), and nucleoprotein (NiV-N) in virus replication and spread, these are the most attractive targets for anti-NiV drug discovery. Therefore, to prospect for potential multitarget chemical/phytochemical inhibitor(s) against NiV, a sequential molecular docking and molecular-dynamics-based approach was implemented by simultaneously targeting NiV-G, NiV-F, and NiV-N. Information on potential NiV inhibitors was compiled from the literature, and their 3D structures were drawn manually, while the information and 3D structures of phytochemicals were retrieved from the established structural databases. Molecules were docked against NiV-G (PDB ID:2VSM), NiV-F (PDB ID:5EVM), and NiV-N (PDB ID:4CO6) and then prioritized based on (1) strong protein-binding affinity, (2) interactions with critically important binding-site residues, (3) ADME and pharmacokinetic properties, and (4) structural stability within the binding site. The molecules that bind to all the three viral proteins (NiV-G ∩ NiV-F ∩ NiV-N) were considered multitarget inhibitors. This study identified phytochemical molecules RASE0125 (17-O-Acetyl-nortetraphyllicine) and CARS0358 (NA) as distinct multitarget inhibitors of all three viral proteins, and chemical molecule ND_nw_193 (RSV604) as an inhibitor of NiV-G and NiV-N. We expect the identified compounds to be potential candidates for in vitro and in vivo antiviral studies, followed by clinical treatment of NiV.

Characterizing and explaining the impact of disease-associated mutations in proteins without known structures or structural homologs

Mutations in human proteins lead to diseases. The structure of these proteins can help understand the mechanism of such diseases and develop therapeutics against them. With improved deep learning techniques, such as RoseTTAFold and AlphaFold, we can predict the structure of proteins even in the absence of structural homologs. We modeled and extracted the domains from 553 disease-associated human proteins without known protein structures or close homologs in the Protein Databank. We noticed that the model quality was higher and the Root mean square deviation (RMSD) lower between AlphaFold and RoseTTAFold models for domains that could be assigned to CATH families as compared to those which could only be assigned to Pfam families of unknown structure or could not be assigned to either. We predicted ligand-binding sites, protein–protein interfaces and conserved residues in these predicted structures. We then explored whether the disease-associated missense mutations were in the proximity of these predicted functional sites, whether they destabilized the protein structure based on ddG calculations or whether they were predicted to be pathogenic. We could explain 80% of these disease-associated mutations based on proximity to functional sites, structural destabilization or pathogenicity. When compared to polymorphisms, a larger percentage of disease-associated missense mutations were buried, closer to predicted functional sites, predicted as destabilizing and pathogenic. Usage of models from the two state-of-the-art techniques provide better confidence in our predictions, and we explain 93 additional mutations based on RoseTTAFold models which could not be explained based solely on AlphaFold models.

Medical countermeasures against henipaviruses: a review and public health perspective

Henipaviruses, including Nipah virus, are regarded as pathogens of notable epidemic potential because of their high pathogenicity and the paucity of specific medical countermeasures to control infections in humans. We review the evidence of medical countermeasures against henipaviruses and project their cost in a post-COVID-19 era. Given the sporadic and unpredictable nature of henipavirus outbreaks, innovative strategies will be needed to circumvent the infeasibility of traditional phase 3 clinical trial regulatory pathways. Stronger partnerships with scientific institutions and regulatory authorities in low-income and middle-income countries can inform coordination of appropriate investments and development of strategies and normative guidelines for the deployment and equitable use of multiple medical countermeasures. Accessible measures should include global, regional, and endemic in-country stockpiles of reasonably priced small molecules, monoclonal antibodies, and vaccines as part of a combined collection of products that could help to control henipavirus outbreaks and prevent future pandemics.

Nipah virus: a potential pandemic agent in the context of the current severe acute respiratory syndrome coronavirus 2 pandemic

For centuries, zoonotic diseases have been responsible for various outbreaks resulting in the deaths of millions of people. The best example of this is the current coronavirus disease 2019 (COVID-19) pandemic. Like severe acute respiratory syndrome coronavirus, Nipah virus is another deadly virus which has caused several outbreaks in the last few years. Though it causes a low number of infections, disease severity results in a higher death rate. In the context of the recent COVID-19 pandemic, we speculate that many countries will be unable to deal with the sudden onset of such a viral outbreak. Thus, further research and attention to the virus are needed to address future outbreaks.

Viral pandemic preparedness: A pluripotent stem cell-based machine-learning platform for simulating SARS-CoV-2 infection to enable drug discovery and repurposing

Infection with the SARS-CoV-2 virus has rapidly become a global pandemic for which we were not prepared. Several clinical trials using previously approved drugs and drug combinations are urgently under way to improve the current situation. A vaccine option has only recently become available, but worldwide distribution is still a challenge. It is imperative that, for future viral pandemic preparedness, we have a rapid screening technology for drug discovery and repurposing. The primary purpose of this research project was to evaluate the DeepNEU stem-cell based platform by creating and validating computer simulations of artificial lung cells infected with SARS-CoV-2 to enable the rapid identification of antiviral therapeutic targets and drug repurposing. The data generated from this project indicate that (a) human alveolar type lung cells can be simulated by DeepNEU (v5.0), (b) these simulated cells can then be infected with simulated SARS-CoV-2 virus, (c) the unsupervised learning system performed well in all simulations based on available published wet lab data, and (d) the platform identified potentially effective anti-SARS-CoV2 combinations of known drugs for urgent clinical study. The data also suggest that DeepNEU can identify potential therapeutic targets for expedited vaccine development. We conclude that based on published data plus current DeepNEU results, continued development of the DeepNEU platform will improve our preparedness for and response to future viral outbreaks. This can be achieved through rapid identification of potential therapeutic options for clinical testing as soon as the viral genome has been confirmed.

Methods for Molecular Modelling of Protein Complexes

Biological processes are often mediated by complexes formed between proteins and various biomolecules. The 3D structures of such protein-biomolecule complexes provide insights into the molecular mechanism of their action. The structure of these complexes can be predicted by various computational methods. Choosing an appropriate method for modelling depends on the category of biomolecule that a protein interacts with and the availability of structural information about the protein and its interacting partner. We intend for the contents of this chapter to serve as a guide as to what software would be the most appropriate for the type of data at hand and the kind of 3D complex structure required. Particularly, we have dealt with protein-small molecule ligand, protein-peptide, protein-protein, and protein-nucleic acid interactions.Most, if not all, model building protocols perform some sampling and scoring. Typically, several alternate conformations and configurations of the interactors are sampled. Each such sample is then scored for optimization. To boost the confidence in these predicted models, their assessment using other independent scoring schemes besides the inbuilt/default ones would prove to be helpful. This chapter also lists such software and serves as a guide to gauge the fidelity of modelled structures of biomolecular complexes.

Multi-step molecular docking and dynamics simulation-based screening of large antiviral specific chemical libraries for identification of Nipah virus glycoprotein inhibitors

Nipah virus (NiV) infections are highly contagious and can cause severe febrile encephalitis. An outbreak of NiV infection has reported high mortality rates in Southeast Asian countries including Bangladesh, East Timor, Malaysia, Papua New Guinea, Vietnam, Cambodia, Indonesia, Madagascar, Philippines, Thailand and India. Considering the high risk for an epidemic outbreak, the World Health Organization (WHO) declared NiV as an emerging priority pathogen. However, there are no effective therapeutics or any FDA approved drugs available for the treatment of this infection. Among the known nine proteins of NiV, glycoprotein plays an important role in initiating the entry of viruses and attaching to the host cell receptors. Herein, three antiviral databases consisting of 79,892 chemical entities have been computationally screened against NiV glycoprotein (NiV-G). Particularly, multi-step molecular docking followed by extensive molecular binding interactions analyses, binding free energy estimation, in silico pharmacokinetics, synthetic accessibility and toxicity profile evaluations have been carried out for initial identification of potential NiV-G inhibitors. Further, molecular dynamics (MD) simulation has been performed to understand the dynamic properties of NiV-G protein-bound with proposed five inhibitors (G1-G5) and their interactions behavior, and any conformational changes in NiV-G protein during simulations. Moreover, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) based binding free energies (∆G) has been calculated from all MD simulation trajectories to understand the energy contribution of each proposed compound in maintaining and stabilizing the complex binding interactions with NiV-G protein. Proposed compounds showed high negative ∆G values ranging from -166.246 to -226.652 kJ/mol indicating a strong affinity towards the NiV-G protein.

A proposed treatment for pathogenic enveloped viruses having high rates of mutation or replication

Several enveloped viruses, particularly some RNA viruses, have high rates of mutation or replication, which can make them virulent pathogens in humans and other mammals. A proposed treatment could use synthesized proteins to mask pathogenic viral surface proteins to quickly induce an immune attack on specific enveloped viruses by using existing immune cells. One treatment could inject dual‐protein ligand masks into patients' bloodstreams to mask pathogenic surface proteins used to infect mammalian cells. The mammalian immune system already uses an analogous, more complex structure called a pentraxin to neutralize some pathogens by connecting their surface proteins to immune cells. And several types of antiviral peptides have already experimentally demonstrated effectiveness in blocking various viral pathogen infections. These treatments offer advantages, especially for currently untreatable viral pathogens. Furthermore, using dual‐protein ligands and the antigenic memory of some sub‐populations of NK cells would also allow the creation of defacto vaccines based on a host's NK cells, instead of vaccines utilizing CD4 and CD8 α:β T cells, which are limited by the requirement of MHC presentation of the target antigens to α:β T cells. Targeted NK cell vaccines could attack host cells latently or actively infected by intracellular pathogens, even host cells having pathogen downregulated MHC antigen presentation. Eight postulates concerning the effects of pathogen mutation, or change in phenotype from genetic recombination or rearrangement, and replication rates on pathogen vs host dominance are also listed, which should be applicable to viral and non‐viral pathogens.

Antiviral Peptides: Identification and Validation

Despite rapid advances in the human healthcare, the infection caused by certain viruses results in high morbidity and mortality accentuate the importance for development of new antivirals. The existing antiviral drugs are limited, due to their inadequate response, increased rate of resistance and several adverse side effects. Therefore, one of the newly emerging field “peptide-based therapeutics” against viruses is being explored and seems promising. Over the last few years, a lot of scientific effort has been made for the identification of novel and potential peptide-based therapeutics using various advanced technologies. Consequently, there are more than 60 approved peptide drugs available for sale in the market of United States, Europe, Japan, and some Asian countries. Moreover, the number of peptide drugs undergoing the clinical trials is rising gradually year by year. The peptide-based antiviral therapeutics have been approved for the Human immunodeficiency virus (HIV), Influenza virus and Hepatitis virus (B and C). This review enlightens the various peptide sources and the different approaches that have contributed to the search of potential antiviral peptides. These include computational approaches, natural and biological sources (library based high throughput screening) for the identification of lead peptide molecules against their target. Further the applications of few advanced techniques based on combinatorial chemistry and molecular biology have been illustrated to measure the binding parameters such as affinity and kinetics of the screened interacting partners. The employment of these advanced techniques can contribute to investigate antiviral peptide therapeutics for emerging infections.