Structure-based drug designing and immunoinformatics approach for SARS-CoV-2

Exploring the Therapeutic Potential of Petiveria alliacea L. Phytochemicals: A Computational Study on Inhibiting SARS-CoV-2's Main Protease (Mpro)

The outbreak of SARS-CoV-2, also known as the COVID-19 pandemic, is still a critical risk factor for both human life and the global economy. Although, several promising therapies have been introduced in the literature to inhibit SARS-CoV-2, most of them are synthetic drugs that may have some adverse effects on the human body. Therefore, the main objective of this study was to carry out an in-silico investigation into the medicinal properties of Petiveria alliacea L. (P. alliacea L.)-mediated phytocompounds for the treatment of SARS-CoV-2 infections since phytochemicals have fewer adverse effects compared to synthetic drugs. To explore potential phytocompounds from P. alliacea L. as candidate drug molecules, we selected the infection-causing main protease (Mpro) of SARS-CoV-2 as the receptor protein. The molecular docking analysis of these receptor proteins with the different phytocompounds of P. alliacea L. was performed using AutoDock Vina. Then, we selected the three top-ranked phytocompounds (myricitrin, engeletin, and astilbin) as the candidate drug molecules based on their highest binding affinity scores of -8.9, -8.7 and -8.3 (Kcal/mol), respectively. Then, a 100 ns molecular dynamics (MD) simulation study was performed for their complexes with Mpro using YASARA software, computed RMSD, RMSF, PCA, DCCM, MM/PBSA, and free energy landscape (FEL), and found their almost stable binding performance. In addition, biological activity, ADME/T, DFT, and drug-likeness analyses exhibited the suitable pharmacokinetics properties of the selected phytocompounds. Therefore, the results of this study might be a useful resource for formulating a safe treatment plan for SARS-CoV-2 infections after experimental validation in wet-lab and clinical trials.

Non-clinical investigations about cytotoxic and anti-platelet activities of gamma-terpinene

Gamma-terpinene (γ-TPN) is a cyclohexane monoterpene isolated from plant essential oils, such as tea tree (Melaleuca alternifolia), oregano (Origanum vulgare), rosemary (Rosmarinus officinalis L.), thyme (Thymus vulgaris Marchand), and eucalyptus (Eucalyptus sp.). Terpenes are widely studied molecules pharmacologically active on the cardiovascular system, hemostasis, and antioxidant actions. Herein, it was investigated the cytotoxic and antiplatelet activity of γ-TPN using different non-clinical laboratory models. For in silico evaluation, the PreADMET, SwissADME, and SwissTargetPrediction softwares were used. Molecular docking was performed using the AutoDockVina and BIOVIA Discovery Studio databases. The cytotoxicity of γ-TPN was analyzed by the MTT assay upon normal murine endothelial SVEC4-10 and fibroblast L-929 cells. Platelet aggregation was evaluated with platelet-rich (PRP) and platelet-poor (PPP) plasma from spontaneously hypertensive rats (SHR), in addition to SVEC4-10 cells pre-incubated with γ-TPN (50, 100, and 200 µM) for 24 h. SHR animals were pre-treated by gavage with γ-TPN for 7 days and divided into four groups (negative control, 25, 50, and 100 mg/kg). Blood samples were collected to measure nitrite using the Griess reagent. Gamma-TPN proved to be quite lipid-soluble (Log P = +4.50), with a qualified profile of similarity to the drug, good bioavailability, and adequate pharmacokinetics. It exhibited affinity mainly for the P2Y12 receptor (6.450 ± 0.232 Kcal/mol), moderate cytotoxicity for L-929 (CC50 = 333.3 µM) and SVEC 4-10 (CC50 = 366.7 µM) cells. The presence of γ-TPN in SVEC 4-10 cells was also able to reduce platelet aggregation by 51.57 and 44.20% at lower concentrations (50 and 100 µM, respectively). Then, γ-TPN has good affinity with purinergic receptors and an effect on the reversal of platelet aggregation and oxidative stress, being promising and safe for therapeutic targets and subsequent studies on the control of thromboembolic diseases.

DReAmocracy: A Method to Capitalise on Prior Drug Discovery Efforts to Highlight Candidate Drugs for Repurposing

In the area of drug research, several computational drug repurposing studies have highlighted candidate repurposed drugs, as well as clinical trial studies that have tested/are testing drugs in different phases. To the best of our knowledge, the aggregation of the proposed lists of drugs by previous studies has not been extensively exploited towards generating a dynamic reference matrix with enhanced resolution. To fill this knowledge gap, we performed weight-modulated majority voting of the modes of action, initial indications and targeted pathways of the drugs in a well-known repository, namely the Drug Repurposing Hub. Our method, DReAmocracy, exploits this pile of information and creates frequency tables and, finally, a disease suitability score for each drug from the selected library. As a testbed, we applied this method to a group of neurodegenerative diseases (Alzheimer's, Parkinson's, Huntington's disease and Multiple Sclerosis). A super-reference table with drug suitability scores has been created for all four neurodegenerative diseases and can be queried for any drug candidate against them. Top-scored drugs for Alzheimer's Disease include agomelatine, mirtazapine and vortioxetine; for Parkinson's Disease, they include apomorphine, pramipexole and lisuride; for Huntington's, they include chlorpromazine, fluphenazine and perphenazine; and for Multiple Sclerosis, they include zonisamide, disopyramide and priralfimide. Overall, DReAmocracy is a methodology that focuses on leveraging the existing drug-related experimental and/or computational knowledge rather than a predictive model for drug repurposing, offering a quantified aggregation of existing drug discovery results to (1) reveal trends in selected tracks of drug discovery research with increased resolution that includes modes of action, targeted pathways and initial indications for the investigated drugs and (2) score new candidate drugs for repurposing against a selected disease.

Targeting RdRp of SARS-CoV-2 with De Novo Molecule Generation

Viruses are known for their extremely high mutation rates, allowing them to evade both the human immune system and many forms of standard medicine. Despite this, the RNA dependent RNA polymerase (RdRp) of the RNA viruses has been largely conserved, and any significant mutation of this protein is unlikely. The recent COVID-19 pandemic presents a need for therapeutics. We have designed a de novo drug design algorithm that generates strong binding ligands from scratch, based on only the structure of the target protein's receptor. In this paper, we applied our method to target SARS-CoV-2 RdRp and generated several de novo molecules. We then chose some drug molecules based on the structural similarity to some of our strongest binding de novo molecules. Subsequently, we showed, using rigorous all-atom explicit-water free energy calculations in near-microsecond time scales using state-of-the-art well-tempered metadynamics simulations, that some of our de novo generated ligands bind more strongly to RdRp than the recent FDA approved drug remdesivir in its active form, remdesivir triphosphate (RTP). We elucidated the binding mechanism for some of the top binders and compared it with RTP. We believe that this work will be useful both by presenting lead structures for RdRp inhibition and by delivering key insights into the residues of the protein potentially involved in the binding/unbinding of these small molecule drugs, leading to more targeted studies in the future.

Combined Immunoinformatics to Design and Evaluate a Multi-Epitope Vaccine Candidate against Streptococcus suis Infection

Streptococcus suis (S. suis) is a zoonotic pathogen with multiple serotypes, and thus, multivalent vaccines generating cross-protection against S. suis infections are urgently needed to improve animal welfare and reduce antibiotic abuse. In this study, we established a systematic and comprehensive epitope prediction pipeline based on immunoinformatics. Ten candidate epitopes were ultimately selected for building the multi-epitope vaccine (MVSS) against S. suis infections. The ten epitopes of MVSS were all derived from highly conserved, immunogenic, and virulence-associated surface proteins in S. suis. In silico analyses revealed that MVSS was structurally stable and affixed with immune receptors, indicating that it would likely trigger strong immunological reactions in the host. Furthermore, mice models demonstrated that MVSS elicited high titer antibodies and diminished damages in S. suis serotype 2 and Chz infection, significantly reduced sequelae, induced cytokine transcription, and decreased organ bacterial burdens after triple vaccination. Meanwhile, anti-rMVSS serum inhibited five important S. suis serotypes in vitro, exerted beneficial protective effects against S. suis infections and significantly reduced histopathological damage in mice. Given the above, it is possible to develop MVSS as a universal subunit vaccine against multiple serotypes of S. suis infections.

Advancing PEDV Vaccination: Comparison between Inactivated and Flagellin N-Terminus-Adjuvanted Subunit Vaccines

Vaccinations can serve as an important preventive measure against the porcine epidemic diarrhea (PED) virus that currently threatens the swine industry. This study focuses on the development of a fusion protein vaccine, FliC99-mCOE, which combines the N-terminus of flagellin (FliC99) with a modified core neutralizing epitope (mCOE) of PEDV. In silico immunoinformatic analysis confirmed the construct's non-toxic, non-allergenic, and highly antigenic nature. Molecular docking and molecular dynamics (MD) simulations demonstrated FliC99-mCOE's strong binding to the TLR-5 immunological receptor. Repeated exposure simulations and immunological simulations suggested enhanced cell-mediated immunity. Both FliC99-mCOE and an inactivated PEDV vaccine were produced and tested in mice. The results from cell proliferation, ELISA, and neutralization assays indicated that FliC99-mCOE effectively stimulated cellular immunity and neutralized PEDV. We conclude that the FliC99-mCOE fusion protein may serve as a promising vaccine candidate against PEDV.

Immunodominant conserved moieties on spike protein of SARS-CoV-2 renders virulence factor for the design of epitope-based peptide vaccines

The outbreak of novel SARS-CoV-2 virion has wreaked havoc with a high prevalence of respiratory illness and high transmission due to a vague understanding of the viral antigenicity, augmenting the dire challenge to public health globally. This viral member necessitates the expansion of diagnostic and therapeutic tools to track its transmission and confront it through vaccine development. Therefore, prophylactic strategies are mandatory. Virulent spike proteins can be the most desirable candidate for the computational design of vaccines targeting SARS-CoV-2, followed by the meteoric development of immune epitopes. Spike protein was characterized using existing bioinformatics tools with a unique roadmap related to the immunological profile of SARS-CoV-2 to predict immunogenic virulence epitopes based on antigenicity, allergenicity, toxicity, immunogenicity, and population coverage. Applying in silico approaches, a set of twenty-four B lymphocyte-based epitopes and forty-six T lymphocyte-based epitopes were selected. The predicted epitopes were evaluated for their intrinsic properties. The physico-chemical characterization of epitopes qualifies them for further in vitro and in vivo analysis and pre-requisite vaccine development. This study presents a set of screened epitopes that bind to HLA-specific allelic proteins and can be employed for designing a peptide vaccine construct against SARS-CoV-2 that will confer vaccine-induced protective immunity due to its structural stability.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-023-00852-9.

Rapidly determining the 3D structure of proteins by surface-enhanced Raman spectroscopy

Despite great advances in protein structure analysis, label-free and ultrasensitive methods to obtain the natural and dynamic three-dimensional (3D) structures are still urgently needed. Surface-enhanced Raman spectroscopy (SERS) can be a good candidate, whereas the complexity originated from the interactions between the protein and the gradient surface electric field makes it extremely challenging to determine the protein structure. Here, we propose a deciphering strategy for accurate determination of 3D protein structure from experimental SERS spectra in seconds by simply summing SERS spectra of isolated amino acids in electric fields of different strength with their orientations in protein. The 3D protein structure can be reconstructed by comparing the experimental spectra obtained in a well-defined gap-mode SERS configuration with the simulated spectra. The gradient electric field endows SERS with a unique advantage to section biomolecules with atomic precision, which makes SERS a competent tool for monitoring biomolecular events under physiological conditions.

Identification of two novel T cell epitopes on the E2 protein of classical swine fever virus C-strain

C-strain, also known as the HCLV strain, is a well-known live attenuated vaccine against classical swine fever (CSF), a devastating disease caused by classical swine fever virus (CSFV). Vaccination with C-strain induces a rapid onset of protection, which is associated with virus-specific gamma interferon (IFN-γ)-secreting CD8+ T cell responses. The E2 protein of CSFV is a major protective antigen. However, the T cell epitopes on the E2 protein remain largely unknown. In this study, eight overlapping nonapeptides of the E2 protein were predicted and synthesized to screen for potential T cell epitopes on the CSFV C-strain E2 protein. Molecular docking was performed on the candidate epitopes with the swine leukocyte antigen-1*0401. The analysis obtained two highly conserved T cell epitopes, 90STEEMGDDF98 and 331ATDRHSDYF339, which were further identified by enzyme-linked immunospot assay. Interestingly, the mutants deleting or substituting the epitopes are nonviable. Further analysis demonstrated that 90STEEMGDDF98 is crucial for the E2 homodimerization, while CSFV infection is significantly inhibited by the 331ATDRHSDYF339 peptide treatment. The two novel T cell epitopes can be used to design new vaccines that are able to provide rapid-onset protection.

Nanomedicine for drug resistant pathogens and COVID-19 using mushroom nanocomposite inspired with bacteriocin – A Review

Multidrug resistant (MDR) pathogens have become a major global health challenge and have severely threatened the health of society. Current conditions have gotten worse as a result of the COVID-19 pandemic, and infection rates in the future will rise. It is necessary to design, respond effectively, and take action to address these challenges by investigating new avenues. In this regard, the fabrication of metal NPs utilized by various methods, including green synthesis using mushroom, is highly versatile, cost-effective, eco-compatible, and superior. In contrast, biofabrication of metal NPs can be employed as a powerful weapon against MDR pathogens and have immense biomedical applications. In addition, the advancement in nanotechnology has made possible to modify the nanomaterials and enhance their activities. Metal NPs with biomolecules composite to prevents their microbial adhesion and kills the microbial pathogens through biofilm formation. Bacteriocin is an excellent antimicrobial peptide that works well as an augmentation substance to boost the antimicrobial effects. As a result, we concentrate on the creation of new, eco-compatible mycosynthesized metal NPs with bacteriocin nanocomposite via electrostatic, covalent, or non-covalent bindings. The synergistic benefits of metal NPs with bacteriocin to combat MDR pathogens and COVID-19, as well as other biomedical applications, are discussed in this review. Moreover, the importance of the adverse outcome pathway (AOP) in risk analysis of manufactured metal nanocomposite nanomaterial and their future possibilities also discussed.

The paradigm of prophylactic viral outbreaks measures by microbial biosurfactants

The recent emergence and outbreak of the COVID-19 pandemic confirmed the incompetence of countries across the world to deal with a global public health emergency. Although the recent advent of vaccines is an important prophylactic measure, effective clinical therapy for SARS-Cov-2 is yet to be discovered. With the increasing mortality rate, research has been focused on understanding the pathogenic mechanism and clinical parameters to comprehend COVID-19 infection and propose new avenues for naturally occurring molecules with novel therapeutic properties to alleviate the current situation. In accordance with recent clinical studies and SARS-CoV-2 infection markers, cytokine storm and oxidative stress are entwined pathogenic processes in COVID-19 progression. Lately, Biosurfactants (BSs) have been studied as one of the most advanced biomolecules of microbial origin with anti-inflammatory, antioxidant, antiviral properties, antiadhesive, and antimicrobial properties. Therefore, this review inspects available literature and proposes biosurfactants with these properties to be encouraged for their extensive study in dealing with the current pandemic as new pharmaceutics in the prevention and control of viral spread, treating the symptoms developed after the incubation period through different therapeutic approaches and playing a potential drug delivery model.

Effect of surfactants on SARS-CoV-2: Molecular dynamics simulations

Surfactants are commonly used as disinfection agents in personal care products against bacteria and viruses, including SARS-CoV-2. However, there is a lack of understanding of the molecular mechanisms of the inactivation of viruses by surfactants. Here, we employ coarse grain (CG) and all-atom (AA) molecular dynamics simulations to investigate the interaction between general families of surfactants and the SARS-CoV-2 virus. To this end, we considered a CG model of a full virion. Overall, we found that surfactants have only a small impact on the virus envelope, being inserted into the envelope without dissolving it or generating pores, at the conditions considered here. However, we found that surfactants may induce a deep impact on the spike protein of the virus (responsible for its infectivity), easily covering it and inducing its collapse over the envelope surface of the virus. AA simulations confirmed that both negatively and positively charged surfactants are able to extensively adsorb over the spike protein and get inserted into the virus envelope. Our results suggest that the best strategy for the design of surfactants as virucidal agents will be to focus on those strongly interacting with the spike protein.

Application of the PHENotype SIMulator for rapid identification of potential candidates in effective COVID-19 drug repurposing

The current, rapidly diversifying pandemic has accelerated the need for efficient and effective identification of potential drug candidates for COVID-19. Knowledge on host-immune response to SARS-CoV-2 infection, however, remains limited with few drugs approved to date. Viable strategies and tools are rapidly arising to address this, especially with repurposing of existing drugs offering significant promise. Here we introduce a systems biology tool, the PHENotype SIMulator, which -by leveraging available transcriptomic and proteomic databases-allows modeling of SARS-CoV-2 infection in host cells in silico to i) determine with high sensitivity and specificity (both>96%) the viral effects on cellular host-immune response, resulting in specific cellular SARS-CoV-2 signatures and ii) utilize these cell-specific signatures to identify promising repurposable therapeutics. Powered by this tool, coupled with domain expertise, we identify several potential COVID-19 drugs including methylprednisolone and metformin, and further discern key cellular SARS-CoV-2-affected pathways as potential druggable targets in COVID-19 pathogenesis.

Recent trends in next generation immunoinformatics harnessed for universal coronavirus vaccine design

The ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has globally devastated public health, the economies of many countries and quality of life universally. The recent emergence of immune-escaped variants and scenario of vaccinated individuals being infected has raised the global concerns about the effectiveness of the current available vaccines in transmission control and disease prevention. Given the high rate mutation of SARS-CoV-2, an efficacious vaccine targeting against multiple variants that contains virus-specific epitopes is desperately needed. An immunoinformatics approach is gaining traction in vaccine design and development due to the significant reduction in time and cost of immunogenicity studies and increasing reliability of the generated results. It can underpin the development of novel therapeutic methods and accelerate the design and production of peptide vaccines for infectious diseases. Structural proteins, particularly spike protein (S), along with other proteins have been studied intensively as promising coronavirus vaccine targets. Numbers of promising online immunological databases, tools and web servers have widely been employed for the design and development of next generation COVID-19 vaccines. This review highlights the role of immunoinformatics in identifying immunogenic peptides as potential vaccine targets, involving databases, and prediction and characterization of epitopes which can be harnessed for designing future coronavirus vaccines.

Unveiling mutation effects on the structural dynamics of the main protease from SARS-CoV-2 with hybrid simulation methods

The main protease of SARS-CoV-2 (called M^pro or 3CL^pro) is essential for processing polyproteins encoded by viral RNA. Several M^pro mutations were found in SARS-CoV-2 variants, which are related to higher transmissibility, pathogenicity, and resistance to neutralization antibodies. Macromolecules adopt several favored conformations in solution depending on their structure and shape, determining their dynamics and function. In this study, we used a hybrid simulation method to generate intermediate structures along the six lowest frequency normal modes and sample the conformational space and characterize the structural dynamics and global motions of WT SARS-CoV-2 M^pro and 48 mutations, including mutations found in P.1, B.1.1.7, B.1.351, B.1.525 and B.1.429+B.1.427 variants. We tried to contribute to the elucidation of the effects of mutation in the structural dynamics of SARS-CoV-2 M^pro. A machine learning analysis was performed following the investigation regarding the influence of the K90R, P99L, P108S, and N151D mutations on the dimeric interface assembling of the SARS-CoV-2 M^pro. The parameters allowed the selection of potential structurally stable dimers, which demonstrated that some single surface aa substitutions not located at the dimeric interface (K90R, P99L, P108S, and N151D) are able to induce significant quaternary changes. Furthermore, our results demonstrated, by a Quantum Mechanics method, the influence of SARS-CoV-2 M^pro mutations on the catalytic mechanism, confirming that only one of the chains of the WT and mutant SARS-CoV-2 M^pros are prone to cleave substrates. Finally, it was also possible to identify the aa residue F140 as an important factor related to the increasing enzymatic reactivity of a significant number of SARS-CoV-2 M^pro conformations generated by the normal modes-based simulations.

Towards Quantum-Chemical Level Calculations of SARS-CoV-2 Spike Protein Variants of Concern by First Principles Density Functional Theory

The spike protein (S-protein) is a crucial part of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), with its many domains responsible for binding, fusion, and host cell entry. In this review we use the density functional theory (DFT) calculations to analyze the atomic-scale interactions and investigate the consequences of mutations in S-protein domains. We specifically describe the key amino acids and functions of each domain, which are essential for structural stability as well as recognition and fusion processes with the host cell; in addition, we speculate on how mutations affect these properties. Such unprecedented large-scale ab initio calculations, with up to 5000 atoms in the system, are based on the novel concept of amino acid-amino acid-bond pair unit (AABPU) that allows for an alternative description of proteins, providing valuable information on partial charge, interatomic bonding and hydrogen bond (HB) formation. In general, our results show that the S-protein mutations for different variants foster an increased positive partial charge, alter the interatomic interactions, and disrupt the HB networks. We conclude by outlining a roadmap for future computational research of biomolecular virus-related systems.

State of the art in epitope mapping and opportunities in COVID-19

The understanding of any disease calls for studying specific biological structures called epitopes. One important tool recently drawing attention and proving efficiency in both diagnosis and vaccine development is epitope mapping. Several techniques have been developed with the urge to provide precise epitope mapping for use in designing sensitive diagnostic tools and developing rpitope-based vaccines (EBVs) as well as therapeutics. In this review, we will discuss the state of the art in epitope mapping with a special emphasis on accomplishments and opportunities in combating COVID-19. These comprise SARS-CoV-2 variant analysis versus the currently available immune-based diagnostic tools and vaccines, immunological profile-based patient stratification, and finally, exploring novel epitope targets for potential prophylactic, therapeutic or diagnostic agents for COVID-19.

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

Exploration of Computational Aids for Effective Drug Designing and Management of Viral Diseases: A Comprehensive Review

BACKGROUND

Microbial diseases, specifically originating from viruses are the major cause of human mortality all over the world. The current COVID-19 pandemic is a case in point, where the dynamics of the viral-human interactions are still not completely understood, making its treatment a case of trial and error. Scientists are struggling to devise a strategy to contain the pandemic for over a year and this brings to light the lack of understanding of how the virus grows and multiplies in the human body.

METHODS

This paper presents the perspective of the authors on the applicability of computational tools for deep learning and understanding of host-microbe interaction, disease progression and management, drug resistance and immune modulation through in silico methodologies which can aid in effective and selective drug development. The paper has summarized advances in the last five years. The studies published and indexed in leading databases have been included in the review.

RESULTS

Computational systems biology works on an interface of biology and mathematics and intends to unravel the complex mechanisms between the biological systems and the inter and intra species dynamics using computational tools, and high-throughput technologies developed on algorithms, networks and complex connections to simulate cellular biological processes.

CONCLUSION

Computational strategies and modelling integrate and prioritize microbial-host interactions and may predict the conditions in which the fine-tuning attenuates. These microbial-host interactions and working mechanisms are important from the aspect of effective drug designing and fine- tuning the therapeutic interventions.

Finding potential inhibitors against RNA-dependent RNA polymerase (RdRp) of bovine ephemeral fever virus (BEFV): an in-silico study

The bovine ephemeral fever virus (BEFV) is an enzootic agent that affects millions of bovines and causes major economic losses. Though the virus is seasonally reported with a very high morbidity rate (80-100%) from African, Australian, and Asiatic continents, it remains a neglected pathogen in many of its endemic areas, with no proper therapeutic drugs or vaccines presently available for treatment. The RNA-dependent RNA polymerase (RdRp) catalyzes the viral RNA synthesis and is an appropriate candidate for antiviral drug developments. We utilized integrated computational tools to build the 3D model of BEFV-RdRp and then predicted its probable active binding sites. The virtual screening and optimization against these active sites, using several small-molecule inhibitors from a different category of Life Chemical database and FDA-approved drugs from the ZINC database, was performed. We found nine molecules that have docking scores varying between -6.84 to -10.43 kcal/mol. Furthermore, these complexes were analyzed for their conformational dynamics and thermodynamic stability using molecular dynamics simulations in conjunction with the molecular mechanics generalized Born surface area (MM-GBSA) scheme. The binding free energy calculations depict that the electrostatic interactions play a dominant role in the RdRp-inhibitor binding. The hot spot residues, such as Arg565, Asp631, Glu633, Asp740, and Glu707, were found to control the RdRp-inhibitor interaction. The ADMET analysis strongly suggests favorable pharmacokinetics of these compounds that may prove useful for treating the BEFV ailment. Overall, we anticipate that these findings would help explore and develop a wide range of anti-BEFV therapy.Communicated by Ramaswamy H. Sarma.

The Development of Pharmacophore Models for the Search of New Natural Inhibitors of SARS-CoV-2 Spike RBD-ACE2 Binding Interface

To date, some succeeding variants of SARS-CoV-2 have become more contagious. This virus is known to enter human cells by binding the receptor-binding domain (RBD) of spike protein with the angiotensin-converting enzyme 2 (ACE2), the latter being a membrane protein that regulates the renin-angiotensin system. Since the host cell receptor plays a critical role in viral entry, inhibition of the RBD-ACE2 complex is a promising strategy for preventing COVID-19 infection. In the present communication, we propose and utilize an approach based on the generation of a complex of pharmacophore models and subsequent Induced Fit Docking (IFD) to identify potential inhibitors of the main binding sites of the Omicron SARS-CoV-2 RBD(S1)-ACE2 complex (PDB ID: 7T9L) among a number of natural products of various types and origins. Several natural compounds have been found to provide a high affinity for the receptor of interest. It is expected that the present results will stimulate further research aimed at the development of specialized drugs against this virus.

Small molecules in the treatment of COVID-19

The outbreak of COVID-19 has become a global crisis, and brought severe disruptions to societies and economies. Until now, effective therapeutics against COVID-19 are in high demand. Along with our improved understanding of the structure, function, and pathogenic process of SARS-CoV-2, many small molecules with potential anti-COVID-19 effects have been developed. So far, several antiviral strategies were explored. Besides directly inhibition of viral proteins such as RdRp and M^pro, interference of host enzymes including ACE2 and proteases, and blocking relevant immunoregulatory pathways represented by JAK/STAT, BTK, NF-κB, and NLRP3 pathways, are regarded feasible in drug development. The development of small molecules to treat COVID-19 has been achieved by several strategies, including computer-aided lead compound design and screening, natural product discovery, drug repurposing, and combination therapy. Several small molecules representative by remdesivir and paxlovid have been proved or authorized emergency use in many countries. And many candidates have entered clinical-trial stage. Nevertheless, due to the epidemiological features and variability issues of SARS-CoV-2, it is necessary to continue exploring novel strategies against COVID-19. This review discusses the current findings in the development of small molecules for COVID-19 treatment. Moreover, their detailed mechanism of action, chemical structures, and preclinical and clinical efficacies are discussed.

Repositioning of anti-dengue compounds against SARS-CoV-2 as viral polyprotein processing inhibitor

A therapy for COVID-19 (Coronavirus Disease 19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) remains elusive due to the lack of an effective antiviral therapeutic molecule. The SARS-CoV-2 main protease (Mpro), which plays a vital role in the viral life cycle, is one of the most studied and validated drug targets. In Several prior studies, numerous possible chemical entities were proposed as potential Mpro inhibitors; however, most failed at various stages of drug discovery. Repositioning of existing antiviral compounds accelerates the discovery and development of potent therapeutic molecules. Hence, this study examines the applicability of anti-dengue compounds against the substrate binding site of Mpro for disrupting its polyprotein processing mechanism. An in-silico structure-based virtual screening approach is applied to screen 330 experimentally validated anti-dengue compounds to determine their affinity to the substrate binding site of Mpro. This study identified the top five compounds (CHEMBL1940602, CHEMBL2036486, CHEMBL3628485, CHEMBL200972, CHEMBL2036488) that showed a high affinity to Mpro with a docking score > -10.0 kcal/mol. The best-docked pose of these compounds with Mpro was subjected to 100 ns molecular dynamic (MD) simulation followed by MM/GBSA binding energy. This showed the maximum stability and comparable ΔG binding energy against the reference compound (X77 inhibitor). Overall, we repurposed the reported anti-dengue compounds against SARS-CoV-2-Mpro to impede its polyprotein processing for inhibiting SARS-CoV-2 infection.

Mapping Potential Vaccine Candidates Predicted by VaxiJen for Different Viral Pathogens between 2017-2021-A Scoping Review

Reverse vaccinology (RV) is a promising alternative to traditional vaccinology. RV focuses on in silico methods to identify antigens or potential vaccine candidates (PVCs) from a pathogen's proteome. Researchers use VaxiJen, the most well-known RV tool, to predict PVCs for various pathogens. The purpose of this scoping review is to provide an overview of PVCs predicted by VaxiJen for different viruses between 2017 and 2021 using Arksey and O'Malley's framework and the Preferred Reporting Items for Systematic Reviews extension for Scoping Reviews (PRISMA-ScR) guidelines. We used the term 'vaxijen' to search PubMed, Scopus, Web of Science, EBSCOhost, and ProQuest One Academic. The protocol was registered at the Open Science Framework (OSF). We identified articles on this topic, charted them, and discussed the key findings. The database searches yielded 1033 articles, of which 275 were eligible. Most studies focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), published between 2020 and 2021. Only a few articles (8/275; 2.9%) conducted experimental validations to confirm the predictions as vaccine candidates, with 2.2% (6/275) articles mentioning recombinant protein expression. Researchers commonly targeted parts of the SARS-CoV-2 spike (S) protein, with the frequently predicted epitopes as PVCs being major histocompatibility complex (MHC) class I T cell epitopes WTAGAAAYY, RQIAPGQTG, IAIVMVTIM, and B cell epitope IAPGQTGKIADY, among others. The findings of this review are promising for the development of novel vaccines. We recommend that vaccinologists use these findings as a guide to performing experimental validation for various viruses, with SARS-CoV-2 as a priority, because better vaccines are needed, especially to stay ahead of the emergence of new variants. If successful, these vaccines could provide broader protection than traditional vaccines.

A common allele of HLA mediates asymptomatic SARS-CoV-2 infection

Despite some inconsistent reporting of symptoms, studies have demonstrated that at least 20% of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will remain asymptomatic. Although most global efforts have focused on understanding factors underlying severe illness in COVID-19 (coronavirus disease of 2019), the examination of asymptomatic infection provides a unique opportunity to consider early disease and immunologic features promoting rapid viral clearance. Owing to its critical role in the immune response, we postulated that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection. We enrolled 29,947 individuals registered in the National Marrow Donor Program for whom high-resolution HLA genotyping data were available in the UCSF Citizen Science smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n=1428) was comprised of unvaccinated, self-identified subjects who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci (HLA-A, -B, -C, -DRB1, -DQB1) with disease course and identified a strong association of HLA-B*15:01 with asymptomatic infection, and reproduced this association in two independent cohorts. Suggesting that this genetic association is due to pre-existing T-cell immunity, we show that T cells from pre-pandemic individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF, and 100% of the reactive cells displayed memory phenotype. Finally, we characterize the protein structure of HLA-B*15:01-peptide complexes, demonstrating that the NQKLIANQF peptide from SARS-CoV-2, and the highly homologous NQKLIANAF from seasonal coronaviruses OC43-CoV and HKU1-CoV, share similar ability to be stabilized and presented by HLA-B*15:01, providing the molecular basis for T-cell cross-reactivity and HLA-B*15:01-mediated pre-existing immunity.

Effect of Delta and Omicron Mutations on the RBD-SD1 Domain of the Spike Protein in SARS-CoV-2 and the Omicron Mutations on RBD-ACE2 Interface Complex

The receptor-binding domain (RBD) is the essential part in the Spike-protein (S-protein) of SARS-CoV-2 virus that directly binds to the human ACE2 receptor, making it a key target for many vaccines and therapies. Therefore, any mutations at this domain could affect the efficacy of these treatments as well as the viral-cell entry mechanism. We introduce ab initio DFT-based computational study that mainly focuses on two parts: (1) Mutations effects of both Delta and Omicron variants in the RBD-SD1 domain. (2) Impact of Omicron RBD mutations on the structure and properties of the RBD-ACE2 interface system. The in-depth analysis is based on the novel concept of amino acid-amino acid bond pair units (AABPU) that reveal the differences between the Delta and/or Omicron mutations and its corresponding wild-type strain in terms of the role played by non-local amino acid interactions, their 3D shapes and sizes, as well as contribution to hydrogen bonding and partial charge distributions. Our results also show that the interaction of Omicron RBD with ACE2 significantly increased its bonding between amino acids at the interface providing information on the implications of penetration of S-protein into ACE2, and thus offering a possible explanation for its high infectivity. Our findings enable us to present, in more conspicuous atomic level detail, the effect of specific mutations that may help in predicting and/or mitigating the next variant of concern.

Computational design of a broad-spectrum multi-epitope vaccine candidate against seven strains of human coronaviruses

Spike (S) proteins are an attractive target as it mediates the binding of the SARS-CoV-2 to the host through ACE-2 receptors. We hypothesize that the screening of the S protein sequences of all the seven known HCoVs would result in the identification of potential multi-epitope vaccine candidates capable of conferring immunity against various HCoVs. In the present study, several machine learning-based in-silico tools were employed to design a broad-spectrum multi-epitope vaccine candidate targeting the S protein of seven known strains of human coronaviruses. Herein, multiple B-cell epitopes and T-cell epitopes (CTL and HTL) were predicted from the S protein sequences of all seven known HCoVs. Post-prediction they were linked together with an adjuvant to construct a potential broad-spectrum vaccine candidate. Secondary and tertiary structures were predicted and validated, and the refined 3D-model was docked with an immune receptor. The vaccine candidate was evaluated for antigenicity, allergenicity, solubility, and its ability to achieve high-level expression in bacterial hosts. Finally, the immune simulation was carried out to evaluate the immune response after three vaccine doses. The designed vaccine is antigenic (with or without the adjuvant), non-allergenic, binds well with TLR-3 receptor and might elicit a diverse and strong immune response.

Computational investigation of the increased virulence and pathogenesis of SARS-CoV-2 lineage B.1.1.7

New variants of SARS-CoV-2 are being reported worldwide. The World Health Organization has reported Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2) and Omicron (B.1.1.529) as the variants of concern. There are speculations that the variants might evade the host immune responses induced by currently available vaccines and develop resistance to drugs under consideration. The first step of viral infection in COVID-19 occurs through the interaction of the spike protein's receptor-binding domain (RBD) with the peptidase domain of the human ACE-2 (hACE-2) receptor. This study aims to get a molecular-level understanding of the mechanism behind the increased infection rate in the alpha variant. We have computationally studied the spike protein interaction in both the wild-type and B.1.1.7 variant with the hACE-2 receptor using molecular dynamics and MM-GBSA based binding free energy calculations. The binding free energy difference shows that the mutant variant of the spike protein has increased binding affinity for the hACE-2 receptor (i.e. ΔG(N501Y,A570D) is in the range -7.2 to -7.6 kcal mol^-1) and the results were validated using Density functional theory. We demonstrate that with the use of state-of-the-art computational approaches, we can, in advance, predict the virulent nature of variants of SARS-CoV-2 and alert the world healthcare system.

Evolutionary progression of collective mutations in Omicron sub-lineages towards efficient RBD-hACE2: Allosteric communications between and within viral and human proteins

The interaction between the Spike (S) protein of SARS-CoV-2 and the human angiotensin converting enzyme 2 (hACE2) is essential for infection, and is a target for neutralizing antibodies. Consequently, selection of mutations in the S protein is expected to be driven by the impact on the interaction with hACE2 and antibody escape. Here, for the first time, we systematically characterized the collective effects of mutations in each of the Omicron sub-lineages (BA.1, BA.2, BA.3 and BA.4) on both the viral S protein receptor binding domain (RBD) and the hACE2 protein using post molecular dynamics studies and dynamic residue network (DRN) analysis. Our analysis suggested that Omicron sub-lineage mutations result in altered physicochemical properties that change conformational flexibility compared to the reference structure, and may contribute to antibody escape. We also observed changes in the hACE2 substrate binding groove in some sub-lineages. Notably, we identified unique allosteric communication paths in the reference protein complex formed by the DRN metrics betweenness centrality and eigencentrality hubs, originating from the RBD core traversing the receptor binding motif of the S protein and the N-terminal domain of the hACE2 to the active site. We showed allosteric changes in residue network paths in both the RBD and hACE2 proteins due to Omicron sub-lineage mutations. Taken together, these data suggest progressive evolution of the Omicron S protein RBD in sub-lineages towards a more efficient interaction with the hACE2 receptor which may account for the increased transmissibility of Omicron variants.

Viral informatics: bioinformatics-based solution for managing viral infections

Several new viral infections have emerged in the human population and establishing as global pandemics. With advancements in translation research, the scientific community has developed potential therapeutics to eradicate or control certain viral infections, such as smallpox and polio, responsible for billions of disabilities and deaths in the past. Unfortunately, some viral infections, such as dengue virus (DENV) and human immunodeficiency virus-1 (HIV-1), are still prevailing due to a lack of specific therapeutics, while new pathogenic viral strains or variants are emerging because of high genetic recombination or cross-species transmission. Consequently, to combat the emerging viral infections, bioinformatics-based potential strategies have been developed for viral characterization and developing new effective therapeutics for their eradication or management. This review attempts to provide a single platform for the available wide range of bioinformatics-based approaches, including bioinformatics methods for the identification and management of emerging or evolved viral strains, genome analysis concerning the pathogenicity and epidemiological analysis, computational methods for designing the viral therapeutics, and consolidated information in the form of databases against the known pathogenic viruses. This enriched review of the generally applicable viral informatics approaches aims to provide an overview of available resources capable of carrying out the desired task and may be utilized to expand additional strategies to improve the quality of translation viral informatics research.

Identification of vaccine candidate against Omicron variant of SARS-CoV-2 using immunoinformatic approaches

Despite the availability of COVID-19 vaccines, additional more potent vaccines are still required against the emerging variations of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the present investigation, we have identified a promising vaccine candidate against the Omicron (B.1.1.529) using immunoinformatics approaches. Various available tools like, the Immune Epitope Database server resource, and NetCTL-1.2, have been used for the identification of the promising T-cell and B-cell epitopes. The molecular docking was performed to check the interaction of TLR-3 receptors and validated 3D model of vaccine candidate. The codon optimization was done followed by cloning using SnapGene. Finally, In-silico immune simulation profile was also checked. The identified T-cell and B-cell epitopes have been selected based on their antigenicity (VaxiJen v2.0) and, allergenicity (AllerTOP v2.0). The identified epitopes with antigenic and non-allergenic properties were fused with the specific peptide linkers. In addition, the 3D model was constructed by the PHYRE2 server and validated using ProSA-web. The validated 3D model was further docked with the Toll-like receptor 3 (TLR3) and showed good interaction with the amino acids which indicate a promising vaccine candidate against the Omicron variant of SARS-CoV-2. Finally, the codon optimization, In-silico cloning and immune simulation profile was found to be satisfactory. Overall, the designed vaccine candidate has a potential against variant of SARS-Cov-2. However, further experimental studies are required to confirm.

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 characteristics, immune evasion, and impact of SARS-CoV-2 variants

The persistent COVID-19 pandemic since 2020 has brought an enormous public health burden to the global society and is accompanied by various evolution of the virus genome. The consistently emerging SARS-CoV-2 variants harboring critical mutations impact the molecular characteristics of viral proteins and display heterogeneous behaviors in immune evasion, transmissibility, and the clinical manifestation during infection, which differ each strain and endow them with distinguished features during populational spread. Several SARS-CoV-2 variants, identified as Variants of Concern (VOC) by the World Health Organization, challenged global efforts on COVID-19 control due to the rapid worldwide spread and enhanced immune evasion from current antibodies and vaccines. Moreover, the recent Omicron variant even exacerbated the global anxiety in the continuous pandemic. Its significant evasion from current medical treatment and disease control even highlights the necessity of combinatory investigation of the mutational pattern and influence of the mutations on viral dynamics against populational immunity, which would greatly facilitate drug and vaccine development and benefit the global public health policymaking. Hence in this review, we summarized the molecular characteristics, immune evasion, and impacts of the SARS-CoV-2 variants and focused on the parallel comparison of different variants in mutational profile, transmissibility and tropism alteration, treatment effectiveness, and clinical manifestations, in order to provide a comprehensive landscape for SARS-CoV-2 variant research.

Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations

Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.

Antiviral Effect of Polyphenolic Substances in Geranium wilfordii Maxim against HSV-2 Infection Using in vitro and in silico Approaches

Background

Herpes simplex virus type 2 (HSV-2) infestation was the most widespread STD (sexually transmitted diseases) among humans and was the leading cause of infectious recurrent genital herpes. Existing therapies against HSV-2 did incompletely restrain the comeback of activated HSV-2 infestation. Geranium wilfordii Maxim had long been used as traditional Chinese medicine for treating the diseases owing to its anti-inflammatory and antiviral effects. Herein, the study was designed to investigate the antiviral activity of G.wilfordii and its potential effect in regulating the host's immune response.

Methods

To identify the stage of infection at which the compounds inhibited HSV-2, we performed virucidal, therapeutic, and prophylactic assays. The antiviral efficacy was evaluated by the analysis of viral components HSV-2 gD and VP16. The antiviral activities of these compounds were also evaluated by phenotypic analysis, such as cell proliferation and apoptosis. Molecular docking studies on candidate compounds were done to indicate binding interactions between the compounds and adopted compound targets.

Results

Quercetin, corilagin, and geraniin inhibited the replication of HSV-2, with geraniin showing greater TI. The obtained IC₅₀ value of quercetin was 204.7 μM and TI (IC₅₀/EC₅₀) was 5.1, whereas the obtained IC₅₀ value of corilagin was 118.0 μg/ml and TI was 4.05. Geraniin exhibited prominent antiviral activity with an IC₅₀ of 212.4 μM and an EC₅₀ of 18.37 μM, resulting in a therapeutic index (TI) of 11.56. Geraniin showed important in vitro virucidal activity through blocking viral attachment. Compared with the virus group, the apoptosis rates in quercetin-, corilagin-, and geraniin-treated groups were significantly decreased (p < 0.001).The expressions at the transcription genes of virus own replication key factors (including HSV-2 gD and VP16) and cytokines (including TBK1) of infected cells treated with quercetin, corilagin, and geraniin were inhibited. The in silico approaches demonstrated a high number of potential strong intermolecular interactions as hydrogen bonds between geraniin, corilagin, and the activity site of HSV-2 gD. Molecular docking studies demonstrated the effects of corilagin by targeting TBK1.

Conclusions

Together, these results highlighted the importance of G.wilfordii treatment in HSV-2 infection and underscored its therapeutic potential. However, additional in vitro and in vivo research was required to validate our findings.

Pyrazolone-type compounds: synthesis and in silico assessment of antiviral potential against key viral proteins of SARS-CoV-2

Coronavirus outbreak is still a major public health concern. The high mutation ability of SARS-CoV-2 periodically delivers more transmissible and dangerous variants. Hence, the necessity for an efficient and inexpensive antiviral agent is urgent. In this work, pyrazolone-type compounds were synthesised, characterised using spectroscopic methods and theoretical tools, and evaluated in silico against proteins of SARS-CoV-2 responsible for host cell entry and reproduction processes, i.e., spike protein (S), M^pro, and PL_pro. Five of twenty compounds are newly synthesised. In addition, the crystal structure of a pyrazolone derivative bearing a vanillin moiety is determined. The obtained in silico results indicate a more favourable binding affinity of pyrazolone analogues towards M^pro, and PL_pro in comparison to drugs lopinavir, remdesivir, chloroquine, and favipiravir, while in the case of S protein only lopinavir exerted higher binding affinity. Also, the investigations were performed on ACE2 and the spike RBD-ACE2 complex. The obtained results for these proteins suggest that selected compounds could express antiviral properties by blocking the binding to the host cell and viral spreading, also. Moreover, several derivatives expressed multitarget antiviral action, blocking both binding and reproduction processes. Additionally, in silico ADME/T calculations predicted favourable features of the synthesised compounds, i.e., drug-likeness, oral bioavailability, as well as good pharmacokinetic parameters related to absorption, metabolism, and toxicity. The obtained results imply the great potential of synthesised pyrazolones as multitarget agents against SARS-CoV-2 and represent a valuable background for further in vitro investigations.

Coronavirus outbreak is still a major public health concern.

Molecular Docking as a Potential Approach in Repurposing Drugs Against COVID-19: a Systematic Review and Novel Pharmacophore Models

Purpose of Review

This article provides a review of the recent literature related to the FDA-approved drugs that had been repurposed as potential drug candidates against COVID-19. Moreover, we performed a quality pharmacophore study for frequently studied targets, namely, the main protease, RNA-dependent RNA polymerase, and spike protein.

Recent Findings

Ever since the COVID-19 pandemic, the whole spectrum of scientific community is still unable to invent an absolute therapeutic agent for COVID-19. Considering such a fact, drug repurposing strategies seem a truly viable approach to develop novel therapeutic interventions.

Summery

Drug repurposing explores previously approved drugs of known safety and pharmacokinetics profile for possible new effects, reducing the cost, time, and predicting prospective side effects and drug interactions. COVID-19 virulent machinery appeared similar to other viruses, making antiviral agents widely repurposed in pursuit for curative candidates. Our main protease pharmacophoric study revealed multiple features and could be a probable starting point for upcoming research.

Progress on SARS-CoV-2 3CLpro Inhibitors: Inspiration from SARS-CoV 3CLpro Peptidomimetics and Small-Molecule Anti-Inflammatory Compounds

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) currently poses a threat to human health. 3C-like proteinase (3CLpro) plays an important role in the viral life cycle. Hence, it is considered an attractive antiviral target protein. Whole-genome sequencing showed that the sequence homology between SARS-CoV-2 3CLpro and SARS-CoV 3CLpro is 96.08%, with high similarity in the substrate-binding region. Thus, assessing peptidomimetic inhibitors of SARS-CoV 3CLpro could accelerate the development of peptidomimetic inhibitors for SARS-CoV-2 3CLpro. Accordingly, we herein discuss progress on SARS-CoV-2 3CLpro peptidomimetic inhibitors. Inflammation plays a major role in the pathophysiological process of COVID-19. Small-molecule compounds targeting 3CLpro with both antiviral and anti-inflammatory effects are also briefly discussed in this paper.

Computational Repurposing of Drugs and Natural Products Against SARS-CoV-2 Main Protease (Mpro) as Potential COVID-19 Therapies

We urgently need to identify drugs to treat patients suffering from COVID-19 infection. Drugs rarely act at single molecular targets. Off-target effects are responsible for undesirable side effects and beneficial synergy between targets for specific illnesses. They have provided blockbuster drugs, e.g., Viagra for erectile dysfunction and Minoxidil for male pattern baldness. Existing drugs, those in clinical trials, and approved natural products constitute a rich resource of therapeutic agents that can be quickly repurposed, as they have already been assessed for safety in man. A key question is how to screen such compounds rapidly and efficiently for activity against new pandemic pathogens such as SARS-CoV-2. Here, we show how a fast and robust computational process can be used to screen large libraries of drugs and natural compounds to identify those that may inhibit the main protease of SARS-CoV-2. We show that the shortlist of 84 candidates with the strongest predicted binding affinities is highly enriched (≥25%) in compounds experimentally validated in vivo or in vitro to have activity in SARS-CoV-2. The top candidates also include drugs and natural products not previously identified as having COVID-19 activity, thereby providing leads for experimental validation. This predictive in silico screening pipeline will be valuable for repurposing existing drugs and discovering new drug candidates against other medically important pathogens relevant to future pandemics.

In silico and in vitro studies on inhibitors for SARS-CoV-2 non-structural proteins with dual herbal combination of Withania somnifera with five rasayana herbs

Being highly transmissible, severe acute respiratory syndrome coronavirus (SARS-CoV-2) has affected millions of people causing devastating global impact and has also not slowed down even after vaccination. The emerges of new strains has made more concerns than the original one. We need a new therapeutic approach against the disease. Our comprehensive in silico study investigates dual herbal combinatorial methanolic extracts of W. somnifera (W) alone and with P. emblica (P) (W:P/1:4) , T. sinensis (T) (W:T/1:4), B. monnieri (B) (W:B/1:1), O. basilicum (O) (W:O/1:4), A. racemosus (A) (W:A/4:1) for potential four phytochemicals as ligands docked with eight COVID-19 Nonstructural proteins (nsp)-main protease (PDB ID:6LU7), papain-like protease (6WUU), helicase ADP (2XZL), N7-methyltransferase (5C8S), endoribonuclease (6WLC), 2'O-methyltransferase (6WVN), RNA dependent RNA polymerase (6M71), and 3Cprotease (6YNQ) along with Remdesivir and Hydroxychloroquine. Ligands from W:P/1:4 showed remarkable docking score (-9.01 kcal/mol) 6M71-(8E,11E,14E)-eicosa-8,11,14-trienoicacidmethylester (EIS) and (-9.99 kcal/mol) 6YNQ-N-[(E)-[4-[(2-methoxydibenzofuran-3-yl)amino]-4-oxobutan-2-ylidene]amino] 4nitrobenzamide (MET). Further, MD simulations were studied for 100 ns and showed the complexes were flexible, stable in the binding pockets of the receptors, and MM-PBSA analysis determined high binding energy of -129.673 ± 15.284 and -134.594 ± 7.085 for 6M71-EIS (Asn⁴⁹⁶, Lys⁵⁷⁷, Arg⁵⁶⁹) and 6YNQ-MET (Cys¹⁴⁵, His⁴¹). Finally, in vitro JURKAT E6.1 cell lines treated with W:P/1:4 and W:O/1:4 methanolic extracts yielded 44.06 and 31.53 ng/mL levels for interferon alpha to counteract an external stimulus by establishing an antiviral state. Thus, nsp is targeted to design effective antiviral drugs for developing an effective therapeutic approach to combat viral RNA synthesis, processing, and suppression of host immunity.Communicated by Ramaswamy H. Sarma.

In Silico Analyses on the Comparative Potential of Therapeutic Human Monoclonal Antibodies Against Newly Emerged SARS-CoV-2 Variants Bearing Mutant Spike Protein

Since the start of the pandemic, SARS-CoV-2 has already infected more than 250 million people globally, with more than five million fatal cases and huge socio-economic losses. In addition to corticosteroids, and antiviral drugs like remdesivir, various immunotherapies including monoclonal antibodies (mAbs) to S protein of SARS-CoV-2 have been investigated to treat COVID-19 patients. These mAbs were initially developed against the wild-type SARS-CoV-2; however, emergence of variant forms of SARS-CoV-2 having mutations in the spike protein in several countries including India raised serious questions on the potential use of these mAbs against SARS-CoV-2 variants. In this study, using an in silico approach, we have examined the binding abilities of eight mAbs against several SARS-CoV-2 variants of Alpha (B.1.1.7) and Delta (B.1.617.2) lineages. The structure of the Fab region of each mAb was designed in silico and subjected to molecular docking against each mutant protein. mAbs were subjected to two levels of selection based on their binding energy, stability, and conformational flexibility. Our data reveal that tixagevimab, regdanvimab, and cilgavimab can efficiently neutralize most of the SARS-CoV-2 Alpha strains while tixagevimab, bamlanivimab, and sotrovimab can form a stable complex with the Delta variants. Based on these data, we have designed, by in silico, a chimeric antibody by conjugating the CDRH3 of regdanivimab with a sotrovimab framework to combat the variants that could potentially escape from the mAb-mediated neutralization. Our finding suggests that though currently available mAbs could be used to treat COVID-19 caused by the variants of SARS-CoV-2, better results could be expected with the chimeric antibodies.

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

Structure-based inhibitor design and repurposing clinical drugs to target SARS-CoV-2 proteases

SARS-CoV-2, the coronavirus responsible for the current COVID-19 pandemic, encodes two proteases, 3CLpro and PLpro, two of the main antiviral research targets. Here we provide an overview of the structures and functions of 3CLpro and PLpro and examine strategies of structure-based drug designing and drug repurposing against these proteases. Rational structure-based drug design enables the generation of potent and target-specific antivirals. Drug repurposing offers an attractive prospect with an accelerated turnaround. Thus far, several protease inhibitors have been identified, and some candidates are undergoing trials that may well prove to be effective antivirals against SARS-CoV-2.

In Silico Evaluation of Binding of 2-Deoxy-D-Glucose with Mpro of nCoV to Combat COVID-19

COVID-19 has threatened the existence of humanity andthis infection occurs due to SARS-CoV-2 or novel coronavirus, was first reported in Wuhan, China. Therefore, there is a need to find a promising drug to cure the people suffering from the infection. The second wave of this viral infection was shaking the world in the first half of 2021. Drugs Controllers of India has allowed the emergency use of 2-deoxy-D-glucose (2DG) in 2021 for patients suffering from this viral infection. The potentiality of 2-deoxy-D-glucose to intervene in D-glucose metabolism exists and energy deprivation is an effective parameter to inhibit cancer cell development. Once 2DG arrives in the cells, it becomes phosphorylated to 2-deoxy-D-glucose-6-phosphate (2-DG6P), a charged molecule expressively captured inside the cells. On the other hand, 2DG lacks the ability to convert into fructose-6-phosphate, resulting in a hampering of the activity of both glucose-6-phosphate isomerase and hexokinase, and finally causing cell death. Hence, the potential and effectiveness of 2DG with the main protease (Mpro) of novel coronavirus (nCoV) should be investigated using the molecular docking and molecular dynamics (MD) simulations. The ability of 2DG to inhibit the Mpro of nCoV is compared with 2-deoxyglucose (2DAG), an acyclic molecule, and 2-deoxy-D-ribose (2DR). The binding energy of the molecules with the Mpro of nCoV is calculated using molecular docking and superimposed analysis data is obtained. The binding energy of 2DG, 2DR and 2DAG was −2.40, −2.22 and −2.88 kcal/mol respectively. Although the molecular docking does not provide reliable information, therefore, the binding affinity can be confirmed by molecular dynamics simulations. Various trajectories such as Rg, RMSD, RMSF, and hydrogen bonds are obtained from the molecular dynamics (MD) simulations. 2DG was found to be a better inhibitor than the 2DAG and 2DR based on the results obtained from the MD simulations at 300 K. Furthermore, temperature-dependent MD simulations of the Mpro of nCoV with promising 2DG was performed at 295, 310 and 315 K, and the effective binding with the Mpro of nCoV occurred at 295 K. With the use of DFT calculations, optimized geometry and localization of electron density of the frontier molecular orbitals were calculated.

SARS-CoV-2 pan-variant inhibitory peptides deter S1-ACE2 interaction and neutralize delta and omicron pseudoviruses

Approved neutralizing antibodies that target the prototype Spike are losing their potency against the emerging variants of concern (VOCs) of SARS-CoV-2, particularly Omicron. Although SARS-CoV-2 is continuously adapting the host environment, emerging variants recognize the same ACE2 receptor for cell entry. Protein and peptide decoys derived from ACE2 or Spike proteins may hold the pan-variant inhibitory potential. Here, we deployed interactive structure- and pharmacophore-based approaches to design short and stable peptides –Coronavirus Spike Neutralizing Peptides (CSNPs)– capable of neutralizing all SARS-CoV-2 VOCs. After in silico structural stability investigation and free energies perturbation of the isolated and target-bound peptides, nine candidate peptides were evaluated for the biophysical interaction through SPR assay. CSNP1, CSNP2, and Pep1 dose-dependently bind the S1 domain of the prototype Spike, whereas CSNP4 binds both S1 and ACE2. After safety and immunocytochemistry evaluation, peptides were probed for their pan-variant inhibitory effects. CSNP1, CSNP2, and CSNP4 inhibited all VOCs dose-dependently, whereas Pep1 had a moderate effect. CSNP2 and CSNP4 could neutralize the wild-type pseudovirus up to 80 % when treated at 0.5 µM. Furthermore, CSNP4 synergize the neutralization effect of monoclonal antibody and CSNP1 in Delta variant pseudovirus assay as they target different regions on the RBD. Thus, we suggest that CSNPs are SARS-CoV-2 pan-variant inhibitory candidates for COVID-19 therapy, which may pave the way for combating the emerging immune-escaping variants. We also propose that CSNP1/2-CSNP4 peptide cocktail or CSNP1/4 mAbs cocktail with no overlapping epitopes could be effective therapeutic strategies against COVID-19.

Immunoinformatics and reverse vaccinomic approaches for effective design

The emergence of mutagenic strains of severe acute respiratory syndrome-Coronavirus-2 (SARS-CoV-2) worst hit the world which already suffered from the Coronavirus disease-2019 (COVID-19) pandemic for 2 years. Due to recent advances in vaccinomics, many vaccine candidates are available but their efficacy against a mutant version of SARS-CoV-2 has remained uncertain. The immune-informatics-based reverse vaccinomic approaches have shown promising investigations recently for the development of cost-effective vaccinomics candidates in a very short period of time. The strategic vaccine development of selected epitopes using artificial intelligence for both B- and T-cells is a very crucial step in this process. This approach provides a highly effective and immunogenic vaccine that offers immunological safety against autoimmunity and other adverse effects over ethnicities, pregnant women, and vulnerable age groups. Several researchers have developed effective vaccine candidates using computational vaccinology and the immune-informatics approach. In this process, a unique peptide sequence of viral proteins such as Nucleocapsid, spike, envelope protein was identified by various in silico tools which are acting as immunological epitopes against TLRs, T-cells, and B-cells. While the conventional immunological vaccine studies take years for vaccine candidature, the immunoinformatics approach is a time-efficient way for the next generation research to study host-pathogen interactions and vaccine development. It is also cost-effective and leads to a better understanding of disease pathogenesis, diagnosis, and immunological response. Owing to the advantage of immunoinformatics-based vaccine approaches the present chapter aimed to discuss vaccine development using immunoinformatics approaches. Besides, the current challenges and future aspects have also been discussed herewith.

Molecular Characterization and Designing of a Novel Multiepitope Vaccine Construct Against Pseudomonas aeruginosa

ABSTRACT

Pseudomonas aeruginosa, an ESKAPE pathogen causes many fatal clinical diseases in humans across the globe. Despite an increase in clinical instances of Pseudomonas infection, there is currently no effective vaccine or treatment available. The major membrane protein candidate of the P. aeruginosa bacterial cell is known to be a critical component for cellular bacterial susceptibility to antimicrobial peptides and survival inside the host organisms. Therefore, the current computational study aims to examine P. aeruginosa's major membrane protein, OprF, and OprI, in order to design linear B-cell, cytotoxic T-cell, and helper T-cell peptide-based vaccine constructs. Utilizing various immune-informatics tools and databases, a total of two B-cells and twelve T-cells peptides were predicted. The final vaccine design was simulated to generate a high-quality three-dimensional structure, which included epitopes, adjuvant, and linkers. The vaccine was shown to be nonallergenic, antigenic, soluble, and had the best biophysical properties. The vaccine and Toll-like receptor 4 have a strong and stable interaction, according to protein-protein docking and molecular dynamics simulations. Additionally, in silico cloning was employed to see how the developed vaccine expressed in the pET28a (+) vector. Ultimately, an immune simulation was performed to see the vaccine efficacy. In conclusion, the newly developed vaccine appears to be a promising option for a vaccine against P. aeruginosa infection.

GRAPHICAL ABSTRACT

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10989-021-10356-z.