Structure-Activity Relationships toward the Identification of a High-Potency Selective Human Toll-like Receptor-7 Agonist.

Toll-like receptor (TLR)-7 agonists are immunostimulatory vaccine adjuvants. A systematic structure-activity relationship (SAR) study of TLR7-active 1-benzyl-2-butyl-1H-imidazo[4,5-c]quinolin-4-amine led to the identification of a potent hTLR7-specific p-hydroxymethyl IMDQ 23 with an EC50 value of 0.22 µM. The SAR investigation also resulted in the identification of TLR7 selective carboxamide 12 with EC50 values of 0.32 µM for hTLR7 and 18.25 µM for hTLR8. In the vaccination study, TLR7-specific compound 23 alone or combined with alum (aluminum hydroxide wet gel) showed adjuvant activity for a spike protein immunogen in mice, with enhanced anti-spike antibody production. Interestingly, the adjuvant system comprising carboxamide 12 and alum showed prominent adjuvant activity with high levels of IgG1, IgG2b, and IgG2c in immunized mice, confirming a balanced Th1/Th2 response. In the absence of any apparent toxicity, the TLR7 selective agonists in combination with alum may make a suitable vaccine adjuvant.

In Silico Structure-Based Vaccine Design.

Structure-based vaccine design (SBVD) is an important technique in computational vaccine design that uses structural information on a targeted protein to design novel vaccine candidates. This increasing ability to rapidly model structural information on proteins and antibodies has provided the scientific community with many new vaccine targets and novel opportunities for future vaccine discovery. This chapter provides a comprehensive overview of the status of in silico SBVD and discusses the current challenges and limitations. Key strategies in the field of SBVD are exemplified by a case study on design of COVID-19 vaccines targeting SARS-CoV-2 spike protein.

Identifying SARS-CoV-2 Drugs Binding to the Spike Fatty Acid Binding Pocket Using In Silico Docking and Molecular Dynamics.

Drugs against novel targets are needed to treat COVID-19 patients, especially as SARS-CoV-2 is capable of rapid mutation. Structure-based de novo drug design and repurposing of drugs and natural products is a rational approach to discovering potentially effective therapies. These in silico simulations can quickly identify existing drugs with known safety profiles that can be repurposed for COVID-19 treatment. Here, we employ the newly identified spike protein free fatty acid binding pocket structure to identify repurposing candidates as potential SARS-CoV-2 therapies. Using a validated docking and molecular dynamics protocol effective at identifying repurposing candidates inhibiting other SARS-CoV-2 molecular targets, this study provides novel insights into the SARS-CoV-2 spike protein and its potential regulation by endogenous hormones and drugs. Some of the predicted repurposing candidates have already been demonstrated experimentally to inhibit SARS-CoV-2 activity, but most of the candidate drugs have yet to be tested for activity against the virus. We also elucidated a rationale for the effects of steroid and sex hormones and some vitamins on SARS-CoV-2 infection and COVID-19 recovery.

Anti-microbial Peptides against Methicillin-resistant Staphylococcus aureus: Promising Therapeutics.

BACKGROUND: Multidrug-resistant (MDR) methicillin-resistant Staphylococcus aureus (MRSA) has become a prime health concern globally. These bacteria are found in hospital areas where they are regularly dealing with antibiotics. This brings many possibilities for its mutation, so drug resistance occurs. INTRODUCTION: Nowadays, these nosocomial MRSA strains spread into the community and live stocks. Resistance in Staphylococcus aureus is due to mutations in their genetic elements. METHODS: As the bacteria become resistant to antibiotics, new approaches like antimicrobial peptides (AMPs) play a vital role and are more efficacious, economical, time, and energy saviours. RESULTS: Machine learning approaches of Artificial Intelligence are the in silico technique which has their importance in better prediction, analysis, and fetching of important details regarding AMPs. CONCLUSION: Anti-microbial peptides could be the next-generation solution to combat drug resistance among Superbugs. For better prediction and analysis, implementing the in silico technique is beneficial for fast and more accurate results.

Potential COVID-19 Therapies from Computational Repurposing of Drugs and Natural Products against the SARS-CoV-2 Helicase.

Repurposing of existing drugs is a rapid way to find potential new treatments for SARS-CoV-2. Here, we applied a virtual screening approach using Autodock Vina and molecular dynamic simulation in tandem to screen and calculate binding energies of repurposed drugs against the SARS-CoV-2 helicase protein (non-structural protein nsp13). Amongst the top hits from our study were antivirals, antihistamines, and antipsychotics, plus a range of other drugs. Approximately 30% of our top 87 hits had published evidence indicating in vivo or in vitro SARS-CoV-2 activity. Top hits not previously reported to have SARS-CoV-2 activity included the antiviral agents, cabotegravir and RSV-604; the NK1 antagonist, aprepitant; the trypanocidal drug, aminoquinuride; the analgesic, antrafenine; the anticancer intercalator, epirubicin; the antihistamine, fexofenadine; and the anticoagulant, dicoumarol. These hits from our in silico SARS-CoV-2 helicase screen warrant further testing as potential COVID-19 treatments.

Stereoisomeric Pam2CS based TLR2 agonists: synthesis, structural modelling and activity as vaccine adjuvants.

Lipopeptides including diacylated Pam2CSK4 as well as triacylated Pam3CSK4 act as ligands of toll-like receptor (TLR)-2, a promising target for the development of vaccine adjuvants. The highly investigated Pam2CSK4 and Pam3CSK4, despite their aqueous solubility have not performed well as vaccine adjuvants which may be attributable to potential denaturation of protein antigens by these cationic surfactant-like lipopeptides. In the present investigation, we synthesized (R), (S) and racemic Pam2CS(OMe) analogs and their N-acetyl derivatives without the tetralysine component to systematically investigate the effect of stereochemistry at the thio-glycerol lipopeptide core of these lipopeptide based TLR2 agonists. The resulting compounds were compared using TLR2 reporter cell-based assays and the ability of the synthesized lipopeptides to stimulate cytokine production (IL-6, IL-10 and TNF-α) by freshly collected human PBMCs and CD40 and CD86 expressions by mouse spleen cells was also investigated. Notably, few synthesized lipopeptides were found to be potent TLR2/6 agonists, inducing cytokine production and upregulating CD40 and CD86 expressions. The TLR2/6 agonistic lipopeptides were further assessed for vaccine adjuvant effects in mice. The results confirmed that the R-stereochemistry at the thio-glycerol lipopeptide core was preferred for maximal TLR2/6 activity, as reflected in Th1 immune deviation, higher antibody levels and enhanced vaccine protection against a lethal influenza challenge.

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.

Artificial Intelligence in Vaccine and Drug Design.

Knowledge in the fields of biochemistry, structural biology, immunological principles, microbiology, and genomics has all increased dramatically in recent years. There has also been tremendous growth in the fields of data science, informatics, and artificial intelligence needed to handle this immense data flow. At the intersection of wet lab and data science is the field of bioinformatics, which seeks to apply computational tools to better understanding of the biological sciences. Like so many other areas of biology, bioinformatics has transformed immunology research leading to the discipline of immunoinformatics. Within this field, many new databases and computational tools have been created that increasingly drive immunology research, in many cases drawing upon artificial intelligence and machine learning to predict complex immune system behaviors, for example, prediction of B cell and T cell epitopes. In this book chapter, we provide an overview of computational tools and artificial intelligence being used for protein modeling, drug screening, vaccine design, and highlight how these tools are being used to transform approaches to pandemic countermeasure development, by reference to the current COVID-19 pandemic.

Computationally repurposed drugs and natural products against RNA dependent RNA polymerase as potential COVID-19 therapies.

Repurposing of existing drugs and drug candidates is an ideal approach to identify new potential therapies for SARS-CoV-2 that can be tested without delay in human trials of infected patients. Here we applied a virtual screening approach using Autodock Vina and molecular dynamics simulation in tandem to calculate binding energies for repurposed drugs against the SARS-CoV-2 RNA-dependent RNA polymerase (RdRp). We thereby identified 80 promising compounds with potential activity against SARS-Cov2, consisting of a mixture of antiviral drugs, natural products and drugs with diverse modes of action. A substantial proportion of the top 80 compounds identified in this study had been shown by others to have SARS-CoV-2 antiviral effects in vitro or in vivo, thereby validating our approach. Amongst our top hits not previously reported to have SARS-CoV-2 activity, were eribulin, a macrocyclic ketone analogue of the marine compound halichondrin B and an anticancer drug, the AXL receptor tyrosine kinase inhibitor bemcentinib. Our top hits from our RdRp drug screen may not only have utility in treating COVID-19 but may provide a useful starting point for therapeutics against other coronaviruses. Hence, our modelling approach successfully identified multiple drugs with potential activity against SARS-CoV-2 RdRp. Supplementary Information: The online version contains supplementary material available at 10.1186/s43556-021-00050-3.

Rapid development of analytical methods for evaluating pandemic vaccines: a COVID-19 perspective.

Vaccines are key in charting a path out of the COVID-19 pandemic. However, development of new vaccines is highly dependent on availability of analytical methods for their design and evaluation. This paper highlights the challenges presented in having to rapidly develop vaccine analytical tools during an ongoing pandemic, including the need to address progressive virus mutation and adaptation which can render initial assays unreliable or redundant. It also discusses the potential of new computational modeling techniques to model and analyze key viral proteins and their attributes to assist vaccine production and assay design. It then reviews the current range of analytical tools available for COVID-19 vaccine application, ranging from in vitro assays for immunogen characterization to assays to measure vaccine responses in vivo. Finally, it provides a future perspective for COVID-19 vaccine analytical tools and attempts to predict how the field might evolve over the next 5-10 years.

Immunisation of ferrets and mice with recombinant SARS-CoV-2 spike protein formulated with Advax-SM adjuvant protects against COVID-19 infection.

The development of a safe and effective vaccine is a key requirement to overcoming the COVID-19 pandemic. Recombinant proteins represent the most reliable and safe vaccine approach but generally require a suitable adjuvant for robust and durable immunity. We used the SARS-CoV-2 genomic sequence and in silico structural modelling to design a recombinant spike protein vaccine (Covax-19™). A synthetic gene encoding the spike extracellular domain (ECD) was inserted into a baculovirus backbone to express the protein in insect cell cultures. The spike ECD was formulated with Advax-SM adjuvant and first tested for immunogenicity in C57BL/6 and BALB/c mice. Covax-19 vaccine induced high spike protein binding antibody levels that neutralised the original lineage B.1.319 virus from which the vaccine spike protein was derived, as well as the variant B.1.1.7 lineage virus. Covax-19 vaccine also induced a high frequency of spike-specific CD4 + and CD8 + memory T-cells with a dominant Th1 phenotype associated with the ability to kill spike-labelled target cells in vivo. Ferrets immunised with Covax-19 vaccine intramuscularly twice 2 weeks apart made spike receptor binding domain (RBD) IgG and were protected against an intranasal challenge with SARS-CoV-2 virus given two weeks after the last immunisation. Notably, ferrets that received the two higher doses of Covax-19 vaccine had no detectable virus in their lungs or in nasal washes at day 3 post-challenge, suggesting that in addition to lung protection, Covax-19 vaccine may have the potential to reduce virus transmission. This data supports advancement of Covax-19 vaccine into human clinical trials.

In silico comparison of SARS-CoV-2 spike protein-ACE2 binding affinities across species and implications for virus origin.

The devastating impact of the COVID-19 pandemic caused by SARS-coronavirus 2 (SARS-CoV-2) has raised important questions about its origins and the mechanism of its transfer to humans. A further question was whether companion or commercial animals could act as SARS-CoV-2 vectors, with early data suggesting susceptibility is species specific. To better understand SARS-CoV-2 species susceptibility, we undertook an in silico structural homology modelling, protein-protein docking, and molecular dynamics simulation study of SARS-CoV-2 spike protein's ability to bind angiotensin converting enzyme 2 (ACE2) from relevant species. Spike protein exhibited the highest binding to human (h)ACE2 of all the species tested, forming the highest number of hydrogen bonds with hACE2. Interestingly, pangolin ACE2 showed the next highest binding affinity despite having a relatively low sequence homology, whereas the affinity of monkey ACE2 was much lower despite its high sequence similarity to hACE2. These differences highlight the power of a structural versus a sequence-based approach to cross-species analyses. ACE2 species in the upper half of the predicted affinity range (monkey, hamster, dog, ferret, cat) have been shown to be permissive to SARS-CoV-2 infection, supporting a correlation between binding affinity and infection susceptibility. These findings show that the earliest known SARS-CoV-2 isolates were surprisingly well adapted to bind strongly to human ACE2, helping explain its efficient human to human respiratory transmission. This study highlights how in silico structural modelling methods can be used to rapidly generate information on novel viruses to help predict their behaviour and aid in countermeasure development.

Toll-like receptor-7/8 agonist kill Leishmania amazonensis by acting as pro-oxidant and pro-inflammatory agent.

OBJECTIVES: Evaluation of the anti-Leishmanial activity of imidazoquinoline-based TLR7/8 agonists. METHODS: TLR7/8-active imidazoquinolines (2 and 3) were synthesized and assessed for activity against Leishmania amazonensis-intracellular amastigotes using mouse peritoneal macrophages. The production of reactive oxygen species (ROS), nitric oxide (NO) and cytokines was determined in infected and non-infected macrophages. KEY FINDINGS: The imidazoquinolines, 2 and 3, were primarily agonists of TLR7 with compound 3 also showing modest TLR8 activity. Docking studies showed them to occupy the same binding pocket on TLR7 and 8 as the known agonists, imiquimod and resiquimod. Compounds 2 and 3 inhibited the growth of L. amazonensis-intracellular amastigotes with the most potent compound (3, IC50 = 5.93 µM) having an IC50 value close to miltefosine (IC50 = 4.05 µM), a known anti-Leishmanial drug. Compound 3 induced macrophages to produce ROS, NO and inflammatory cytokines that likely explain the anti-Leishmanial effects. CONCLUSIONS: This study shows that activating TLR7 using compounds 2 or 3 induces anti-Leishmanial activity associated with induction of free radicals and inflammatory cytokines able to kill the parasites. While 2 and 3 had a very narrow cytotoxicity window for macrophages, this identifies the possibility to further develop this chemical scaffold to less cytotoxic TLR7/8 agonist for potential use as anti-Leishmanial drug.

TLR2 Agonistic Small Molecules: Detailed Structure-Activity Relationship, Applications, and Future Prospects.

Toll-like receptors (TLRs) are the pattern recognition receptors (PRRs) that recognize pathogen-associated molecular patterns (PAMPs) in microbial species. Among the various TLRs, TLR2 has a special place due to its ability to sense the widest repertoire of PAMPs owing to its heterodimerization with either TLR1 or TLR6, broadening its ligand diversity against pathogens. Various scaffolds are reported to activate TLR2, which include naturally occurring lipoproteins, synthetic lipopeptides, and small heterocyclic molecules. We described a detailed SAR in TLR2 agonistic scaffolds and also covered the design and chemistry for the conjugation of TLR2 agonists to antigens, carbohydrates, polymers, and fluorophores. The approaches involved in delivery of TLR2 agonists such as lipidation of antigen, conjugation to polymers, phosphonic acids, and other linkers to achieve surface adsorption, liposomal formulation, and encapsulating nanoparticles are elaborated. The crystal structure analysis and computational modeling are also included with the structural features that facilitate TLR2 activation.

Predicting Diagnostic Potential of Cathepsin in Epithelial Ovarian Cancer: A Design Validated by Computational, Biophysical and Electrochemical Data.

BACKGROUND: Epithelial ovarian cancer remains one of the leading variants of gynecological cancer with a high mortality rate. Feasibility and technical competence for screening and detection of epithelial ovarian cancer remain a major obstacle and the development of point of care diagnostics (POCD) may offer a simple solution for monitoring its progression. Cathepsins have been implicated as biomarkers for cancer progression and metastasis; being a protease, it has an inherent tendency to interact with Cystatin C, a cysteine protease inhibitor. This interaction was assessed for designing a POCD module. METHODS: A combinatorial approach encompassing computational, biophysical and electron-transfer kinetics has been used to assess this protease-inhibitor interaction. RESULTS: Calculations predicted two cathepsin candidates, Cathepsin K and Cathepsin L based on their binding energies and structural alignment and both predictions were confirmed experimentally. Differential pulse voltammetry was used to verify the potency of Cathepsin K and Cathepsin L interaction with Cystatin C and assess the selectivity and sensitivity of their electrochemical interactions. Electrochemical measurements indicated selectivity for both the ligands, but with increasing concentrations, there was a marked difference in the sensitivity of the detection. CONCLUSIONS: This work validated the utility of dry-lab integration in the wet-lab technique to generate leads for the design of electrochemical diagnostics for epithelial ovarian cancer.

BBIQ, a pure TLR7 agonist, is an effective influenza vaccine adjuvant.

Better adjuvants are needed for vaccines against seasonal influenza. TLR7 agonists are potent activators of innate immune responses and thereby may be promising adjuvants. Among the imidazoquinoline compounds, 1-benzyl-2-butyl-1H-imidazo[4,5-c]quinolin-4-amine (BBIQ) was reported to be a highly active TLR7 agonist but has remained relatively unexplored because of its commercial unavailability. Indeed, in silico molecular modeling studies predicted that BBIQ had a higher TLR7 docking score and binding free energy than imiquimod, the gold standard TLR7 agonist. To circumvent the availability issue, we developed an improved and higher yield method to synthesize BBIQ. Testing BBIQ on human and mouse TLR7 reporter cell lines confirmed it to be TLR7 specific with significantly higher potency than imiquimod. To test its adjuvant potential, BBIQ or imiquimod were admixed with recombinant influenza hemagglutinin protein and administered to mice as two intramuscular immunizations 2 weeks apart. Serum anti-influenza IgG responses assessed by ELISA 2 weeks after the second immunization confirmed that the mice that received vaccine admixed with BBIQ had significantly higher anti-influenza IgG1 and IgG2c responses than mice immunized with antigen alone or admixed with imiquimod. This confirmed BBIQ to be a TLR7-specific adjuvant able to enhance humoral immune responses.

Neuroinformatics analyses reveal GABAt and SSADH as major proteins involved in anticonvulsant activity of valproic acid.

The unequivocal hypotheses about anticonvulsant activity of valproic acid (VPA) have always been a basic hurdle in designing next generation neurotherapeutics, particularly the anti-epileptic drugs. The present study reports about a comprehensive in-silico investigation into qualitative and quantitative binding of VPA and corresponding natural ligands of four major enzymes involved in neurotransmissions, namely-GABA transaminase (GABAt), α-keto glutarate dehydrogenase (α-KGDH), Succinate Semialdehyde dehydrogenase (SSADH) and Glutamate Decarboxylase (GAD), respectively. The molecular docking analyses revealed that VPA inhibits GABAt and α-KGDH through allosteric while SSADH through competitive mode of binding. There is an observed elevation in binding of glutamate over GAD in the presence of VPA. The docking inhibition constant (Ki) of VPA to all the studied enzymatic receptors were observed to be well below the therapeutic concentration of VPA in blood, except for α-KGDH, thus favouring GABAergic over glutamatergic mode of anticonvulsant activity of VPA. The report is probably the first comprehensive in-silico molecular study about VPA action.

Homology modelling and molecular docking studies of human placental cadherin protein for its role in teratogenic effects of anti-epileptic drugs.

Anti-epileptic drugs (AEDs) have high risk of teratogenic side effects, including neural tube defects while mother is on AEDs for her own prevention of convulsions during pregnancy. The present study investigated the interaction of major marketed AEDs and human placental (hp)-cadherin protein, in-silico, to establish the role of hp-cadherin protein in teratogenicity and also to evaluate the importance of Ca(2+) ion in functioning of the protein. A set of 21 major marketed AEDs were selected for the study and 3D-structure of hp-cadherin was constructed using homology modelling and energy minimized using MD simulations. Molecular docking studies were carried out using selected AEDs as ligand with hp-cadherin (free and bound Ca(2+) ion) to study the behavioural changes in hp-cadherin due to presence of Ca(2+) ion. The study reflected that four AEDs (Gabapentin, Pregabalin, Remacimide and Vigabatrine) had very high affinity towards hp-cadherin and thus the later may have prominent role in the teratogenic effects of these AEDs. From docking simulation analysis it was observed that Ca(2+) ion is required to make hp-cadherin energetically favourable and sterically functional.

Molecular Dynamics of Rab7::REP1::GGTase-II Ternary Complex and Identification of Their Putative Drug Binding Sites.

The structure-function correlation of membrane proteins have been a difficult task, particularly in context to transient protein complexes. The molecular simulation of ternary complex of Rab7::REP1::GGTase-II was carried out to understand the basic structural events occurring during the prenylation event of Rab proteins, using the software YASARA. The study suggested that the C-terminus of Rab7 has to be in completely extended conformation during prenylation to reach the active site of RabGGTase-II. Also, attempt was made to find putative drug binding sites on the ternary complex of Rab7::REP1::GGTase-II using Q-SiteFinder programme. The comprehensive consensus probe generated by the program revealed a total of 10 major pockets as putative drug binding sites on Rab7::REP:: GGTase-II ternary complex. These pockets were found on REP protein and GGTase protein subunits. The Rab7 was found to be devoid of any putative drug binding sites in the ternary complex. The phylogenetic analysis of 60 Rab proteins of human was carried out using PHYLIP and study indicated the close phylogenetic relationship between Rab7 and Rab9 proteins of human and hence with further in silico study, the present observations can be extrapolated to Rab9 proteins. The study paves a good platform for further experimental verifications of the findings and other in silico studies like identifying the potential drug targets by searching the putative drug binding sites, generating pharmacophoric pattern, searching or constructing suitable ligand and docking studies.