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.

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.

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.