Computational vaccinology involves using tools that facilitate in silico prediction of immune responses to pathogens by:
- Epitope (art of an antigen molecule to which an antibody attaches itself) mapping
- Antigen selection
- Immunogen design

Carnegie Mellon University

Combines computational and statistical techniques, mathematics, and engineering to analyze and interpret biological data of patients affected by COVID-19

Bioinformatics for COVID-19

Groot, A. S., Moise, L., Terry, F., Gutierrez, A. H., Hindocha, P., Richard, G., . . . Martin, W. D. (2020). Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools. Frontiers in Immunology, 11. doi:10.3389/fimmu.2020.00442

Better Epitope Discovery, Precision Immune Engineering, and Accelerated Vaccine Design Using Immunoinformatics Tools. 

- Oxford University Press, Bioinformatics 
- Journal of Translational Medicine
- Frontiers in Immunology

Bioinformatics for COVID-19 References by Journal

Machine Learning models can be used to better understand the proteins and complexes that are incvolved with the novel Coronavirus. This includes producing predictions of pandemic regarding hospitalizations, patient outcomes, and simulations.

Machine Learning Analysis 

Understanding of COVID-19 target-ligand interactions represents a key challenge, particularly in observing the interactions in vitro. To combat this, the 
COVID-19 Docking Server was developed. It is a web server that predicts binding modes between COVID-19 targets and ligands (small molecules, peptides, antibodies, etc). 

In a very short time, many proteins essential to SARS-CoV-2 have been discovered by researchers in China, including the main protease and spike protein in binding with angiotensin-converting enzyme 2 (ACE2) (this is the primary mode of entry that SARS-CoV-2 uses when initiating cell entry).

As of May 2020, 181 structures were released in the Protein Data Bank, which were  related to 11 different kinds of SARS-CoV-2 proteins.

Structures of the other proteins remain unknown, so the modeled structures of SARS-CoV-2 in the dock server are based on the homologous structures of SARS-CoV, due to high amino acid identity between the two coronaviruses (77.2%). 


INTRODUCTION: COVID-19 Docking Server 

- AI-based programs are trained with large amounts of existing data for their individual purposes. The two types used in this study are:
- (1) Fluency, the AI-based binding affinity prediction platform, which was used on existing FDA approved drugs to test their binding to ACE2 or TMPRSS2 which are 2 proteins used by the virus to enter cells. 
- (2) The Disease Cancelling Technology platform, which was used to assess different drugs' ability to induce gene expression signals that counteract disease-associated signals (identified by examining existing data on gene expression in mouse models). 

Methods of Using Bioinformatics to Identify Potential Therapeutics for COVID-19

- The main benefits of using these unbiased AI programs are (1) speed and (2) widespread availability. 
- Because time is of an essence  in a pandemic, these programs can aid in identifying existing drugs for repurposing and select candidates for clinical trials. 

Benefits of Using Bioinformatics 

Virus-CKB or the viral-associated disease-specific chemogenomics knowledgebase is a platform created to quickly predict existing FDA-approved drugs which can be repurposed and progress to clinical trials to be used to treat COVID-19. New and effective anti-COVID-19 drugs are in urgent need, but discovering new drugs can take up to 8 years, can cost up to $2 billion and have low approval ratings, hence why re-purposing drugs could be useful. Essentially, the database was created to make viral infection research easier.


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