COVID-19: Computational Approaches as Emerging Methods

Pharma Tech Outlook: Pharma Tech Magazine

COVID-19: Computational Approaches as Emerging Methods

More than 12 months into the COVID-19 pandemic caused by SARS-Cov-2, highly effective vaccines from Pfizer and Moderna were approved and roughly half a million people in the United States have been vaccinated as of Jan.12, 2021(1).

It is unrealistic to make exact predictions about timelines of pandemics and the post-pandemic outlook, but healthcare experts seem to agree that SARS-Cov-2 is likely to stay with us (2).

mRNA vaccines have broken record times in clinical trials. Historically, the vaccine process had taken a number of years with clinical trials. For example, SARS-CoV vaccine reached clinical trials 2 years after outbreak. Because the SARS-CoV outbreak was ultimately contained, a SARS vaccine was never completed (3). SARS-Cov-2 was different because researchers leveraged previous genetic sequence of this new virus and researchers could whittle down lists of potential vaccine targets by computational and informatics approaches and newer vaccine technology. Then, mRNA vaccines are used and validated.

The COVID-19 experience will change the future of vaccines and has already had immediate impacts on our society. Infectious disease has attracted high research attention in biopharma and government R&D.

We would like to share our opinion about new technology and platform in demand for fast and effective research as the world has learned the lessons of the COVID-19 pandemic.

Monoclonal antibodies (mAbs) are produced by B cells targeting antigens. The hybridoma method introduced by Kohler and Milstein in 1975 by fusing spleen cells to immortalized Myeloma cells became a cornerstone of immunology and biomedical research. The hybridoma technique made it possible to produce monoclonal antibodies in large scale, enhancing the potential of therapeutics.

It is apparent that therapeutic antibodies have become one of the predominant treatments for various diseases such as autoimmune, infectious diseases, cancer, and inflammation over the past 25 years. Currently, there are around 600 therapeutic antibodies in clinical trials as of the early 2020.
Antibodies are discovered by well-established in vivo immunization procedures or in vitro antibody library screening approaches. Establishing a viable antibody candidate can take time and could cost up to tens of millions of dollars. A high failure rate of the traditional path also has prompted the need for computational and informatics approaches. Computational protocols should be extremely useful especially given the explosion of “omic data” available.

The advent of next-generation sequencing technologies increases the tremendous volume of sequence data including B-cell and T-cell receptor repertoires. In bulk sequencing by popular Illumina MiSeq and HiSeq platforms, higher coverage can be achieved but the main limitation of bulk sequencing of repertoires is the loss of pairing between receptor chains (Heavy/Light chains of BCR, alpha/beta chains of TCR)(4). Single-cell RNA-based repertoire sequencing uses barcoding strategies to keep the pairing information of chains (5). Computational downstream analysis such as antibody/protein modelling (6,7), TCR epitope prediction (4,8), antibody-antigen docking (4) can provide a significant impact in streamlining antibody discovery process for years to come.


2. Veldhoen, M., Simas, J.P. Endemic SARS-CoV-2 will maintain post-pandemic immunity. Nat Rev Immunol (2021).

3. Kim, Y.C., Dema, B. & Reyes-Sandoval, A. COVID-19 vaccines: breaking record times to first-in-human trials. npj Vaccines 5, 34 (2020).

4. Teraguchi, S and Saputri, D. et al. Methods for sequence and structural analysis of B and T cell receptor repertoires. Comput. Struct. Biotechnol. J. 2020, 18, 2000–2011.

5. Singh, M., Al-Eryani, G., Carswell, S. et al. High-throughput targeted long-read single cell sequencing reveals the clonal and transcriptional landscape of lymphocytes. Nat Commun 10, 3120 (2019).

6. Norman, R.A and Ambrosetti, F et al. Computational approaches to therapeutic antibody design: established methods and emerging trends, Briefings in Bioinformatics, 21, 5 (2020).

7. Leem J et al. ABodyBuilder: Automated antibody structure prediction with data-driven accuracy estimation. MAbs 2016;8(7):1259–68.

8. Springer I, Besser H, Tickotsky-Moskovitz N, Dvorkin S and Louzoun Y (2020) Prediction of Specific TCR-Peptide Binding From Large Dictionaries of TCR-Peptide Pairs. Front. Immunol. 11:1803. doi: 10.3389/fimmu.2020.01803
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