In silico Identification of Vaccine Candidates against Viral Infections

Authors

Prabin BaralFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Physics

First Advisor's Name

Bernard S. Gerstman

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Prem P. Chapagain

Second Advisor's Committee Title

Co-Committee chair

Third Advisor's Name

Yuan Liu

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Jin He

Fourth Advisor's Committee Title

Committee member

Keywords

virus, epitope, allele, glycoprotein, antibody, docking, dynamics, simulations

Date of Defense

3-23-2022

Abstract

There are many viral diseases without effective treatments or vaccines. These viruses can cause catastrophic epidemics such as the Lassa, Ebola, and Marburg viruses. Similarly, the recent coronavirus pandemic is of great concern as new variants are continuously emerging with decreased susceptibility to antibodies and vaccines that were developed for earlier strains. A critical step in the immune system’s fight against viruses involves an immunological protein molecule binding to a viral protein molecule. I investigate the atomic and molecular details of binding site recognition and binding interactions and dynamics for three important viruses.

Antigens are molecules, such as viral proteins, that are foreign to the human body and can generate an immune response such as the production of antibody proteins to attack the antigen. An epitope is the part of an antigen molecule that is the site for antibody binding. They are categorized as T-cell or B-cell epitopes based upon which type of immunological cell can bind to the epitope. The B cell epitopes are surface segments of the antigen protein that are recognized by the immunological B-cells, whereas T-cell epitopes are peptides derived from antigens and recognized by immunological T-cells when the epitope is bound to a major histocompatibility complex molecule. The identification of epitopes is an essential step for the discovery and development of epitope-based vaccines. Experimental identification of epitopes involves expensive and time-consuming steps and therefore in-silico identification is a powerful tool to facilitate the identification of potential epitope candidates and can decrease the time and expense spent on validation experiments.

I employed several epitope computational prediction methods that are based upon the antigen protein’s amino acid sequence and conformation for the glycoprotein of the Lassa virus as well as for different proteins of the Marburg virus. The predicted epitopes are further filtered based on a consensus approach that resulted in the identification of new epitopes that have not yet been tested experimentally. I performed molecular dynamics computational simulations on the most promising epitopes to determine atomic-level details of the epitope’s interactions and dynamics.

In addition, I performed MD simulations to investigate the dynamics and antibody evasion behavior by the B.1.617.2 (delta) variant of SARS-CoV-2. I found that the receptor-binding β-loop-β motif in the spike protein adopts an altered conformation that causes binding difficulty for some of the neutralizing antibodies that were generated against the original coronavirus strain. This study reflects the possible mechanism for the immune evasion exhibited by the delta variant.

Identifier

FIDC010498

ORCID

0000-0001-5910-3846

Previously Published In

1) Baral, P., Pavadai, E., Gerstman, B.S. et al. In-silico identification of the vaccine candidate epitopes against the Lassa virus hemorrhagic fever. Sci Rep 10, 7667 (2020). https://doi.org/10.1038/s41598-020-63640-1

2) Baral, P., Bhattarai, N., Hossen, M. L., Stebliankin, V., Gerstman, B. S., Narasimhan, G., & Chapagain, P. P. (2021). Mutation-induced changes in the receptor-binding interface of the SARS-CoV-2 Delta variant B. 1.617. 2 and implications for immune evasion. Biochemical and biophysical research communications, 574, 14-19.

Recommended Citation

Baral, Prabin, "In silico Identification of Vaccine Candidates against Viral Infections" (2022). FIU Electronic Theses and Dissertations. 4925.
https://digitalcommons.fiu.edu/etd/4925