Genes4Vaccines: A computational model that utilizes comparative genetics to identify DNA & protein sequences for novel vaccines

Keywords

Computational Vaccinology; Metagenomics; Bioinformatics

Abstract

With the lack of efficient treatment for many devastating infections, the emergence of multidrug resistant bacteria, and the great promise for innovative vaccine design and research with genomics, vaccine research and development is experiencing a renaissance of interest from the global scientific community. An emerging field known as ‘reverse vaccinology’ uses a combination of whole-genome sequencing, in silico processing, and recombinant DNA technology to develop new vaccines. Only 1 for every 5,000 to 10,000 compounds screened is approved by the Food and Drug Administration. As a result it takes a long period of time to create a vaccine that will be completely approved, 10 to12 years. During the early stages of development the risk of failure is at its highest. This is because much of early stage development is based off trial and error of different components of a vaccine. To eliminate this dated guess-and-check methodology, an algorithm, Genes4Vaccines,will aid in predicting the specific DNA and protein sequences for antigens and/or virmugens of bacteria and viruses. Genes4Vaccines can be utilized in developing novel vaccines, as well as predicting their efficacy. By collecting mass data on biological classification properties of current vaccines, such as molecule role and protein length, from publicly available databases and developing a statistical model, it is anticipated that Genes4Vaccines will be able to decrease the time and monetary investment in the early stages of vaccine development.

Date Created

May 2019

This document is currently not available here.

Share

COinS