Computational biology: Role and scope in taming antimicrobial resistance

Abstract

Background: Infectious diseases pose many challenges due to increasing threat of antimicrobial resistance, which necessitates continuous research to develop novel strategies for development of new molecules with antibacterial activity. In the era of computational biology there are tools and techniques available to address and solve the disease management issues in the field of clinical microbiology. The sequencing techniques, structural biology and machine learning can be implemented collectively to tackle infectious diseases e.g. for the diagnosis, epidemiological typing, pathotyping, antimicrobial resistance detection as well as the discovery of novel drugs and vaccine biomarkers.

Objectives: The present review is a narrative review representing a comprehensive literature-based assessment regarding the use of whole genome sequencing, structural biology and machine learning for the diagnosis, molecular typing and antibacterial drug discovery.

Content: Here, we seek to present an overview of molecular and structural basis of resistance to antibiotics, while focusing on the recent bioinformatics approaches in whole genome sequencing and structural biology. The application of next generation sequencing in management of bacterial infections focusing on investigation of microbial population diversity, genotypic resistance testing and scope for the identification of targets for novel drug and vaccine candidates, has been addressed along with the use of structural biophysics and artificial intelligence.

Keywords: Antibiotic resistance; Computational biology; Genome sequencing; Machine learning; S. Typhi.