Computational resources in the management of antibiotic resistance: Speeding up drug discovery

Lubna Maryam; Salman Sadullah Usmani; Gajendra P S Raghava

doi:10.1016/j.drudis.2021.04.016

Computational resources in the management of antibiotic resistance: Speeding up drug discovery

Drug Discov Today. 2021 Sep;26(9):2138-2151. doi: 10.1016/j.drudis.2021.04.016. Epub 2021 Apr 20.

Authors

Lubna Maryam¹, Salman Sadullah Usmani¹, Gajendra P S Raghava²

Affiliations

¹ Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India.
² Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi 110020, India. Electronic address: raghava@iiitd.ac.in.

PMID: 33892146
DOI: 10.1016/j.drudis.2021.04.016

Abstract

This article reviews more than 50 computational resources developed in past two decades for forecasting of antibiotic resistance (AR)-associated mutations, genes and genomes. More than 30 databases have been developed for AR-associated information, but only a fraction of them are updated regularly. A large number of methods have been developed to find AR genes, mutations and genomes, with most of them based on similarity-search tools such as BLAST and HMMER. In addition, methods have been developed to predict the inhibition potential of antibiotics against a bacterial strain from the whole-genome data of bacteria. This review also discuss computational resources that can be used to manage the treatment of AR-associated diseases.

Keywords: Antibiotic resistance; Computational biology; Databases; In silico tools.

Publication types

Research Support, Non-U.S. Gov't
Review

MeSH terms

Animals
Anti-Bacterial Agents / therapeutic use
Computational Biology
Databases, Factual
Drug Discovery*
Drug Resistance, Bacterial*
Humans

Substances

Anti-Bacterial Agents