Computational resources in the management of antibiotic resistance: Speeding up drug discovery. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Maryam, Lubna; Usmani, Salman Sadullah; Raghava, Gajendra P.S.
• Is Part Of: Drug discovery today. Volume 26:Issue 9(2021)
• Page Start: 2138
• Page End: 2151
• Publication Date: 2021-09
• Subjects: Antibiotic resistance -- Computational biology -- Databases -- In silico tools; Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2021.04.016
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Among several available databases only fraction of them are regularly updated. Most of the methods apply similarity-based approach for predicting AR genes. Recently machine/deep learning has been used for finding AR genes in genome. Concordance between phenotypic susceptibility testing and genome based prediction. Role of computational tools in managing antibiotic resistance has been discussed. 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.