Bioinformatics toolbox for exploring target mutation-induced drug resistance

Yuan Qin Huang, Ping Sun, Yi Chen, Huan Xiang Liu, Ge Fei Hao, Bao An Song

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician’s perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Original languageEnglish
Article numberbbad033
JournalBriefings in Bioinformatics
Volume24
Issue number2
DOIs
Publication statusPublished - Mar 2023

Keywords

  • database
  • drug resistance mutation
  • in silico
  • protein–ligand affinity
  • web server

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