TY - JOUR
T1 - Bioinformatics toolbox for exploring target mutation-induced drug resistance
AU - Huang, Yuan Qin
AU - Sun, Ping
AU - Chen, Yi
AU - Liu, Huan Xiang
AU - Hao, Ge Fei
AU - Song, Bao An
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press. All rights reserved.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - database
KW - drug resistance mutation
KW - in silico
KW - protein–ligand affinity
KW - web server
UR - http://www.scopus.com/inward/record.url?scp=85150666026&partnerID=8YFLogxK
U2 - 10.1093/bib/bbad033
DO - 10.1093/bib/bbad033
M3 - Review article
C2 - 36738254
AN - SCOPUS:85150666026
SN - 1467-5463
VL - 24
JO - Briefings in Bioinformatics
JF - Briefings in Bioinformatics
IS - 2
M1 - bbad033
ER -