Faculty, Staff and Student Publications
Language
English
Publication Date
7-7-2025
Journal
Nucleic Acids Research
DOI
10.1093/nar/gkaf366
PMID
40308214
PMCID
PMC12230729
PubMedCentral® Posted Date
5-1-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Protein kinases (PKs) regulate various cellular functions, and are targeted by small-molecule kinase inhibitors (KIs) in cancers and other diseases. However, drug resistance (DR) of KIs occurs through critical mutations in four types of representative hotspots, including gatekeeper, G-loop, αC-helix, and A-loop. KI DR has become a common clinical complication affecting multiple cancers, targeted kinases, and drugs. To tackle this challenge, we report an upgraded web server, namely Dr. Kinase, for predicting the loci of four DR hotspots and assessing effects of mutations on DR hotspots for PKs in our previous studies, by utilizing multimodal features and deep hybrid learning. The performance of Dr. Kinase has been rigorously evaluated using independent testing, demonstrating excellent accuracy with area under the curve values exceeding 0.89 in different types of DR hotspot predictions. We further conducted in silico analyses to evaluate and validate the epidermal growth factor receptor mutations on protein conformation and KIs' binding efficacy. Dr. Kinase is freely available at http://modinfor.com/drkinase, with comprehensive annotations and visualizations. We anticipate that Dr. Kinase will be a highly useful service for the basic, translational, and clinical community to unveil the molecular mechanisms of DR and the development of next-generation KIs for emerging cancer precision medicine.
Keywords
Protein Kinase Inhibitors, Humans, Drug Resistance, Neoplasm, ErbB Receptors, Mutation, Software, Protein Kinases, Neoplasms, Deep Learning, Internet
Published Open-Access
yes
Recommended Citation
Lin, Shaofeng; Tu, Chao; Hu, Ruifeng; et al., "Dr Kinase: Predicting the Drug-Resistance Hotspots of Protein Kinases" (2025). Faculty, Staff and Student Publications. 678.
https://digitalcommons.library.tmc.edu/uthshis_docs/678