Back to Search Start Over

Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations

Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations

Authors :
You-Wei Fan
Wan-Hsin Liu
Yun-Ti Chen
Yen-Chao Hsu
Nikhil Pathak
Yu-Wei Huang
Jinn-Moon Yang
Source :
BMC Bioinformatics, Vol 23, Iss S4, Pp 1-17 (2022)
Publication Year :
2022
Publisher :
BMC, 2022.

Abstract

Abstract Background While it has been known that human protein kinases mediate most signal transductions in cells and their dysfunction can result in inflammatory diseases and cancers, it remains a challenge to find effective kinase inhibitor as drugs for these diseases. One major challenge is the compensatory upregulation of related kinases following some critical kinase inhibition. To circumvent the compensatory effect, it is desirable to have inhibitors that inhibit all the kinases belonging to the same family, instead of targeting only a few kinases. However, finding inhibitors that target a whole kinase family is laborious and time consuming in wet lab. Results In this paper, we present a computational approach taking advantage of interpretable deep learning models to address this challenge. Specifically, we firstly collected 9,037 inhibitor bioassay results (with 3991 active and 5046 inactive pairs) for eight kinase families (including EGFR, Jak, GSK, CLK, PIM, PKD, Akt and PKG) from the ChEMBL25 Database and the Metz Kinase Profiling Data. We generated 238 binary moiety features for each inhibitor, and used the features as input to train eight deep neural networks (DNN) models to predict whether an inhibitor is active for each kinase family. We then employed the SHapley Additive exPlanations (SHAP) to analyze the importance of each moiety feature in each classification model, identifying moieties that are in the common kinase hinge sites across the eight kinase families, as well as moieties that are specific to some kinase families. We finally validated these identified moieties using experimental crystal structures to reveal their functional importance in kinase inhibition. Conclusion With the SHAP methodology, we identified two common moieties for eight kinase families, 9 EGFR-specific moieties, and 6 Akt-specific moieties, that bear functional importance in kinase inhibition. Our result suggests that SHAP has the potential to help finding effective pan-kinase family inhibitors.

Details

Language :
English
ISSN :
14712105
Volume :
23
Issue :
S4
Database :
Directory of Open Access Journals
Journal :
BMC Bioinformatics
Publication Type :
Academic Journal
Accession number :
edsdoj.537cc754f024ceeb96fda93cc9b5961
Document Type :
article
Full Text :
https://doi.org/10.1186/s12859-022-04760-5