期刊
BMC BIOINFORMATICS
卷 23, 期 SUPPL 4, 页码 -出版社
BMC
DOI: 10.1186/s12859-022-04663-5
关键词
Kinase family inhibitor; Random forest; Shapley Additive exPlanations (SHAP) approach; Merged moiety-based interpretable features (MMIFs); Kinase inhibitors
类别
资金
- Ministry of Education (MOE) in Taiwan Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B)
- Ministry of Science and Technology (MOST) of Taiwan [MOST110-2634-F-002-016-, MOST1092327-B-010-005, MOST 109-2327-B-016-002, MOST110-2634-F-009-015-, MOST110-2321-B-A49-003]
- National Yang Ming Chiao Tung University
- National Health Research Institutes [NHRIEX110-11017BI]
This study successfully constructed accurate prediction models for kinase family inhibitors using various features and machine learning methods. It also discovered common and specific inhibitor features between different kinase families, providing new insights for inhibitor design.
Background Human protein kinases play important roles in cancers, are highly co-regulated by kinase families rather than a single kinase, and complementarily regulate signaling pathways. Even though there are > 100,000 protein kinase inhibitors, only 67 kinase drugs are currently approved by the Food and Drug Administration (FDA). Results In this study, we used merged moiety-based interpretable features (MMIFs), which merged four moiety-based compound features, including Checkmol fingerprint, PubChem fingerprint, rings in drugs, and in-house moieties as the input features for building random forest (RF) models. By using > 200,000 bioactivity test data, we classified inhibitors as kinase family inhibitors or non-inhibitors in the machine learning. The results showed that our RF models achieved good accuracy (> 0.8) for the 10 kinase families. In addition, we found kinase common and specific moieties across families using the Shapley Additive exPlanations (SHAP) approach. We also verified our results using protein kinase complex structures containing important interactions of the hinges, DFGs, or P-loops in the ATP pocket of active sites. Conclusions In summary, we not only constructed highly accurate prediction models for predicting inhibitors of kinase families but also discovered common and specific inhibitor moieties between different kinase families, providing new opportunities for designing protein kinase inhibitors.
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