Discovery of moiety preference by Shapley value in protein kinase family using random forest models

Yu Wei Huang, Yen Chao Hsu, Yi Hsuan Chuang, Yun Ti Chen, Xiang Yu Lin, You Wei Fan, Nikhil Pathak, Jinn Moon Yang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number130
JournalBMC Bioinformatics
Volume23
DOIs
StatePublished - Apr 2022

Keywords

  • Kinase family inhibitor
  • Kinase inhibitors
  • Merged moiety-based interpretable features (MMIFs)
  • Random forest
  • Shapley Additive exPlanations (SHAP) approach

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