Improved Prediction Model of Protein Lysine Crotonylation Sites Using Bidirectional Recurrent Neural Networks

Sian Soo Tng, Nguyen Quoc Khanh Le, Hui Yuan Yeh, Matthew Chin Heng Chua

Research output: Contribution to journalArticlepeer-review

39 Citations (Scopus)


Histone lysine crotonylation (Kcr) is a post-translational modification of histone proteins that is involved in the regulation of gene transcription, acute and chronic kidney injury, spermatogenesis, depression, cancer, and so forth. The identification of Kcr sites in proteins is important for characterizing and regulating primary biological mechanisms. The use of computational approaches such as machine learning and deep learning algorithms have emerged in recent years as the traditional wet-lab experiments are time-consuming and costly. We propose as part of this study a deep learning model based on a recurrent neural network (RNN) termed as Sohoko-Kcr for the prediction of Kcr sites. Through the embedded encoding of the peptide sequences, we investigate the efficiency of RNN-based models such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), and bidirectional gated recurrent unit (BiGRU) networks using cross-validation and independent tests. We also established the comparison between Sohoko-Kcr and other published tools to verify the efficiency of our model based on 3-fold, 5-fold, and 10-fold cross-validations using independent set tests. The results then show that the BiGRU model has consistently displayed outstanding performance and computational efficiency. Based on the proposed model, a webserver called Sohoko-Kcr was deployed for free use and is accessible at

Original languageEnglish
Pages (from-to)265-273
Number of pages9
JournalJournal of Proteome Research
Issue number1
Publication statusPublished - Jan 7 2022


  • bidirectional long short-term memory
  • bioinformatics
  • computational biology
  • deep learning
  • gated recurrent unit
  • lysine crotonylation pathway
  • post-translational modifications
  • protein sequence
  • recurrent neural network

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)


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