ProtTrans-Glutar: Incorporating Features From Pre-trained Transformer-Based Models for Predicting Glutarylation Sites
Fatma Indriani1,2, Kunti Robiatul Mahmudah3, Bedy Purnama4
1Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa, Japan.
Frontiers in Genetics
|June 17, 2022
Summary
Identifying lysine glutarylation sites is crucial. Our new computational model, ProtTrans-Glutar, combines sequence features with advanced AI to rapidly predict glutarylation sites, improving upon existing methods.
Area of Science:
- Biochemistry
- Proteomics
- Bioinformatics
Background:
- Lysine glutarylation is a vital post-translational modification (PTM) regulating biological processes.
- Current proteomic methods for identifying glutarylation are costly and slow.
Purpose of the Study:
- To develop an efficient computational model for predicting lysine glutarylation sites.
- To enhance the speed and accuracy of glutarylation site identification in protein sequences.
Main Methods:
- Proposed ProtTrans-Glutar model integrating traditional sequence features (composition/transition/distribution encoding, enhanced amino acid composition) with transformer-based protein model features (ProtT5-XL-UniRef50).
- Employed random under-sampling and XGBoost classification algorithm for model training and validation.
Main Results:
- The ProtTrans-Glutar model achieved a recall of 0.7864, specificity of 0.6286, and AUC of 0.7075 on an independent test set.
- Demonstrated superior recall and AUC scores compared to previous models on the same dataset.
Conclusions:
- ProtTrans-Glutar offers a rapid and effective approach for identifying lysine glutarylation sites.
- The model's high recall suggests potential for discovering novel glutarylation sites, advancing research in this PTM.
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