## 5 Conclusions  
In this work, we proposed a new model (PTG-PLM) for Ngly and Kgly site prediction. Recently, NLP pre-trained language models have been transferred to process protein sequences. Our proposed, PTG-PLM, is based on CNN and embedding extracting using six protein language models (PLMs) including ProtBert-BFD, ProtBert, ProtAlbert, ProtXInet, ESM-1b, and TAPE. The PLMs embedding extract information of evolutionary relationships and complicated interactions between tokens (amino acids) of peptide sequences without using the standard feature extraction methods such as MSA. The model was trained and evaluated on two Ngly and Kgly datasets that were used in previous studies. For evaluation, the model was evaluated using a five-fold cross-validation method based on every PLMs individually. The PTG-PLM models based on ESM-1b and ProtBert-BFD have resulted in better performance than the other used PLMs.  
In addition, PTG-PLM was compared with SVM, LR, RF, and XBG machine learning methods. The performance results demonstrated that PTG-PLM outperforms the other models in terms of accuracy, AUC, and MCC. Moderately, XBG and SVM models based on ProtBert-BFD had a good performance. Moreover, seven existing tools for Ngly site prediction: PUStack-NGly, SPRINT-GLY, GlycoMine_PU, GlycoMine, GlycoEP, GlycoPP, and GPP were compared with PTG-PLM based on each used PLMs. Except with ProtXNet PLM, PTG-PLM outperforms the existing tools for Ngly site prediction in terms of accuracy, recall, precision, F1, AUC, and MCC. Likewise, four existing tools for Kgly site prediction: BERT-Kgly, NetGlycate, Gly-PseAAC, and BPB-GlySite were compared with PTG-PLM based on each used PLMs. PTG-PLM also outperforms the existing tools for Kgly site prediction in terms of accuracy, recall, precision, F1, AUC, and MCC. The evaluation and comparison results indicate that the extracted features using PLMs are more informative than features extracted by the other feature extraction methods in the existing studies. In addition, CNN deep learning is better than the other machine learning methods in which the same features extracted by the six PLMs used in PTG-PLM that result in better performance. Thus, PTG-PLM can be used to predict the other types of PTMs by adjusting the PTG-PLM parameters.  
**Author Contributions:** Conceptualization, A.A. and W.G.; methodology, A.A.; software, A.A.; validation, A.A. and W.G.; formal analysis, A.A.; writing--original draft preparation, A.A. and W.G.; writing--review and editing, W.G., M.R., M.G.V. and A.-b.M.S.; supervision, W.G., M.R., M.G.V. and A.-b.M.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The datasets as well as the code is available in ([[https://github.com/Alhasanalkuhlani/PTG-PLM](https://github.com/Alhasanalkuhlani/PTG-PLM)]([https://github.com/Alhasanalkuhlani/PTG-PLM](https://github.com/Alhasanalkuhlani/PTG-PLM))).  
**Conflicts of Interest:** The authors declare no conflict of interest.