ArticleFrontiers in physiology2021
A Transfer Learning-Based Approach for Lysine Propionylation Prediction.
Article in Frontiers in physiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Leveraging learned representations and multitask learning for lysine methylation site discovery.Scientific reports · 2026Article
- PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks.BMC bioinformatics · 2026Article
- Post-translational acylation of proteins in cardiac hypertrophy.Nature reviews. Cardiology · 2025Review
- Leveraging learned representations and multitask learning for lysine methylation site discovery.bioRxiv : the preprint server for biology · 2025Article
- A hybrid feature extraction scheme for efficient malonylation site prediction.Scientific reports · 2022Article
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4 authors.
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Abstract
Lysine propionylation is a newly discovered posttranslational modification (PTM) and plays a key role in the cellular process. Although proteomics techniques was capable of detecting propionylation, large-scale detection was still challenging. To bridge this gap, we presented a transfer learning-based method for computationally predicting propionylation sites. The recurrent neural network-based deep learning model was trained firstly by the malonylation and then fine-tuned by the propionylation. The trained model served as feature extractor where protein sequences as input were translated into numerical vectors. The support vector machine was used as the final classifier. The proposed method reached a matthews correlation coefficient (MCC) of 0.6615 on the 10-fold crossvalidation and 0.3174 on the independent test, outperforming state-of-the-art methods. The enrichment analysis indicated that the propionylation was associated with these GO terms (GO:0016620, GO:0051287, GO:0003735, GO:0006096, and GO:0005737) and with metabolism. We developed a user-friendly online tool for predicting propoinylation sites which is available at http://47.113.117.61/.
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