This tool provides a dedicated interface for plant protein post-translational modification (PTM) analysis, supporting nine tasks: acetylation, n_glycosylation, ubiquitination, phosphorylation, crotonylation, hydroxyisobutyrylation, malonylation, s_acylation, and succinylation. It supports both site-level probability prediction and mutation effect assessment.
The method is based on the PlantPTM dataset (Molecular Plant), which integrates five public PTM databases and experimentally validated mass spectrometry data from the literature, covering six plant species including Arabidopsis thaliana and rice. The dataset spans nine PTM site types, including N-glycosylation, S-acylation, lysine acylation, ubiquitination, and phosphorylation. The model combines protein language model representations with structural features: RSA(1) + pLDDT(1) + SS(3) + angles(6) = 11 dimensions. It outputs residue-level modification probabilities and provides false positive rate (FPR) references. In mutation mode, it compares WT and mutant probabilities (delta_prob) to help rapidly screen potentially functional variants.
Tool Limitations: 1) The current model does not explicitly incorporate subcellular localization, which may lead to biologically implausible predictions in specific cellular compartment contexts. 2) Training data covers six plant species; results outside the training species range should be interpreted cautiously and ideally validated experimentally.
1. Task Type
2. Analysis Mode
Model Performance Metrics
| Task | AUPRC | AUROC | MCC | F1 | Precision | Sn | Sp | BestThreshold |
|--------------------------|---------:|---------:|---------:|---------:|----------:|---------:|---------:|--------------:|
| acetylation_k | 0.293598 | 0.857072 | 0.320577 | 0.353425 | 0.293675 | 0.443698 | 0.945135 | 0.690 |
| glycosylation_n | 0.541512 | 0.954157 | 0.564524 | 0.572741 | 0.433875 | 0.842342 | 0.915101 | 0.320 |
| ubiquitination_k | 0.224094 | 0.802456 | 0.254521 | 0.292695 | 0.211087 | 0.477178 | 0.890374 | 0.535 |
| phosphorylation_st | 0.297552 | 0.869686 | 0.333739 | 0.357119 | 0.296542 | 0.448798 | 0.957550 | 0.860 |
| phosphorylation_y | 0.238416 | 0.775083 | 0.261183 | 0.317265 | 0.227309 | 0.525046 | 0.849311 | 0.560 |
| crotonylation_k | 0.351780 | 0.837288 | 0.354494 | 0.411367 | 0.297756 | 0.665167 | 0.827394 | 0.520 |
| hydroxyisobutyrylation_k | 0.419311 | 0.852072 | 0.403022 | 0.470808 | 0.381343 | 0.615117 | 0.877028 | 0.680 |
| malonylation_k | 0.208657 | 0.781781 | 0.252083 | 0.291367 | 0.210117 | 0.475073 | 0.888787 | 0.595 |
| s_acylation_c | 0.370137 | 0.753056 | 0.343977 | 0.472527 | 0.419512 | 0.540881 | 0.837209 | 0.590 |
| succinylation_k | 0.262392 | 0.784928 | 0.263801 | 0.306056 | 0.196222 | 0.695167 | 0.739898 | 0.380 |
References
Dou, G., Wang, K., Qin, Z., Zhang, M., Zhang, W., Miao, C., Li, J., & Chen, Z. (2026). PlantPTM: A deep learning framework integrating protein language models with multi-view features for predicting diverse post-translational modification sites in plants. Molecular Plant, S1674-2052(26)00260-1. Advance online publication. https://doi.org/10.1016/j.molp.2026.08.002
Last updated: 2026-08-25