Ubigo-X: Protein ubiquitination site prediction using ensemble learning with image-based feature representation and weighted voting
Year: 2025
Type: article
Abstract: Accurate ubiquitination identification is crucial in biological function analysis. We developed Ubigo-X, a novel protein ubiquitination prediction tool. Our training data, sourced from the Protein Lysine Modification Database (PLMD 3.0), comprised 53,338 ubiquitination and 71,399 non-ubiquitination sites, retained after CD-HIT and CD-HIT-2d sequence filtering. Three sub-models: Single-Type sequence-based features (Single-Type SBF), k-mer sequence-based features (Co-Type SBF), and structure-based and function-based features (S-FBF), were developed. Single-Type SBF used amino acid composition (AAC), amino acid index (AAindex), and one-hot encoding; Co-Type SBF used Single-Type SBF via k-mer encoding; and S-FBF used secondary structure, relative solvent accessibility (RSA)/absolute solvent-accessible area (ASA), and signal peptide cleavage sites. S-FBF was trained using XGBoost, while Single-Type SBF and Co-Type SBF were transformed into image-based features and trained using Resnet34. Ubigo-X was developed by combining the three models via a weighted voting strategy. Independent testing using PhosphoSitePlus data (65,421 ubiquitination and 61,222 non-ubiquitination sites) retained after filtering yielded 0.85, 0.79, and 0.58 for area under the curve (AUC), accuracy (ACC), and Matthews correlation coefficient (MCC), respectively. Further testing on imbalanced PhosphoSitePlus data (1:8 positive-to-negative sample ratio) yielded 0.94 AUC, 0.85 ACC, and 0.55 MCC. Using the GPS-Uber data, the AUC, ACC, and MCC were 0.81, 0.59, and 0.27, respectively. In conclusion, Ubigo-X outperformed existing tools in MCC (for both balanced and unbalanced data) and AUC and ACC (for balanced data), highlighting the efficacy of integrating image-based feature representation and weighted voting in ubiquitination prediction. Ubigo-X is a potential species-neutral ubiquitination site prediction tool, accessible at http://merlin.nchu.edu.tw/ubigox/.
Authors: Disline Manli Tantoh, Jen-Chieh Yu, Ching-Hsuan Chien, Wei-Yi Yeh, Yen-Wei Chu
Institution: National Chung Hsing University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,230
Awards: Smart Sustainable New Agriculture Research Center (Smarter)(II)( I ), Use Artificial Intelligence to Find the Influential Factors of Survival Analysis and Medication Prediction for Breast Cancer on Rna-Seq and Tnm and Their Applications, Profiling and Enhancing Resilient Brains Longitudinally across the Adult Lifespan( II ), G6899381696, Study into the Correlation between Neural Plasticity in Perseveration of Neuropsychological Performance in Delaying Aging of Patients with Mental Disorders
OpenAlex ID: W4412441008