ResUbiNet: A Novel Deep Learning Architecture for Ubiquitination Site Prediction

Ubiquitination, a unique post-translational modification, plays a cardinal role in diverse cellular functions such as protein degradation, signal transduction, DNA repair, and regulation of cell cycle. Thus, accurate prediction of potential ubiquitination sites is an urgent requirement for exploring the ubiquitination mechanism as well as the disease pathogenesis associated with ubiquitination processes.

This study introduces a novel deep learning architecture, ResUbiNet, which utilized a protein language model (ProtTrans), amino acid properties, and BLOSUM62 matrix for sequence embedding and multiple state-of-the-art architectural components, i.e., transformer, multi-kernel convolution, residual connection, and squeeze-and-excitation for feature extractions.

The results of cross-validation and external tests showed that the ResUbiNet model achieved better prediction performances in comparison with the available hCKSAAP_UbSite, RUBI, MDCapsUbi, and MusiteDeep models.

ResUbiNet's integration of advanced features and architectures significantly enhances prediction performance, aiding in understanding ubiquitination mechanisms and related diseases.