Affiliation:
1. Key Laboratory of Biorheological Science and Technology (Ministry of Education), College of Bioengineering,
Chongqing University, Chongqing 400044, China
2. School of Pharmaceutical Sciences, Chongqing University,
Chongqing 401331, China
3. College of Bioengineering, Chongqing University, Chongqing 400044, China
Abstract
Introduction:
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.
Method:
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.
Results:
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.
Conclusion:
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.
Publisher
Bentham Science Publishers Ltd.