ResUbiNet: A Novel Deep Learning Architecture for Ubiquitination Site Prediction.

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. 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.

Methods

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

SUBMITTER: Duan Z 

PROVIDER: S-EPMC12606654 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature