Multimodal deep learning for predicting protein ubiquitination sites.

ABSTRACT:

Motivation

Ubiquitination is a crucial post-translational modification that regulates various biological functions, including protein degradation, signal transduction, and cellular homeostasis. Accurate identification of ubiquitination sites is essential for understanding these mechanisms, yet existing prediction tools often lack generalizability across diverse datasets. To address this limitation, we developed Multimodal Ubiquitination Predictor, a deep learning-based approach capable of predicting ubiquitination sites across general, human-specific, and plant-specific datasets. By integrating diverse protein sequence representations-one-hot encoding, embeddings, and physicochemical properties-within a unified deep-learning framework, the proposed method significantly enhances pre

SUBMITTER: Pakhrin SC 

PROVIDER: S-EPMC12408473 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature