SUMO-LMNet: Lossless mapping network for predicting SUMOylation sites in SUMO1 and SUMO2 using high-dimensional features.

ABSTRACT: Accurate SUMOylation site prediction is crucial for deciphering gene regulation and disease mechanisms. However, distinguishing SUMO1 and SUMO2 modifications remains a major challenge due to their structural similarities. Conventional prediction models often struggle to differentiate between these paralogues, limiting their applicability in biological research. To address this, we introduce SUMO-LMNet, a deep learning-based framework for the precise prediction of SUMO1 and SUMO2 sites. Unlike previous models, SUMO-LMNet integrates a lossless mapping strategy and deep learning architectures to enhance both prediction accuracy and interpretability. Our model extracts high-dimensional features from sequences and transforms them into two-dimensional feature maps, enabling convolutional neural

SUBMITTER: Ho CH 

PROVIDER: S-EPMC11937687 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature