UniPTMs: The First Unified Multi-type PTM Site Prediction Model via Master-Slave Architecture-Based Multi-Stage Fusion Strategy and Hierarchical Contrastive Loss
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
As a core mechanism of epigenetic regulation in eukaryotes, protein
post-translational modifications (PTMs) require precise prediction to decipher
dynamic life activity networks. To address the limitations of existing deep
learning models in cross-modal feature fusion, domain generalization, and
architectural optimization, this study proposes UniPTMs: the first unified
framework for multi-type PTM prediction. The framework innovatively establishes
a "Master-Slave" dual-path collaborative architecture: The master path
dynamically integrates high-dimensional representations of protein sequences,
structures, and evolutionary information through a Bidirectional Gated
Cross-Attention (BGCA) module, while the slave path optimizes feature
discrepancies and recalibration between structural and traditional features
using a Low-Dimensional Fusion Network (LDFN). Complemented by a Multi-scale
Adaptive convolutional Pyramid (MACP) for capturing local feature patterns and
a Bidirectional Hierarchical Gated Fusion Network (BHGFN) enabling multi-level
feature integration across paths, the framework employs a Hierarchical Dynamic
Weighting Fusion (HDWF) mechanism to intelligently aggregate multimodal
features. Enhanced by a novel Hierarchical Contrastive loss function for
feature consistency optimization, UniPTMs demonstrates significant performance
improvements (3.2%-11.4% MCC and 4.2%-14.3% AP increases) over state-of-the-art
models across five modification types and transcends the Single-Type Prediction
Paradigm. To strike a balance between model complexity and performance, we have
also developed a lightweight variant named UniPTMs-mini.
