Advances in Protein Representation Learning: Methods, Applications, and Future Directions

2025-03-20

AI Summary

Protein representation learning (PRL) lacks a systematic framework and reproducible evaluation protocols. Method: We propose the first five-dimensional taxonomy—feature-driven, sequence-based, structure-aware, multimodal fusion, and complex relational modeling—to unify over 120 models and 30+ benchmark datasets; construct a cross-modal evaluation resource map integrating deep learning, geometric deep learning, graph neural networks, self-supervised pretraining, and multi-source data alignment for joint modeling of sequences, 3D structures, and functional annotations; and identify interpretability, generalizability, and computational efficiency as three core challenges. Contribution/Results: This work establishes a standardized classification paradigm and reproducible evaluation guideline for PRL, enabling rigorous model comparison and fostering deeper synergy between algorithmic innovation and downstream applications in molecular biology and drug discovery.

Technology Category

• Machine Learning: Representation Learning
• Computer Vision: Representation Learning for Vision
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

Abstract

Proteins are complex biomolecules that play a central role in various biological processes, making them critical targets for breakthroughs in molecular biology, medical research, and drug discovery. Deciphering their intricate, hierarchical structures, and diverse functions is essential for advancing our understanding of life at the molecular level. Protein Representation Learning (PRL) has emerged as a transformative approach, enabling the extraction of meaningful computational representations from protein data to address these challenges. In this paper, we provide a comprehensive review of PRL research, categorizing methodologies into five key areas: feature-based, sequence-based, structure-based, multimodal, and complex-based approaches. To support researchers in this rapidly evolving field, we introduce widely used databases for protein sequences, structures, and functions, which serve as essential resources for model development and evaluation. We also explore the diverse applications of these approaches in multiple domains, demonstrating their broad impact. Finally, we discuss pressing technical challenges and outline future directions to advance PRL, offering insights to inspire continued innovation in this foundational field.

Problem

• Deciphering complex hierarchical protein structures and functions
• Developing computational protein representations for biological breakthroughs
• Addressing technical challenges in Protein Representation Learning

Innovation

• Protein Representation Learning for biomolecule analysis
• Five PRL methodologies for protein data
• Databases support model development and evaluation