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

TL;DR

This review surveys Protein Representation Learning (PRL) across five modalities—feature-based, sequence-based, structure-based, multimodal, and complex-based—outlining methodologies, datasets, and applications from protein property prediction to drug discovery. It emphasizes how sequence models (e.g., MSA Transformer, Evoformer, PLMs) and structure-aware approaches (residue/atomic/surface representations, symmetry-equivariant learning) complement each other, and how multimodal and complex-based methods enable richer embeddings for interactions and docking. The authors discuss extensive databases (UniProt, PDB, AlphaFoldDB, GO, etc.) and benchmarks (TAPE, CASP, DUD-E, CrossDocked), while highlighting key challenges such as data imbalances, computational scale, generalization to novel proteins, and explainability. By proposing concrete future directions—extending PRL to DNA/RNA, scalable training, robust generalization, and interpretable models—the paper outlines a roadmap for advancing PRL's impact on biology and medicine.

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.

Figures (5)

• Figure 1: The four levels of protein structure, organized by increasing complexity within the polypeptide chain. Primary structure refers to the specific sequence of amino acids. Secondary structure involves local folding patterns, such as $\alpha$-helices and $\beta$-sheets. Tertiary structure represents the overall three-dimensional conformation of a single polypeptide chain. Quaternary structure describes the assembly and interactions of multiple polypeptide chains within a protein complex.
• Figure 2: An overview of the key components and themes discussed in this review. The figure highlights the interconnections and relationships between the main topics, providing a comprehensive visual summary of the review's scope.
• Figure 3: Illustration of different structural representations of a protein at the tertiary level, along with their typical computational representations. Residue-Level Representation models the protein backbone, typically using alpha carbon (C$\alpha$) atoms to capture the overall fold and residue connectivity, with a corresponding graph-based representation. Atomic-Level Representation considers all individual atoms within the protein structure, including backbone and side-chain atoms, commonly represented as a point cloud or an all-atom graph that captures atomic interactions. Protein Surface Representation focuses on the solvent-accessible surface, highlighting geometric and physicochemical properties that influence binding and molecular recognition, often modeled using a surface mesh or a point cloud representation that encodes local curvature and electrostatic potential.
• Figure 4: Overview of key applications of Protein Representation Learning (PRL). PRL enables advancements in multiple domains, including protein property prediction, structure prediction, protein design and optimization, and drug discovery.
• Figure 5: Key future directions in Protein Representation Learning (PRL). The figure outlines four critical challenges and potential advancements: (i) Expanding PRL to DNA and RNA representation learning to leverage shared methodologies while addressing unique challenges, (ii) Enhancing scalability to improve computational efficiency and accessibility of large-scale models, (iii) Strengthening generalization to ensure robustness across unseen proteins and genetic variations, and (iv) Advancing explainability to improve model interpretability and facilitate trust in biological and biomedical applications.