Transformers in Protein: A Survey

2025-05-26

AI Summary

The protein informatics community lacks a systematic review of Transformer applications in protein science. Method: This paper introduces the first comprehensive taxonomy of Transformers tailored for protein science, systematically surveying over 100 studies across five core tasks—structure prediction, functional annotation, protein–protein interaction prediction, drug discovery, and multiscale modeling. It proposes the novel concept of “protein-specialized Transformers” to unify architectural variants, and integrates domain-specific pretraining strategies (e.g., domain adaptation, protein language modeling), benchmark datasets, open-source implementations, and standardized evaluation protocols. Contribution/Results: The work identifies critical challenges—including data scarcity, biological interpretability, and cross-task generalization—and outlines reproducible methodological pathways. It delivers a foundational theoretical framework, practical implementation guidelines, and a curated resource index, thereby advancing deep synergy between AI models and biological problem-solving in protein research.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Application Domains: Natural Sciences
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• 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
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

As protein informatics advances rapidly, the demand for enhanced predictive accuracy, structural analysis, and functional understanding has intensified. Transformer models, as powerful deep learning architectures, have demonstrated unprecedented potential in addressing diverse challenges across protein research. However, a comprehensive review of Transformer applications in this field remains lacking. This paper bridges this gap by surveying over 100 studies, offering an in-depth analysis of practical implementations and research progress of Transformers in protein-related tasks. Our review systematically covers critical domains, including protein structure prediction, function prediction, protein-protein interaction analysis, functional annotation, and drug discovery/target identification. To contextualize these advancements across various protein domains, we adopt a domain-oriented classification system. We first introduce foundational concepts: the Transformer architecture and attention mechanisms, categorize Transformer variants tailored for protein science, and summarize essential protein knowledge. For each research domain, we outline its objectives and background, critically evaluate prior methods and their limitations, and highlight transformative contributions enabled by Transformer models. We also curate and summarize pivotal datasets and open-source code resources to facilitate reproducibility and benchmarking. Finally, we discuss persistent challenges in applying Transformers to protein informatics and propose future research directions. This review aims to provide a consolidated foundation for the synergistic integration of Transformer and protein informatics, fostering further innovation and expanded applications in the field.

Problem

• Surveying Transformer applications in protein research
• Addressing gaps in protein structure and function prediction
• Providing datasets and resources for reproducibility

Innovation

• Transformer models enhance protein structure prediction
• Attention mechanisms improve protein function analysis
• Domain-oriented classification for protein research