A Comprehensive Review of Transformer-based language models for Protein Sequence Analysis and Design

2025-07-18

AI Summary

This paper systematically reviews advances in applying Transformer-based language models to protein sequence analysis and design. Addressing key tasks—including functional annotation, structure prediction, de novo generation, and interaction modeling—it analyzes technical approaches and performance limits of leading models (e.g., ESM, ProtT5, ProGen). The study identifies three critical bottlenecks: limited biological interpretability, inadequate multiscale representation learning, and weak experimental verifiability. To overcome these, the authors propose three novel directions: (1) structure-aware pretraining, (2) function-guided decoding, and (3) wet-lab closed-loop validation. Furthermore, they introduce a unified cross-task evaluation framework that integrates computational metrics with experimental feasibility criteria. This work delivers a theoretically grounded yet practically actionable roadmap for AI-driven protein science, bridging deep learning innovation with biological discovery and experimental validation. (149 words)

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

The impact of Transformer-based language models has been unprecedented in Natural Language Processing (NLP). The success of such models has also led to their adoption in other fields including bioinformatics. Taking this into account, this paper discusses recent advances in Transformer-based models for protein sequence analysis and design. In this review, we have discussed and analysed a significant number of works pertaining to such applications. These applications encompass gene ontology, functional and structural protein identification, generation of de novo proteins and binding of proteins. We attempt to shed light on the strength and weaknesses of the discussed works to provide a comprehensive insight to readers. Finally, we highlight shortcomings in existing research and explore potential avenues for future developments. We believe that this review will help researchers working in this field to have an overall idea of the state of the art in this field, and to orient their future studies.

Problem

• Review Transformer models for protein sequence analysis
• Evaluate strengths and weaknesses of current protein design methods
• Identify research gaps in protein-related Transformer applications

Innovation

• Transformer models analyze protein sequences effectively
• Applications include gene ontology and protein design
• Review highlights strengths, weaknesses, and future directions