A Comprehensive Review of Protein Language Models (2502.06881v1)

Abstract: At the intersection of the rapidly growing biological data landscape and advancements in NLP, protein LLMs (PLMs) have emerged as a transformative force in modern research. These models have achieved remarkable progress, highlighting the need for timely and comprehensive overviews. However, much of the existing literature focuses narrowly on specific domains, often missing a broader analysis of PLMs. This study provides a systematic review of PLMs from a macro perspective, covering key historical milestones and current mainstream trends. We focus on the models themselves and their evaluation metrics, exploring aspects such as model architectures, positional encoding, scaling laws, and datasets. In the evaluation section, we discuss benchmarks and downstream applications. To further support ongoing research, we introduce relevant mainstream tools. Lastly, we critically examine the key challenges and limitations in this rapidly evolving field.

• The paper presents a systematic review of Protein Language Models (PLMs), covering their historical context, diverse model architectures (especially Transformer-based), evaluation metrics, significant challenges, and practical biological applications.
• It details how PLMs, powered by large datasets and Transformer architectures like ProtTrans and ESM, are evaluated across downstream tasks like structure and function prediction, often achieving results comparable to experimental methods.
• Key challenges identified include high computational costs and data requirements, while future directions emphasize multimodal approaches and the potential for PLMs to accelerate biological discovery in fields like drug design and enzyme engineering.

A Comprehensive Review of Protein LLMs

Protein LLMs (PLMs) represent a convergence point between biological data analysis and advancements in NLP. This paper presents a systematic review of PLMs, highlighting historical milestones, current trends, model architectures, evaluation metrics, and the challenges that mark the field.

Historical Context and Mainstream Trends

PLMs have evolved rapidly due to the exponential growth of protein sequence data and breakthroughs in NLP, particularly the success of the Transformer model. The analogy between protein sequences and human language (both comprising linear chains of elements) underpins the use of NLP methodologies to predict protein properties and behaviors. The review elucidates how these models have transitioned from merely adopting NLP techniques to sophisticated architectures tailored for biological data, emphasizing scalability and integrative capabilities.

Model Architectures

The review categorizes PLMs into those based on transformer architectures and others. Non-transformer-based models include traditional neural networks like CNNs, RNNs, and their applications in early protein sequence representation. Nevertheless, the transformational impact of the Transformer model—especially in encoder (as seen in BERT) and decoder configurations (exemplified by GPT)—has fostered the development of highly parameterized PLMs that encode proteins with enhanced precision. The paper also discusses innovations such as ProtTrans and the ESM series, which illustrate the effectiveness of scaling models with protein-specific modifications.

Evaluation Metrics and Applications

For evaluating PLMs, the paper identifies downstream applications and benchmarks that range from traditional structure and function predictions to more sophisticated tasks like mutation effects prediction. The authors emphasize that these models now often match or surpass experimental results due to their ability to capture evolutionary, structural, and dynamic information from sequences alone.

Challenges and Future Directions

The paper does not shy away from elucidating the key challenges PLMs face. Foremost among these is the reliance on massive datasets and computational costs associated with scaling these models. There is also the pertinent issue of model generalization across different tasks and the trade-offs between model size and performance. Furthermore, the paper highlights the emerging trend of MSA-free models and multimodal approaches that integrate sequence, structure, and function, suggesting that these directions may hold the key to overcoming current limitations.

Practical Implications

Practically, PLMs have demonstrated utility in drug discovery, enzyme engineering, and synthetic biology, where their strong predictive capabilities significantly streamline the experimental workloads. The authors speculate that future advancements in PLMs could lead to more efficient computers and algorithms that further refine our understanding of protein dynamics and functionalities.

Conclusion

This comprehensive review serves as both a guide and a benchmark for understanding the current state and future potential of protein LLMs. By systematically exploring the architectures, applications, and inherent challenges of PLMs, the authors provide a pivotal resource for researchers aiming to navigate and contribute to this rapidly developing field. The ongoing improvements in these models suggest a future where computational biology can more rapidly translate data into actionable scientific knowledge, enhancing our ability to make meaningful biological discoveries.