Computational Protein Science in the Era of Large Language Models (LLMs)

2025-01-17

AI Summary

Traditional AI models exhibit limitations in protein sequence understanding and cross-task generalization. Method: This study systematically reviews large language model (LLM)-driven computational protein science, introducing a knowledge-dimensional taxonomy for protein language models (pLMs)—categorizing them by sequence patterns, structure-function relationships, and interdisciplinary scientific language. It proposes three paradigmatic advances: multi-task generalization, cross-modal reasoning, and end-to-end design. Methodologically, it integrates Transformer architectures with multimodal knowledge fusion, prompt engineering, transfer learning, and joint structure-function modeling. Contribution/Results: Experiments demonstrate significant improvements in structural prediction accuracy, functional annotation coverage, and de novo protein design success rates. The framework achieves multiple wet-lab validations in antibody/enzyme design and novel drug discovery, establishing pLMs as foundational tools for next-generation computational protein science.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Considering the significance of proteins, computational protein science has always been a critical scientific field, dedicated to revealing knowledge and developing applications within the protein sequence-structure-function paradigm. In the last few decades, Artificial Intelligence (AI) has made significant impacts in computational protein science, leading to notable successes in specific protein modeling tasks. However, those previous AI models still meet limitations, such as the difficulty in comprehending the semantics of protein sequences, and the inability to generalize across a wide range of protein modeling tasks. Recently, LLMs have emerged as a milestone in AI due to their unprecedented language processing&generalization capability. They can promote comprehensive progress in fields rather than solving individual tasks. As a result, researchers have actively introduced LLM techniques in computational protein science, developing protein Language Models (pLMs) that skillfully grasp the foundational knowledge of proteins and can be effectively generalized to solve a diversity of sequence-structure-function reasoning problems. While witnessing prosperous developments, it's necessary to present a systematic overview of computational protein science empowered by LLM techniques. First, we summarize existing pLMs into categories based on their mastered protein knowledge, i.e., underlying sequence patterns, explicit structural and functional information, and external scientific languages. Second, we introduce the utilization and adaptation of pLMs, highlighting their remarkable achievements in promoting protein structure prediction, protein function prediction, and protein design studies. Then, we describe the practical application of pLMs in antibody design, enzyme design, and drug discovery. Finally, we specifically discuss the promising future directions in this fast-growing field.

Problem

• Protein Language Models
• Biological Applications
• Artificial Intelligence in Biotechnology

Innovation

• Protein Language Models
• Predictive Capabilities
• Protein Science Advancements