[论文解读] Computational Protein Science in the Era of Large Language Models (LLMs)

Wenqi Fan, Yi Zhou+6 more|ArXiv.org|Jan 17, 2025

Genetics, Bioinformatics, and Biomedical Research被引用 6

一句话总结

本论文综述蛋白质语言模型（pLMs）及其在结构预测、功能预测和设计中的应用，构建一个序列-结构-功能语言框架，并按学习到的知识对 pLMs 进行分类。

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.

研究动机与目标

• 解释蛋白质的序列-结构-功能语言及人工智能在计算蛋白质科学中的作用。
• 按其掌握的知识对现有蛋白质语言模型进行分类：序列模式、显式的结构/功能信息，以及外部语言。
• 总结 pLMs 在结构预测、功能预测和蛋白质设计中的使用及改造。
• 讨论 pLMs 的实际生物医药应用并概述未来方向。

提出的方法

• 将 pLMs 分类为基于序列、增强结构与功能、以及多模态三类。
• 描述代表性 pLMs 的预训练目标与架构，以及它们与蛋白质知识的关系。
• 解释通过编码器-解码器或集成架构，将 pLM 表示用于结构、功能和设计任务。
• 讨论生物领域大语言模型的优化策略，包括微调、提示和 PEFT 机制。
• 回顾下游应用，如抗体设计、酶设计和药物发现。

实验结果

研究问题

• RQ1 pLMs 如何捕获并利用关于蛋白质序列、结构及功能的知识？
• RQ2 存在哪些类别的 pLMs，它们掌握哪些知识？
• RQ3 pLMs 如何被改造以提升蛋白质结构预测、功能预测和设计任务？
• RQ4 当前 pLMs 的生物医学应用有哪些，预期的未来方向是什么？

主要发现

• pLMs can infer structural and functional information from protein sequences even without explicit evolutionary data in some cases.
• Single-sequence pLMs scale with parameter size and improve knowledge about protein structure at atomic resolution.
• pLMs are effectively used in structure prediction, function prediction, and protein design through various encoder, decoder, and prompting strategies.
• Multi-task and question-answering frameworks enable unified handling of sequence-structure-function reasoning tasks.
• There exist distinct categories of pLMs with different data inputs, training objectives, and architectural designs influencing their suitability for specific protein tasks.