大型语言模型（LLMs）时代的计算蛋白质科学
Computational Protein Science in the Era of Large Language Models (LLMs)
AI总结：
本文系统综述了大型语言模型在计算蛋白质科学中的应用，依据掌握的知识对蛋白质语言模型进行分类，并重点介绍了其在结构预测、功能预测、蛋白质设计及药物发现等方面的成就与未来方向。
摘要：
鉴于蛋白质的重要性，计算蛋白质科学一直是一个关键的科学领域，致力于揭示蛋白质序列-结构-功能范式内的知识并开发相关应用。在过去几十年中，人工智能（AI）对计算蛋白质科学产生了重大影响，在特定的蛋白质建模任务中取得了显著成功。然而，这些早期的AI模型仍存在局限性，例如难以理解蛋白质序列的语义，以及无法泛化到广泛的蛋白质建模任务中。近年来，LLMs凭借其前所未有的语言处理和泛化能力，已成为AI领域的一座里程碑。它们能够推动特定领域的全面进步，而不仅仅是解决单个任务。因此，研究人员积极将LLM技术引入计算蛋白质科学，开发了蛋白质语言模型，这些模型巧妙地掌握了蛋白质的基础知识，并能有效泛化以解决各种序列-结构-功能推理问题。在见证繁荣发展的同时，有必要对由LLM技术赋能的计算蛋白质科学进行系统性概述。首先，我们根据现有pLMs所掌握的蛋白质知识将其归纳为几类，即底层序列模式、显式结构与功能信息，以及外部科学语言。其次，我们介绍了pLMs的利用与适配，重点突出了它们在推动蛋白质结构预测、蛋白质功能预测和蛋白质设计研究方面的卓越成就。然后，我们描述了pLMs在抗体设计、酶设计和药物发现中的实际应用。最后，我们专门探讨了这一快速发展领域的未来发展方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
