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核酸酶开发的适应度景观与热力学方法：从经典方法到人工智能整合

Fitness landscapes and thermodynamic approaches to development of nucleic acids enzymes: from classical methods to AI integration.

作者信息

Takahashi Shuntaro, Hamada Michiaki, Tateishi-Karimata Hisae, Sugimoto Naoki

机构信息

FIBER (Frontier Institute for Biomolecular Engineering Research), Konan University 7-1-20 Minatojima-Minamimachi Chuo-ku Kobe 650-0047 Japan

FIRST (Graduate School of Frontiers of Innovative Research in Science and Technology), Konan University 7-1-20 Minatojima-Minamimachi Chuo-ku Kobe 650-0047 Japan.

出版信息

RSC Chem Biol. 2025 Aug 21. doi: 10.1039/d5cb00105f.

DOI:10.1039/d5cb00105f
PMID:40937336
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12421328/
Abstract

Nucleic acids (NA), namely DNA and RNA, dynamically fold and unfold to perform their functions in cells. Functional NAs include NA enzymes, such as ribozymes and DNAzymes. Their folding and target binding are governed by interactions between nucleobases, including base pairings, which follow thermodynamic principles. To elucidate biological mechanisms and enable diverse technical applications, it is essential to clarify the relationship between the primary sequence and the catalytic activity of NA enzymes. Unlike methods for predicting the stability of NA duplexes, which have been widely used for over half a century, predictive approaches for the catalytic activity of NA enzymes remain limited due to the low throughput of activity assays. However, recent advances in genome analysis and computational data science have significantly improved our understanding of the sequence-function relationship in NA enzymes. This article reviews the contributions of data-driven chemistry to understanding the reaction mechanisms of NA enzymes at the nucleotide level and predicting novel NA enzymes with catalytic activity from sequence information. Furthermore, we discuss potential databases for predicting NA enzyme activity under various solution conditions and their integration with artificial intelligence for future applications.

摘要

核酸（NA），即DNA和RNA，在细胞中动态折叠和展开以执行其功能。功能性核酸包括核酸酶，如核酶和脱氧核酶。它们的折叠和与靶标的结合受核碱基之间相互作用的支配，包括遵循热力学原理的碱基配对。为了阐明生物学机制并实现各种技术应用，明确核酸酶一级序列与催化活性之间的关系至关重要。与已广泛使用半个多世纪的预测核酸双链体稳定性的方法不同，由于活性测定的通量较低，核酸酶催化活性的预测方法仍然有限。然而，基因组分析和计算数据科学的最新进展显著增进了我们对核酸酶序列-功能关系的理解。本文综述了数据驱动化学在核苷酸水平上理解核酸酶反应机制以及从序列信息预测具有催化活性的新型核酸酶方面的贡献。此外，我们讨论了用于预测各种溶液条件下核酸酶活性的潜在数据库及其与人工智能的整合，以用于未来应用。

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