核酸酶发展的适应度景观和热力学方法:从经典方法到人工智能集成。

IF 3.1 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Shuntaro Takahashi, Michiaki Hamada, Hisae Tateishi-Karimata, Naoki Sugimoto
{"title":"核酸酶发展的适应度景观和热力学方法:从经典方法到人工智能集成。","authors":"Shuntaro Takahashi, Michiaki Hamada, Hisae Tateishi-Karimata, Naoki Sugimoto","doi":"10.1039/d5cb00105f","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":40691,"journal":{"name":"RSC Chemical Biology","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421328/pdf/","citationCount":"0","resultStr":"{\"title\":\"Fitness landscapes and thermodynamic approaches to development of nucleic acids enzymes: from classical methods to AI integration.\",\"authors\":\"Shuntaro Takahashi, Michiaki Hamada, Hisae Tateishi-Karimata, Naoki Sugimoto\",\"doi\":\"10.1039/d5cb00105f\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":40691,\"journal\":{\"name\":\"RSC Chemical Biology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-08-21\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12421328/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"RSC Chemical Biology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1039/d5cb00105f\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"RSC Chemical Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1039/d5cb00105f","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

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NAs包括NA酶,如核酶和dnazyme。它们的折叠和靶结合是由核碱基之间的相互作用控制的,包括碱基对,这遵循热力学原理。为了阐明NA酶的生物学机制和实现多种技术应用,有必要明确NA酶的一级序列与催化活性之间的关系。与广泛使用了半个多世纪的预测NA双链稳定性的方法不同,由于活性测定的低通量,NA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提高了我们对NA酶序列-功能关系的理解。本文综述了数据驱动化学在了解NA酶在核苷酸水平上的反应机制以及从序列信息预测具有催化活性的新型NA酶方面的贡献。此外,我们还讨论了在不同溶液条件下预测NA酶活性的潜在数据库,以及它们与人工智能的集成,以供未来应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fitness landscapes and thermodynamic approaches to development of nucleic acids enzymes: from classical methods to AI integration.

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.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
6.10
自引率
0.00%
发文量
128
审稿时长
10 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信