Christopher Jurich, Qianzhen Shao, Xinchun Ran, Zhongyue J. Yang
{"title":"酶工程新时代的物理建模。","authors":"Christopher Jurich, Qianzhen Shao, Xinchun Ran, Zhongyue J. Yang","doi":"10.1038/s43588-025-00788-8","DOIUrl":null,"url":null,"abstract":"Enzyme engineering is entering a new era characterized by the integration of computational strategies. While bioinformatics and artificial intelligence methods have been extensively applied to accelerate the screening of function-enhancing mutants, physics-based modeling methods, such as molecular mechanics and quantum mechanics, are essential complements in many objectives. In this Perspective, we highlight how physics-based modeling will help the field of computational enzyme engineering reach its full potential by exploring current developments, unmet challenges and emerging opportunities for tool development. This Perspective highlights the vital role of physics-based modeling in computational enzyme engineering, exploring key advances, challenges and future steps. By integrating machine learning, these approaches can enhance each other, unlocking the full potential of enzyme design and discovery.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"5 4","pages":"279-291"},"PeriodicalIF":18.3000,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Physics-based modeling in the new era of enzyme engineering\",\"authors\":\"Christopher Jurich, Qianzhen Shao, Xinchun Ran, Zhongyue J. Yang\",\"doi\":\"10.1038/s43588-025-00788-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Enzyme engineering is entering a new era characterized by the integration of computational strategies. While bioinformatics and artificial intelligence methods have been extensively applied to accelerate the screening of function-enhancing mutants, physics-based modeling methods, such as molecular mechanics and quantum mechanics, are essential complements in many objectives. In this Perspective, we highlight how physics-based modeling will help the field of computational enzyme engineering reach its full potential by exploring current developments, unmet challenges and emerging opportunities for tool development. This Perspective highlights the vital role of physics-based modeling in computational enzyme engineering, exploring key advances, challenges and future steps. By integrating machine learning, these approaches can enhance each other, unlocking the full potential of enzyme design and discovery.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"5 4\",\"pages\":\"279-291\"},\"PeriodicalIF\":18.3000,\"publicationDate\":\"2025-04-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-025-00788-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-025-00788-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Physics-based modeling in the new era of enzyme engineering
Enzyme engineering is entering a new era characterized by the integration of computational strategies. While bioinformatics and artificial intelligence methods have been extensively applied to accelerate the screening of function-enhancing mutants, physics-based modeling methods, such as molecular mechanics and quantum mechanics, are essential complements in many objectives. In this Perspective, we highlight how physics-based modeling will help the field of computational enzyme engineering reach its full potential by exploring current developments, unmet challenges and emerging opportunities for tool development. This Perspective highlights the vital role of physics-based modeling in computational enzyme engineering, exploring key advances, challenges and future steps. By integrating machine learning, these approaches can enhance each other, unlocking the full potential of enzyme design and discovery.