Expanding the universe of functional metalloproteins through AI-assisted computational design

通过人工智能辅助计算设计扩展功能金属蛋白的范围

• 批准号: 2137880
• 负责人: Parisa Hosseinzadeh
• 金额: $ 58.5万
• 依托单位: University of Oregon Eugene
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2025-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2137880&HistoricalAwards=false
• 关键词: Expanding; universe; functional; metalloproteins; through

Metalloproteins are an abundant class of proteins. They perform some of the most difficult and life-sustaining reactions on earth with high efficiency. They have potential as green biocatalysts for applications in energy production and bioremediation. Adding new functions to these proteins or generating completely novel metalloproteins is challenging. Computational methods will be used to design new functional metalloproteins. The project will also introduce high school students and undergraduates to protein engineering.The goal of this project is to expand the universe of functional metalloproteins. To address the limitations of native proteins and the current challenges facing traditional design approaches, latest advances in machine learning in combination with protein design software will be applied to generate: (a) a library of functional electron transfer proteins with diverse shapes that contain a well-known 2His-1Cys copper binding motif (T1Cu site), (b) a library of functional catalytic proteins with 3His zinc binding motifs (hydrolase active site), and (c) a new platform for metal coordination using the non-canonical bpy-ala amino acid. It is hoped that the results obtained will provide new insight into the role of protein shape on tuning properties of the metal center and the rules for designing new metalloproteins. Methods developed in this proposal can be generalized to the design of other metalloproteins (dinuclear, heme binding, etc.) and small molecule-binding proteins, largely expanding the current scope of functional protein design.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

金属蛋白是一类丰富的蛋白质。它们以高效率执行地球上一些最困难和维持生命的反应。 它们作为绿色生物催化剂在能源生产和生物修复中具有潜在的应用价值。为这些蛋白质添加新功能或产生全新的金属蛋白是具有挑战性的。计算方法将用于设计新的功能金属蛋白。 本计画也将介绍高中生与大学生蛋白质工程，其目标是拓展功能性金属蛋白质的领域。为了解决天然蛋白质的局限性和传统设计方法目前面临的挑战，机器学习的最新进展与蛋白质设计软件相结合，将用于生成：（a）具有不同形状的功能性电子转移蛋白的文库，其含有众所周知的2 His-1Cys铜结合基序（T1 Cu位点），（B）具有3 His锌结合基序（水解酶活性位点）的功能性催化蛋白文库，和（c）使用非规范bpy-ala氨基酸的金属配位的新平台。希望所获得的结果将提供新的见解蛋白质的形状上的金属中心的调谐性能的作用和设计新的金属蛋白质的规则。在这个建议中开发的方法可以推广到其他金属蛋白（双核，血红素结合等）的设计。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。