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。它们能高效地完成一些地球上最困难、最能维持生命的反应。它们在能源生产和生物修复方面具有作为绿色生物催化剂的潜力。为这些蛋白添加新的功能或生成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。该项目还将向高中生和本科生介绍蛋白质工程。该项目的目标是扩大功能性金属蛋白的范围。为了解决天然蛋白质的局限性和传统设计方法面临的当前挑战，将应用机器学习与蛋白质设计软件相结合的最新进展来生成：(a)包含已知的2His-1Cys铜结合基序（T1Cu位点）的各种形状的功能电子转移蛋白库，(b)包含3His锌结合基序（水解酶活性位点）的功能催化蛋白库，以及(c)使用非规范的bpy-ala氨基酸进行金属配位的新平台。希望这些结果能够为蛋白质形状对金属中心调谐特性的作用以及设计新型金属蛋白的规律提供新的见解。本文提出的方法可以推广到其他金属蛋白（双核、血红素结合等）和小分子结合蛋白的设计中，极大地扩展了目前功能蛋白设计的范围。该奖项反映了美国国家科学基金会的法定使命，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。