MFB: NSF-BSF: Data-Adaptive and Metamorphosis Machine Learning Architectures for Generative Protein Design of Metal Biosensors

MFB：NSF-BSF：用于金属生物传感器生成蛋白质设计的数据自适应和变形机器学习架构

• 批准号: 2226804
• 负责人: Joanna Slusky
• 金额: $ 150万
• 依托单位: University of Kansas Center for Research Inc
• 依托单位国家: 美国
• 项目类别: Standard 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=2226804&HistoricalAwards=false
• 关键词: MFB; NSF; BSF; Data; Adaptive

Dr. Joanna Slusky from the University of Kansas in collaboration with Dr. Margarita Osadchy and Dr. Rachel Kolodny from the University of Haifa, Israel, are designing protein-based biosensors for pollutants that could be used in efforts create cleaner waterways. The technology undergirding the creation of the sensors will include novel machine learning architectures. These new ML architectures could be used to develop biosensors for other applications in biotechnology more broadly. The research will create opportunities for undergraduates from Kansas to learn about how nature controls metal binding, to acquire knowledge and skills that span the intersection of biology and computer science, and to participate in international collaborations.This project involves the use of existing and newly developed machine learning architectures for the generation of new scaffolds for biosensors, particularly metal sensing proteins. Membrane β-barrels are ideal platforms for biosensors for a wide range of analytes because of their shape, variety of radii, and the ability to proscribe chemical moieties on the interior of the barrel. Moreover, because these proteins span membranes, the act of analyte binding causes a measurable change in conductance across the membrane, which can be used as biosensor output. This project thas three ambitious ML objectives: 1) create a fold-specific language model using less training data, 2) generate novel protein sequences with patch-based metamorphosis, locally-aware iterative design, and 3) design and test protein barrel candidates as biosensors for water contaminants. The research pursues a new way to restrict attention for transformer-based language models, a new way to traverse protein sequence space with a metamorphosis based generator, a deeper understanding of membrane β-barrels, newly generated scaffolds for biosensing, a deeper understanding of what makes some protein sites ideal for metal binding, and a design method for proteins that could be used to identify heavy metal contaminants in water. This collaborative US/Israel project is supported by the US National Science Foundation and the Israeli Binational Science Foundation. At the US National Science Foundation, the project is supported by the Division of Chemistry, the Division of Chemical, Bioengineering, Environmental and Transport Systems, the Division of Information and Intelligent Systems, and the Division of Molecular and Cellular Biosciences.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.

来自堪萨斯大学的Joanna Slusky博士与来自以色列海法大学的Margarita Osadchy博士和Rachel Kolodny博士合作，正在设计基于蛋白质的污染物生物传感器，可用于创造更清洁的水道。支撑传感器创建的技术将包括新型机器学习架构。这些新的ML架构可以用于更广泛地开发生物传感器，用于生物技术中的其他应用。该研究将为来自堪萨斯的本科生创造机会，了解自然如何控制金属结合，获得跨越生物学和计算机科学交叉的知识和技能，并参与国际合作。该项目涉及使用现有和新开发的机器学习架构来生成生物传感器的新支架，特别是金属传感蛋白。膜β-桶是用于各种分析物的生物传感器的理想平台，因为它们的形状、半径的变化以及在桶内部禁止化学部分的能力。此外，由于这些蛋白质跨越膜，分析物结合的行为导致跨膜电导的可测量变化，其可用作生物传感器输出。该项目有三个雄心勃勃的ML目标：1）使用更少的训练数据创建折叠特定的语言模型，2）生成具有基于补丁的变态，局部感知迭代设计的新型蛋白质序列，以及3）设计和测试蛋白质桶候选物作为水污染物的生物传感器。 该研究寻求一种新的方法来限制基于transformer的语言模型的注意力，一种新的方法来遍历蛋白质序列空间与基于变形的生成器，更深入地了解膜β桶，新生成的生物传感支架，更深入地了解是什么使一些蛋白质位点成为金属结合的理想选择，以及一种可用于识别水中重金属污染物的蛋白质的设计方法。这个美国/以色列合作项目得到了美国国家科学基金会和以色列两国科学基金会的支持。在美国国家科学基金会，该项目得到了化学部，化学，生物工程，环境和运输系统部，信息和智能系统部，以及分子和细胞生物科学部的支持。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。

项目成果

General features of transmembrane beta barrels from a large database.

• DOI: 10.1073/pnas.2220762120
• 发表时间: 2023-07-18
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Montezano, Daniel;Bernstein, Rebecca;Copeland, Matthew M.;Slusky, Joanna S. G.
• 通讯作者: Slusky, Joanna S. G.