MFB: Novel Graph Neural Networks to Understand, Predict, and Design Allosteric Transcription Factors

MFB：用于理解、预测和设计变构转录因子的新型图神经网络

• 批准号: 2226663
• 负责人: Corey Wilson
• 金额: $ 148.59万
• 依托单位: Georgia Tech Research Corporation
• 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=2226663&HistoricalAwards=false
• 关键词: MFB; Novel; Graph; Neural; Networks

In this Molecular Foundations for Biotechnology (MFB) project, Professors Corey J. Wilson, Matthew J. Realff, and Yao Xie at the Georgia Institute of Technology are leveraging both novel experimental and machine learning strategies to understand, predict, and design allosteric communication in a family of proteins called transcription factors that regulate gene expression in living systems. Protein allostery is an important protein function which enables communication between different parts of a functional protein that are widely separated. Our lack of understanding of the mechanism of allostery prevents scientist and engineers from designing this critically important function. By combining the tools of molecular biology and artificial intelligence, this project aims to decipher structure/activity patterns for naturally occurring and engineered transcription factors at the molecular level, specifically at the level of individual amino acids. Understanding the rules that govern allosteric communication would, in principle, enable investigators to design new transcription factors for a variety of high-impact applications such as manipulating the composition of bacteria in the gut. This project involves a blend of biochemistry, biophysics, engineering, and machine learning approaches that will facilitate student engagement across traditional disciplinary boundaries. In addition to diverse student involvement, the broader impacts of this project will include the development of innovative pedagogical modules in the areas of machine learning and biological engineering. This project will contribute to the development of a diverse and engaged STEM (science, technology, engineering and mathematics) workforce, building a firm foundation for a lifetime of contributions to research, education, and their integration. Protein allostery is a vitally important protein function that has proven to be a vexing problem to understand at the molecular level. The goal of this project is to decipher the underlying molecular mechanisms by which the allosteric signal traverses the scaffold across several naturally occurring and engineer transcription factors with alternate allosteric controls from the broader LacI/GalR family of protein homologues. In general, allosteric communication involves networks of non-neighboring amino acid positions; therefore, traditional pairwise computational approaches (e.g., molecular mechanics simulations, and related computer-aided protein design strategies) are of limited use in understanding and designing allosteric networks a priori. Accordingly, this project seeks to develop novel machine learning approaches and complementary experimental strategies to accelerate scientific progress and transform the nature of studying and designing allosteric communication. This project has the potential to lead to a paradigm shift with regard to the origins and construction (design) of allosteric networks in a single fold. Moreover, the machine learning approaches developed in this project can in principle be applied to other complex network problems beyond the designated protein systems – e.g., distillation column sequences, communication systems, and power grid systems.This project is jointly supported by the Division of Chemistry, the Division of Chemical, Bioengineering, Environmental and Transport Systems, and the Division of Information and Intelligent Systems.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.

在这个生物技术分子基础（MFB）项目中，乔治亚技术研究所的Corey J. Wilson，Matthew J. Realff和Yao Xie正在利用新颖的实验和机器学习策略来理解，预测和设计变形蛋白的变形因素，这些转录因子在调节生命系统中的转化因素中具有变形蛋白。蛋白质变构是一种重要的蛋白质功能，可以在功能蛋白的不同部分之间进行通信。我们对变构机制的理解不足阻止了科学家和工程师设计这一至关重要的功能。通过结合分子生物学和人工智能的工具，该项目旨在破译结构/活性模式，用于分子水平上自然发生和工程转录因子，特别是在单个氨基酸的水平上。理解主导变构沟通的规则原则上将使研究人员能够为各种高影响应用程序（例如操纵肠道中的细菌组成）设计新的转录因子。该项目涉及生物化学，生物物理学，工程和机器学习方法的融合，这些方法将有助于跨传统学科界限的学生参与。除了潜水员的参与外，该项目对该项目的更广泛影响将有助于在机器学习和生物技术领域的创新教学模块的发展。该项目将有助于发展多样性和参与的STEM（科学，技术，工程和数学）劳动力，为研究，教育及其整合的贡献奠定了坚定的基础。蛋白质变构是一种至关重要的蛋白质功能，已被证明是在分子水平上理解的一个烦人的问题。该项目的目的是破译基础分子机制，通过这些机制，变构信号通过这些机制横穿几种天然发生和工程师的转录因子，并具有从更广泛的蛋白质同源物的较宽的laci/galr家族中的替代变构控制。一般而言，变构通信涉及非邻近氨基酸位置的网络。因此，传统的成对计算方法（例如，分子力学模拟和相关的计算机辅助蛋白设计策略）在理解和设计变构网络方面的使用有限。根据这个项目，该项目旨在开发新颖的机器学习方法和完整的实验策略，以加速科学进步并改变研究和设计变构沟通的性质。该项目有可能导致单一折叠网络的起源和构建（设计）的范式转移。此外，本项目中开发的机器学习方法原则上可以应用于指定的蛋白质系统以外的其他复杂网络问题，例如，蒸馏柱序列，通信系统和电网系统。该项目由化学，化学，生物启动，环境和运输系统和智能奖的化学，化学，生物工程性，环境和运输系统的诉讼划分。使用基金会的智力优点和更广泛的影响评估标准进行评估。