Collaborative Research: DMS/NIGMS 2: Novel machine-learning framework for AFMscanner in DNA-protein interaction detection

合作研究：DMS/NIGMS 2：用于 DNA-蛋白质相互作用检测的 AFM 扫描仪的新型机器学习框架

• 批准号: 10797460
• 负责人: Wenxuan Zhong
• 金额: $ 31.73万
• 依托单位: UNIVERSITY OF GEORGIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10797460
• 关键词: 3-Dimensional; Algorithms; Analysis of Variance; Area; Binding; Binding Sites; Biology; Biomedical Engineering; Biotechnology; Chemical Engineering; Computer Vision Systems; DNA; DNA Binding; DNA-Protein Interaction; Data; Detection; Development; Diagnosis; Diffusion; Disease; Education; Engineering; Face; Future; Genetic Transcription; Genome Scan; Graph; Hand; Image; Interdisciplinary Study; Intervention; Investigation; Location; Marketing; Mathematics; Methods; National Institute of General Medical Sciences; Physiological; Population Heterogeneity; Process; Production; Proteins; Psychological reinforcement; Reaction; Regulator Genes; Research; Resolution; Robot; Scanning Probe Microscopes; Science; System; Techniques; Technology; Testing; Time; Translations; Visualization; Work; analytical method; automated algorithm; commercialization; deep learning algorithm; detection method; genome-wide analysis; high resolution imaging; imaging approach; imaging modality; learning algorithm; learning strategy; machine learning framework; machine learning method; macromolecule; mathematical methods; new technology; novel; protein protein interaction; prototype; response; single molecule; skills; statistics; superresolution imaging; three dimensional structure; tool; whole genome

Quantifying TF-DNA binding, including locations, distributions, and binding mechanism is an important first step toward the understanding of gene regulatory machinery. In this proposal, we will develop an atomic force microscope (AFM)-based single-molecule imaging method for the detection and quantification of TF-DNA binding. The new technique brings the methods of mathematics and statistics to bear on the technological breakthrough in an experimental system. This new technology is inherently different from classical single-molecule imaging approaches, which solely rely on the technician’s experimental skills. Combining mathematics, statistics, bioengineering, and chemical engineering, this proposal creates a perfect platform for multidisciplinary research by merging analytics, biology, and engineering. We see this as a translational effort of what started as a lab-bench discovery into a new biotechnology tool, as the proposed machine learning (ML) methods combined with robot hands pave a revolutionary path to the massive production and fully automated system for precise TF-DNA imaging. Analytically, we face three challenges: construction of high-throughput images, prediction of TF binding region, and force decomposition to recover the binding mechanism. To attack these problems, we will (1) develop smoothing spline diffusion and annealing process for image super-resolution, (2) develop novel reinforcement learning algorithm for automatic TFBSs searching, and (3) develop graph ANOVA method to compare the TF-DNA binding mechanism. Our efforts in these areas should lead to (1) fundamental advances in image super-resolution and reinforcement learning algorithms which enjoy both algorithm simplicity and theoretical rigorous; (2) development and refinement of the technology for the rapid and precise genome-wide identification and quantification of TF-DNA binding sites using AFM technology; (3) visualization of not only TF-DNA binding sequence and location but also 3-D structures; (4) investigation of TF-DNA interactions under nearly physiological conditions by controlling the reaction conditions experimentally; and most importantly; (5) prototyping of a fully automatic system for potential technology translation. This system permits accurate detection of TF-DNA binding with a rapid response that requires essentially no user intervention for field deployment and data capture.

定量TF-DNA结合，包括位置，分布和结合机制是重要的 这是理解基因调控机制的第一步。在本建议中，我们将制定一项 基于原子力显微镜（AFM）的单分子成像检测方法， TF-DNA结合的定量。这项新技术将数学和统计学的方法引入到 与实验系统的技术突破有关。这项新技术本质上是 不同于经典的单分子成像方法，其仅依赖于技术人员的 实验技能结合数学、统计学、生物工程和化学工程， 该提案通过合并分析学，生物学和生物学，为多学科研究创造了一个完美的平台。 工程.我们认为这是一个翻译的努力，开始作为一个实验室的发现到一个新的 生物技术工具，作为提出的机器学习（ML）方法结合机器人手铺平了道路， 这是一条通往大规模生产和全自动系统的革命性道路，可用于精确的TF-DNA成像。 分析上，我们面临三个挑战：高通量图像的构建，TF结合的预测 区域，并强制分解以恢复绑定机制。为了解决这些问题，我们将（1） 提出了一种新的图像超分辨率平滑样条扩散和退火算法， 强化学习算法用于TFBS的自动搜索;（3）开发了图形方差分析方法 比较TF-DNA结合机制。我们在这些领域的努力应导致（1）基本的 图像超分辨率和强化学习算法的进展， 简单性和理论严谨性;（2）开发和完善技术， 使用AFM技术精确的全基因组鉴定和定量TF-DNA结合位点;（3） 不仅显示TF-DNA结合序列和位置，而且显示三维结构;（4）调查 通过控制反应条件， 实验;最重要的是;（5）为潜在技术开发全自动系统的原型 翻译.该系统允许准确检测TF-DNA结合，具有快速响应， 现场部署和数据采集基本上无需用户干预。