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.

量化 TF-DNA 结合（包括位置、分布和结合机制）是一项重要的工作 理解基因调控机制的第一步。在本提案中，我们将开发一种基因调控机制。 基于原子力显微镜（AFM）的单分子成像方法用于检测和 新技术将数学和统计学方法引入到 TF-DNA 结合的定量分析中。 这项新技术本质上是在一个实验系统上进行技术突破。 与传统的单分子成像方法不同，传统的单分子成像方法仅依赖于技术人员的 结合数学、统计学、生物工程和化学工程，这 该提案通过融合分析学、生物学和 我们认为这是从实验室发现到新发现的转化努力。 生物技术工具，作为所提出的机器学习（ML）方法与机器人手相结合铺平了道路 实现精确 TF-DNA 成像的大规模生产和全自动系统的革命性途径。 从分析上来说，我们面临三个挑战：高通量图像的构建、TF结合的预测 为了解决这些问题，我们将（1）。 开发用于图像超分辨率的平滑样条扩散和退火过程，（2）开发新颖的 用于自动 TFBS 搜索的强化学习算法，以及（3）开发图方差分析方法 比较 TF-DNA 结合机制，我们在这些领域的努力应该导致 (1) 根本性的结果。 图像超分辨率和强化学习算法的进步，这两种算法都享有盛誉 (2) 技术的发展和完善，以实现快速、准确的应用。 (3)利用AFM技术对TF-DNA结合位点进行全基因组精确鉴定和定量； 不仅可以可视化 TF-DNA 结合序列和位置，还可以可视化 3-D 结构；(4) 研究 通过控制反应条件在接近生理条件下的 TF-DNA 相互作用 (5)潜在技术的全自动系统原型制作 该系统可以准确检测 TF-DNA 结合，并做出快速响应。 现场部署和数据捕获基本上无需用户干预。