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

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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.