NC State CS AI Seminar Series

Date: 10/03/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: EB II, Conference Room 3211
Zoom: https://ncsu.zoom.us/j/91683735738

Title: Synergizing Sparse Sequence, Experimental, and AI-Predicted Structures for Protein-Nucleic Acid Interaction Predictions

Speaker: Xingcheng Lin (Physics Department at NC State University)

Abstract: Sequence-specific nucleic acid recognition underlies essential processes in gene regulation, yet experiment-independent methods for simultaneous predictions of genomic DNA recognition sites and their binding affinity remain limited. Our group developed data-driven methods and simulation tools to predict and elucidate protein-nucleic acid interactions and their contributions in reshaping chromatin structures. Specifically, we introduce the Interpretable protein-DNA Energy Associative (IDEA) model, an interpretable residue-level biophysical model capable of predicting binding sites and affinities of DNA-binding proteins without relying on experimental binding data. By integrating the structures and sequences of known protein-DNA complexes into an optimized energy model, IDEA enables a direct interpretation of the physicochemical interactions among individual amino acids and nucleotides. Using transcription factors as examples, we demonstrate that IDEA accurately predicts genomic DNA recognition sites and their binding strengths. Additionally, IDEA is incorporated into a coarse-grained simulation framework that quantitatively captures the absolute protein-DNA binding free energies. Collectively, IDEA provides an integrated computational platform that alleviates experimental costs and biases in the assessment of DNA recognition and can be utilized for mechanistic studies of various DNA recognition processes. Finally, I will present our recent progress in extending this framework to predict protein-single-stranded nucleic acid interactions and to design therapeutic aptamers.

Biography: Xingcheng Lin is an assistant professor in the Physics Department at North Carolina State University, starting in August 2023. He is also affiliated with the Bioinformatics Cluster of the Chancellor’s Faculty Excellence Program. Dr. Lin earned his Ph.D. in Biological Physics from the Center for Theoretical Biological Physics and the Physics Department at Rice University. During his graduate studies, he employed both atomistic and coarse-grained simulations to investigate the molecular mechanisms behind the invasion of influenza viruses. He also developed simulation-based tools to refine folded protein structures and to simulate intrinsically disordered proteins. Following his doctorate, Dr. Lin conducted postdoctoral research in the Chemistry Department at the Massachusetts Institute of Technology (MIT), where he broadened his research focus to include the chromatin system. The Lin group focuses on integrating top-down data-driven approaches with bottom-up biophysical simulations to predict protein-nucleic acid interactions and understand their implications for genome regulation.

Date: 9/5/2025, Friday
Time: 11:00 AM - 12:00 PM (EST)
Location: EB II, Conference Room 3211
Zoom: https://ncsu.zoom.us/j/91683735738

Title: Toward Real-Time Ultrasound Computed Tomography: Bridging Wave Physics and Data-Driven Learning

Speaker: Youzuo Lin (University of North Carolina at Chapel Hill & Los Alamos National Laboratory)

Abstract: Ultrasound Computed Tomography (USCT), also known as Full Waveform Inversion (FWI), reconstructs the mechanical properties of biological tissues by modeling the full propagation of ultrasound waves. This modality shows great promise for advanced applications such as breast, neuro, and prostate imaging, yet its clinical adoption has been limited by the trade-off between accuracy and computational efficiency. Physics-based reconstruction methods achieve high-resolution, quantitative maps of tissue properties but are computationally demanding and sensitive to model uncertainties. Data-driven approaches, particularly deep learning, have recently offered accelerated solutions but often lack robustness and generalizability. In this work, we present hybrid USCT strategies that bridge wave physics and machine learning. By embedding physical principles into self-supervised learning frameworks, our methods substantially reduce computational cost while maintaining reconstruction fidelity. We demonstrate their efficacy in challenging prostate imaging scenarios, highlighting their potential to advance USCT toward real-time clinical translation.

Biography: Youzuo Lin is an Associate Professor in the School of Data Science and Society at the University of North Carolina at Chapel Hill. Previously, he served as a Senior Scientist at Los Alamos National Laboratory. He earned his Ph.D. in Applied and Computational Mathematics from Arizona State University in 2010. Youzuo’s research focuses on scientific machine learning methods and their applications, particularly in computational wave imaging, ultrasound tomography, geophysical inversion, and UAV image analysis. He has published over 100 articles in leading journals and conference proceedings and is a co-inventor on several U.S. patents related to ultrasound imaging techniques.