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.

Short Bio: 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.