2025/03/31 by Yang Li, Yang, Li, Dongbo Wang +1
Biochemistry, Genetics and Molecular Biology · #Epigenetics and DNA Methylation #FOS: Biological sciences #Genomics and Chromatin Dynamics #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.2504.00764
openalex publication_date 2025/03/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
How DNA-binding proteins locate specific genomic targets remains a central challenge in molecular biology. Traditional protein-centric approaches, which rely on wet-lab experiments and visualization techniques, often lack genome-wide resolution and fail to capture physiological dynamics in living cells. Here, we introduce a DNA-centric strategy that leverages in vivo N6-methyladenine (6mA) data to decode the logic of protein-DNA recognition. By integrating linguistically inspired modeling with machine learning, we reveal two distinct search modes: a protein-driven diffusion mechanism and a DNA sequence-driven mechanism, wherein specific motifs function as protein traps. We further reconstruct high-resolution interaction landscapes at the level of individual sequences and trace the evolutionary trajectories of recognition motifs across species. This framework addresses fundamental limitations of protein-centered approaches and positions DNA itself as an intrinsic reporter of protein-binding behavior.