NEAT-DNA: A Chemically Accurate, Sequence-Dependent Coarse-Grained Model for Large-Scale DNA Simulations

Abstract

DNAs physical properties play a central role in genome organization and regulation, but simulating its behavior across biologically relevant scales remains a major computational challenge. Coarse grained DNA models have enabled faster simulations, yet they often sacrifice chemical accuracy or produce unphysical conformations, limiting their utility for studying genome structure. A key difficulty has been constructing a model that is both efficient enough for large-scale simulations and faithful to the molecular mechanics of DNA. Here we introduce NEAT-DNA, a new coarse-grained DNA model that resolves longstanding limitations in physical realism and parameter optimization. By combining a physically principled energy formulation with a unified training framework that integrates data from both atomistic simulations and experiments, NEAT-DNA accurately reproduces sequence-dependent structure and flexibility while remaining computationally efficient. This approach marks a significant advance over previous models, which either lacked sequence specificity or introduced distortions inconsistent with experimental observations. NEAT-DNA bridges this gap, offering a high-fidelity yet tractable representation of DNA suitable for exploring chromatin folding. More broadly, it provides a foundation for large-scale simulations that couple molecular detail with gene-level chromatin organization, opening new avenues for predictive modeling in structural genomics.