De Silva Namindu, Perez Alberto
Department of Chemistry, Quantum Theory Project, University of Florida, Gainesville, FL 32611, United States.
Nucleic Acids Res. 2025 Jul 8;53(13). doi: 10.1093/nar/gkaf601.
DNA exhibits local conformational preferences that affect its ability to adopt biologically relevant conformations, such as those required for binding proteins. Traditional methods, like Markov state models and molecular dynamics (MD) simulations, have advanced our understanding but often struggle to capture these rare conformational states due to high computational demands. Here, we introduce a novel AI framework based on dynamical graphical models (DGMs), a generative machine learning approach trained on equilibrium MD data, to predict DNA conformational transitions that are never seen in the MD ensembles. By leveraging local DNA interactions, DGMs generate a comprehensive transition matrix that captures both thermodynamic and kinetic properties of unsampled states, enabling accurate predictions of rare global conformations without the need for extensive sampling. Applying this model to the B→A transition, we demonstrate that DGMs can efficiently predict sequence-dependent A-DNA preferences, achieving results that align closely with replica exchange umbrella sampling simulations. DGMs provide new insights into DNA sequence-structure relationships, paving the way for applications in DNA sequence design and optimization.
DNA表现出局部构象偏好，这会影响其形成生物学相关构象的能力，例如与结合蛋白所需的构象。传统方法，如马尔可夫状态模型和分子动力学（MD）模拟，增进了我们的理解，但由于计算需求高，往往难以捕捉这些罕见的构象状态。在这里，我们引入了一种基于动态图形模型（DGM）的新型人工智能框架，这是一种在平衡MD数据上训练的生成式机器学习方法，用于预测MD集合中从未见过的DNA构象转变。通过利用局部DNA相互作用，DGM生成一个全面的转变矩阵，该矩阵捕获未采样状态的热力学和动力学特性，从而能够在无需广泛采样的情况下准确预测罕见的全局构象。将该模型应用于B→A转变，我们证明DGM可以有效地预测序列依赖性A-DNA偏好，获得与副本交换伞形采样模拟密切一致的结果。DGM为DNA序列-结构关系提供了新的见解，为DNA序列设计和优化的应用铺平了道路。
