Center for Condensed Matter Theory, Department of Physics, Indian Institute of Science, Bangalore 560012, India.
J Phys Chem A. 2020 Sep 24;124(38):7658-7664. doi: 10.1021/acs.jpca.0c04368. Epub 2020 Sep 14.
Charge transport in deoxyribonucleic acid (DNA) is of immense interest in biology and molecular electronics. Electronic coupling between the DNA bases is an important parameter describing the efficiency of charge transport in DNA. A reasonable estimation of this electronic coupling requires many expensive first principle calculations. In this article, we present a machine learning (ML) based model to calculate the electronic coupling between the guanine bases of the DNA (in the same strand) of any length, thus avoiding expensive first-principle calculations. The electronic coupling between the bases are evaluated using density functional theory (DFT) calculations with the morphologies derived from fully atomistic molecular dynamics (MD) simulations. A new and simple protocol based on the coarse-grained model of the DNA has been used to extract the feature vectors for the DNA bases. A deep neural network (NN) is trained with the feature vector as input and the DFT-calculated electronic coupling as output. Once well trained, the NN can predict the DFT-calculated electronic coupling of new structures with a mean absolute error (MAE) of 0.02 eV.
脱氧核糖核酸 (DNA) 中的电荷输运在生物学和分子电子学中具有重要意义。DNA 碱基之间的电子耦合是描述 DNA 中电荷输运效率的一个重要参数。合理估计这种电子耦合需要进行许多昂贵的第一性原理计算。在本文中，我们提出了一种基于机器学习 (ML) 的模型，用于计算任何长度的 DNA（同一条链上）中鸟嘌呤碱基之间的电子耦合，从而避免昂贵的第一性原理计算。使用密度泛函理论 (DFT) 计算结合从全原子分子动力学 (MD) 模拟中得出的形态学来评估碱基之间的电子耦合。我们使用基于 DNA 粗粒度模型的新的简单协议来提取 DNA 碱基的特征向量。将深度神经网络 (NN) 用特征向量作为输入，DFT 计算出的电子耦合作为输出进行训练。一旦训练良好，NN 就可以用均方根误差 (MAE) 为 0.02 eV 的新结构预测 DFT 计算出的电子耦合。
