Zhang Yi, Yang Chenxi, Xiong Yiduo, Xiao Yi
Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Institute of Biophysics, School of Physics, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Biophys J. 2024 Sep 3;123(17):2696-2704. doi: 10.1016/j.bpj.2024.02.018. Epub 2024 Feb 26.
DNA molecules are vital macromolecules that play a fundamental role in many cellular processes and have broad applications in medicine. For example, DNA aptamers have been rapidly developed for diagnosis, biosensors, and clinical therapy. Recently, we proposed a computational method of predicting DNA 3D structures, called 3dDNA. However, it lacks a scoring function to evaluate the predicted DNA 3D structures, and so they are not ranked for users. Here, we report a scoring function, 3dDNAscoreA, for evaluation of DNA 3D structures based on a deep learning model ARES for RNA 3D structure evaluation but using a new strategy for training. 3dDNAscoreA is benchmarked on two test sets to show its ability to rank DNA 3D structures and select the native and near-native structures.
DNA分子是至关重要的大分子,在许多细胞过程中发挥着基础性作用,并且在医学领域有广泛应用。例如,DNA适体已被迅速开发用于诊断、生物传感器和临床治疗。最近,我们提出了一种预测DNA三维结构的计算方法,称为3dDNA。然而,它缺乏一个评分函数来评估预测的DNA三维结构,因此无法为用户对这些结构进行排名。在此,我们报告一种评分函数3dDNAscoreA,用于基于深度学习模型ARES评估DNA三维结构,但采用了一种新的训练策略。3dDNAscoreA在两个测试集上进行了基准测试,以展示其对DNA三维结构进行排名以及选择天然和近天然结构的能力。