Alnabati Eman, Esquivel-Rodriguez Juan, Terashi Genki, Kihara Daisuke
Department of Computer Science, Purdue University, West Lafayette, IN, United States.
Computer Engineering School, Costa Rica Institute of Technology, Cartago, Costa Rica.
Front Mol Biosci. 2022 Jul 25;9:935411. doi: 10.3389/fmolb.2022.935411. eCollection 2022.
An increasing number of protein complex structures are determined by cryo-electron microscopy (cryo-EM). When individual protein structures have been determined and are available, an important task in structure modeling is to fit the individual structures into the density map. Here, we designed a method that fits the atomic structures of proteins in cryo-EM maps of medium to low resolutions using Markov random fields, which allows probabilistic evaluation of fitted models. The accuracy of our method, MarkovFit, performed better than existing methods on datasets of 31 simulated cryo-EM maps of resolution 10 , nine experimentally determined cryo-EM maps of resolution less than 4 , and 28 experimentally determined cryo-EM maps of resolution 6 to 20 .
越来越多的蛋白质复合物结构是通过冷冻电子显微镜（cryo-EM）确定的。当单个蛋白质结构已被确定并可用时，结构建模中的一项重要任务是将单个结构拟合到密度图中。在此，我们设计了一种方法，该方法使用马尔可夫随机场将蛋白质的原子结构拟合到中低分辨率的冷冻电子显微镜图中，这允许对拟合模型进行概率评估。我们的方法MarkovFit在31个分辨率为10 的模拟冷冻电子显微镜图数据集、9个分辨率小于4 的实验确定的冷冻电子显微镜图数据集以及28个分辨率为6至20 的实验确定的冷冻电子显微镜图数据集上，其准确性优于现有方法。
