MUFOLD: A new solution for protein 3D structure prediction.
MUFOLD: A new solution for protein 3D structure prediction.
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DOI：
10.1002/prot.22634
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发表时间：
2010-04
影响因子：
2.9
通讯作者：
Xu, Dong
中科院分区：
文献类型：
--
作者：
Zhang, Jingfen;Wang, Qingguo;Barz, Bogdan;He, Zhiquan;Kosztin, Ioan;Shang, Yi;Xu, Dong
关键词：
在过去的20年里，蛋白质结构预测取得了稳步的进步。然而，目前的方法仍然远远不能一致地预测结构模型，准确地与普通用户可访问的计算能力。为了实现更准确和有效的结构预测，我们开发了一些新的方法，并将它们集成到一个软件包，MUFOLD。首先，开发了一个系统的协议，以确定有用的模板和片段从蛋白质数据库为给定的目标蛋白。然后，一个有效的过程被应用于迭代的粗粒度模型生成和评估在Cα或骨干水平。在这个过程中，我们构建模型使用interresidue空间限制来自多维缩放的路线，通过聚类和静态评分功能，评估和选择模型，并通过整合空间限制和以前的模型迭代改进选定的模型。最后，全原子模型进行了评估，使用分子动力学模拟的基础上模拟加热下的结构变化。我们通过使用来自Astral数据库的200种蛋白质的基准不断改进MUFOLD的性能，其中不包括与任何靶蛋白具有>25%序列同一性的模板。最佳模型与天然结构的平均均方根偏差为4.28 μ m，这表明我们以前的方法有了显着和系统的改进。MUFOLD的计算时间比许多其他工具（如Rosetta）短得多。MUFOLD在2008年的蛋白质结构预测CASP8社区实验中取得了一些成功。
There have been steady improvements in protein structure prediction during the past 2 decades. However, current methods are still far from consistently predicting structural models accurately with computing power accessible to common users. Toward achieving more accurate and efficient structure prediction, we developed a number of novel methods and integrated them into a software package, MUFOLD. First, a systematic protocol was developed to identify useful templates and fragments from Protein Data Bank for a given target protein. Then, an efficient process was applied for iterative coarse-grain model generation and evaluation at the Cα or backbone level. In this process, we construct models using interresidue spatial restraints derived from alignments by multidimensional scaling, evaluate and select models through clustering and static scoring functions, and iteratively improve the selected models by integrating spatial restraints and previous models. Finally, the full-atom models were evaluated using molecular dynamics simulations based on structural changes under simulated heating. We have continuously improved the performance of MUFOLD by using a benchmark of 200 proteins from the Astral database, where no template with >25% sequence identity to any target protein is included. The average root-mean-square deviation of the best models from the native structures is 4.28 Å, which shows significant and systematic improvement over our previous methods. The computing time of MUFOLD is much shorter than many other tools, such as Rosetta. MUFOLD demonstrated some success in the 2008 community-wide experiment for protein structure prediction CASP8.
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影响因子：
2.9
作者：
Bondugula, Rajkumar;Xu, Dong
通讯作者：
Xu, Dong
影响因子：
5.6
作者：
Samudrala, R;Moult, J
通讯作者：
Moult, J
影响因子：
5.8
作者：
Inbar, Yuval;Benyamini, Hadar;Wolfson, Haim J.
通讯作者：
Wolfson, Haim J.
影响因子：
14.9
作者：
Bairoch, Amos;Bougueleret, Lydie;Zhang, Jian
通讯作者：
Zhang, Jian
DOI：
10.1073/pnas.84.19.6611
发表时间：
1987-10-01
影响因子：
11.1
作者：
LI, ZQ;SCHERAGA, HA
通讯作者：
SCHERAGA, HA