3DRobot: automated generation of diverse and well-packed protein structure decoys

3DRobot: automated generation of diverse and well-packed protein structure decoys
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3DRobot：自动生成多样化且包装良好的蛋白质结构诱饵

DOI：
10.1093/bioinformatics/btv601
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发表时间：
2016-02-01
期刊：
BIOINFORMATICS
影响因子：
5.8
通讯作者：
Zhang, Yang
Zhang, Yang
中科院分区：
生物学3区
文献类型：
--
作者：
Deng, Haiyou;Jia, Ya;Zhang, Yang

文献摘要

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动机 通过计算产生的非天然蛋白质结构构象(或诱饵)经常被用来设计蛋白质折叠模拟方法和力场。然而，目前文献中使用的几乎所有诱饵集都存在均方根偏差(RMSD)不均匀的分布，偏向于非蛋白质，如氢键和致密模式。同时，大多数蛋白质诱饵集都是预先计算的，缺乏为任何目标蛋白质自动生成高质量诱饵的方法。 结果 我们开发了一种新的算法3DRobot，通过增强氢键和致密性相互作用的自由片段组装来创建蛋白质结构诱饵。该方法以从头算折叠和比较建模模拟中广泛使用的三个诱饵集为基准。由3DRobot生成的诱饵在RMSD空间中连续分布，显著提高了多样性和均衡性。3DRobot中引入的新能量项改善了诱饵的氢键网络和紧凑性，消除了利用平凡势能识别天然结构的可能性。能够从任何蛋白质结构中自动产生如此多样且排列良好的非天然构象的算法应该会对高级蛋白质力场和折叠模拟方法的发展产生广泛的影响。可用性和实施：http://zhanglab.ccmb.med.umich.edu/3DRobot/ 接触 邮箱：jiay@Phy.ccnu.edu.cn；zhng@umich.edu 补充信息 补充数据可在生物信息学在线上获得。
MOTIVATION Computationally generated non-native protein structure conformations (or decoys) are often used for designing protein folding simulation methods and force fields. However, almost all the decoy sets currently used in literature suffer from uneven root mean square deviation (RMSD) distribution with bias to non-protein like hydrogen-bonding and compactness patterns. Meanwhile, most protein decoy sets are pre-calculated and there is a lack of methods for automated generation of high-quality decoys for any target proteins. RESULTS We developed a new algorithm, 3DRobot, to create protein structure decoys by free fragment assembly with enhanced hydrogen-bonding and compactness interactions. The method was benchmarked with three widely used decoy sets from ab initio folding and comparative modeling simulations. The decoys generated by 3DRobot are shown to have significantly enhanced diversity and evenness with a continuous distribution in the RMSD space. The new energy terms introduced in 3DRobot improve the hydrogen-bonding network and compactness of decoys, which eliminates the possibility of native structure recognition by trivial potentials. Algorithms that can automatically create such diverse and well-packed non-native conformations from any protein structure should have a broad impact on the development of advanced protein force field and folding simulation methods. AVAILIABLITY AND IMPLEMENTATION: http://zhanglab.ccmb.med.umich.edu/3DRobot/ CONTACT jiay@phy.ccnu.edu.cn; zhng@umich.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.