Department Chemie, Biomolecular NMR and Munich Center for Integrated Protein Science, Technische Universität München, Lichtenbergstrasse 4, 85747, Garching, Germany.
J Biomol NMR. 2013 Oct;57(2):117-27. doi: 10.1007/s10858-013-9772-4. Epub 2013 Aug 22.
A new fragment picker has been developed for CS-Rosetta that combines beneficial features of the original fragment picker, MFR, used with CS-Rosetta, and the fragment picker, NNMake, that was used for purely sequence based fragment selection in the context of ROSETTA de-novo structure prediction. Additionally, the new fragment picker has reduced sensitivity to outliers and other difficult to match data points rendering the protocol more robust and less likely to introduce bias towards wrong conformations in cases where data is bad, missing or inconclusive. The fragment picker protocol gives significant improvements on 6 of 23 CS-Rosetta targets. An independent benchmark on 39 protein targets, whose NMR data sets were published only after protocol optimization had been finished, also show significantly improved performance for the new fragment picker (van der Schot et al. in J Biomol NMR, 2013).
开发了一种新的碎片选择器，用于 CS-Rosetta，它结合了 CS-Rosetta 中使用的原始碎片选择器 MFR 的有益特性，以及用于 ROSETTA 从头结构预测中基于序列的纯片段选择的碎片选择器 NNMake。此外，新的碎片选择器降低了对异常值和其他难以匹配数据点的敏感性，从而使该方案更具鲁棒性，并且在数据质量差、缺失或不确定的情况下，不太可能引入对错误构象的偏差。该碎片选择器方案在 23 个 CS-Rosetta 目标中的 6 个上有显著的改进。在 39 个蛋白质目标上进行的独立基准测试，其 NMR 数据集是在方案优化完成后才公布的，也显示出新的碎片选择器的性能有显著提高（van der Schot 等人，2013 年，J Biomol NMR）。
