POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins
POTENCI: prediction of temperature, neighbor and pH-corrected chemical shifts for intrinsically disordered proteins
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DOI：
10.1007/s10858-018-0166-5
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发表时间：
2018-03-01
影响因子：
2.7
通讯作者：
Mulder, Frans A. A.
中科院分区：
文献类型：
--
作者：
Nielsen, Jakob Toudahl;Mulder, Frans A. A.
化学位移包含关于蛋白质结构和动力学的重要位点特异性信息。从统计平均值的偏差，被称为随机线圈化学位移（RCCS），被广泛用于推断这些关系。不幸的是，使用不精确的参考RCCS会导致有偏见的推断，并阻碍了微妙的结构特征的检测。在这里，我们提出了一种新的方法，POTENCI，RCCS的预测，优于目前最权威的方法。POTENCI使用经过验证的无序蛋白质片段的化学位移的大型策划数据库进行参数化;它明确考虑了pH和温度，并包括序列依赖的最近和次最近邻校正以及二阶校正。使用POTENCI的RCCS预测显示均方根值降低了25- 78%，其中H-1 α和C-13 '的改善最大。它演示了如何POTENCI可以应用于分析从RCCS的细微偏差，以检测小种群的残留结构在本质上是不可辨别的蛋白质之前。POTENCI源代码可供下载，也可从URL http：//www. protein-nmr. org部署。
Chemical shifts contain important site-specific information on the structure and dynamics of proteins. Deviations from statistical average values, known as random coil chemical shifts (RCCSs), are extensively used to infer these relationships. Unfortunately, the use of imprecise reference RCCSs leads to biased inference and obstructs the detection of subtle structural features. Here we present a new method, POTENCI, for the prediction of RCCSs that outperforms the currently most authoritative methods. POTENCI is parametrized using a large curated database of chemical shifts for protein segments with validated disorder; It takes pH and temperature explicitly into account, and includes sequence-dependent nearest and next-nearest neighbor corrections as well as second-order corrections. RCCS predictions with POTENCI show root-mean-square values that are lower by 25-78%, with the largest improvements observed for H-1 alpha and C-13'. It is demonstrated how POTENCI can be applied to analyze subtle deviations from RCCSs to detect small populations of residual structure in intrinsically disorder proteins that were not discernible before. POTENCI source code is available for download, or can be deployed from the URLhttp://www.protein-nmr.org.