Accurate protein structure prediction with hydroxyl radical protein footprinting data.
Accurate protein structure prediction with hydroxyl radical protein footprinting data.
复制标题
用羟基自由基蛋白质足迹数据精确预测蛋白质结构。
DOI：
10.1038/s41467-020-20549-7
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发表时间：
2021-01-12
影响因子：
16.6
通讯作者：
Lindert S
中科院分区：
文献类型：
--
作者：
Biehn SE;Lindert S
羟基自由基蛋白足迹（HRPF）结合质谱法揭示了蛋白质中标记残基的相对溶剂暴露，从而提供了对蛋白质三级结构的洞察。HRPF标记了19种具有不同程度可靠性和反应性的残留物。在这里，我们提出了一个动态驱动的hrpf引导算法用于蛋白质结构预测。在我们算法的基准测试中，在得分项中使用动态数据导致得分最低的从头算模型的均方根偏差显著改善，并改善了所有基准蛋白质的漏斗状度量Pnear。我们确定了四种基准蛋白中有三种具有准确原子细节的模型。这项工作表明，HRPF数据以及Rosetta移动集合采样的侧链动力学可以用来准确预测蛋白质结构。基于质谱的共价标记技术，如羟基自由基蛋白足迹（HRPF）提供了有关蛋白质三级结构的信息。在这里，作者提出了一种动态驱动的HRPF引导算法，用于蛋白质结构预测，该算法集成在Rosetta软件套件中，只需要蛋白质序列和HRPF数据作为输入，并演示了其成功应用于四种基准蛋白质。
Hydroxyl radical protein footprinting (HRPF) in combination with mass spectrometry reveals the relative solvent exposure of labeled residues within a protein, thereby providing insight into protein tertiary structure. HRPF labels nineteen residues with varying degrees of reliability and reactivity. Here, we are presenting a dynamics-driven HRPF-guided algorithm for protein structure prediction. In a benchmark test of our algorithm, usage of the dynamics data in a score term resulted in notable improvement of the root-mean-square deviations of the lowest-scoring ab initio models and improved the funnel-like metric Pnear for all benchmark proteins. We identified models with accurate atomic detail for three of the four benchmark proteins. This work suggests that HRPF data along with side chain dynamics sampled by a Rosetta mover ensemble can be used to accurately predict protein structure. Mass spectrometry-based covalent labeling techniques such as hydroxyl radical protein footprinting (HRPF) provide information about protein tertiary structures. Here, the authors present a dynamics driven HRPF-guided algorithm for protein structure prediction that is incorporated in the Rosetta software suite and only requires the protein sequence and HRPF data as input and they demonstrate its successful application to four benchmark proteins.
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影响因子：
16.6
作者：
Manzi, Lucio;Barrow, Andrew S.;Scott, Daniel;Layfield, Robert;Wright, Timothy G.;Moses, John E.;Oldham, Neil J.
通讯作者：
Oldham, Neil J.
影响因子：
64.8
作者：
Bhardwaj, Gaurav;Mulligan, Vikram Khipple;Bahl, Christopher D.;Gilmore, Jason M.;Harvey, Peta J.;Cheneval, Olivier;Buchko, Garry W.;Pulavarti, Surya V. S. R. K.;Kaas, Quentin;Eletsky, Alexander;Huang, Po-Ssu;Johnsen, William A.;Greisen, Per Jr;Rocklin, Gabriel J.;Song, Yifan;Linsky, Thomas W.;Watkins, Andrew;Rettie, Stephen A.;Xu, Xianzhong;Carter, Lauren P.;Bonneau, Richard;Olson, James M.;Coutsias, Evangelos;Correnti, Colin E.;Szyperski, Thomas;Craik, David J.;Baker, David
通讯作者：
Baker, David
影响因子：
62.1
作者：
Liu XR;Zhang MM;Gross ML
通讯作者：
Gross ML
影响因子：
5.6
作者：
Leelananda, Sumudu P.;Lindert, Steffen
通讯作者：
Lindert, Steffen
影响因子：
3.7
作者：
Kahraman A;Herzog F;Leitner A;Rosenberger G;Aebersold R;Malmström L
通讯作者：
Malmström L