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Solution- and Adsorbed-State Structural Ensembles Predicted for the Statherin-Hydroxyapatite System

机译:Statherin-羟基磷灰石体系的固溶态和吸附态结构体预测

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摘要

We have developed a multiscale structure prediction technique to study solution- and adsorbed-state ensembles of biomineralization proteins. The algorithm employs a Metropolis Monte Carlo-plus-minimization strategy that varies all torsional and rigid-body protein degrees of freedom. We applied the technique to fold statherin, starting from a fully extended peptide chain in solution, in the presence of hydroxyapatite (HAp) (001), (010), and (100) monoclinic crystals. Blind (unbiased) predictions capture experimentally observed macroscopic and high-resolution structural features and show minimal statherin structural change upon adsorption. The dominant structural difference between solution and adsorbed states is an experimentally observed folding event in statherin's helical binding domain. Whereas predicted statherin conformers vary slightly at three different HAp crystal faces, geometric and chemical similarities of the surfaces allow structurally promiscuous binding. Finally, we compare blind predictions with those obtained from simulation biased to satisfy all previously published solid-state NMR (ssNMR) distance and angle measurements (acquired from HAp-adsorbed statherin). Atomic clashes in these structures suggest a plausible, alternative interpretation of some ssNMR measurements as intermolecular rather than intramolecular. This work demonstrates that a combination of ssNMR and structure prediction could effectively determine high-resolution protein structures at biomineral interfaces.
机译:我们已经开发了一种多尺度结构预测技术,以研究生物矿化蛋白的溶液态和吸附态集合。该算法采用都会蒙特卡洛加最小化策略,该策略会改变所有扭转和刚体蛋白质的自由度。我们在羟基磷灰石(HAp)(001),(010)和(100)单斜晶体存在的情况下,从溶液中完全延伸的肽链开始,应用了该技术来折叠抗皱素。盲目(无偏)预测捕获了实验观察到的宏观和高分辨率结构特征,并显示了吸附后最小的斯台汀结构变化。溶液状态和吸附状态之间的主要结构差异是在实验中观察到的在史达汀的螺旋结合域中的折叠事件。尽管在三个不同的HAp晶面上预测的statherin构象异构体略有不同,但表面的几何和化学相似性允许结构混杂结合。最后,我们将盲预测与从模拟偏差中获得的预测进行比较,以满足所有先前发布的固态NMR(ssNMR)距离和角度测量值(从HAp吸附的Statherin获得)。这些结构中的原子碰撞表明,某些ssNMR测量结果可能是分子间而非分子内的合理解释。这项工作表明,ssNMR和结构预测的结合可以有效地确定生物矿物界面处的高分辨率蛋白质结构。

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