Despite advances in sampling and scoring strategies, Monte Carlo modeling methods still struggle to accurately predict de novo the structures of large proteins, membrane proteins, or proteins of complex topologies. Previous approaches have addressed these shortcomings by leveraging sparse distance data gathered using site-directed spin labeling and electron paramagnetic resonance spectroscopy to improve protein structure prediction and refinement outcomes. However, existing computational implementations entail compromises between coarse-grained models of the spin label that lower the resolution and explicit models that lead to resource-intense simulations. These methods are further limited by their reliance on distance distributions, which are calculated from a primary refocused echo decay signal and contain uncertainties that may require manual refinement. Here, we addressed these challenges by developing RosettaDEER, a scoring method within the Rosetta software suite capable of simulating double electron-electron resonance spectroscopy decay traces and distance distributions between spin labels fast enough to fold proteins de novo. We demonstrate that the accuracy of resulting distance distributions match or exceed those generated by more computationally intensive methods. Moreover, decay traces generated from these distributions recapitulate intermolecular background coupling parameters even when the time window of data collection is truncated. As a result, RosettaDEER can discriminate between poorly folded and native-like models by using decay traces that cannot be accurately converted into distance distributions using regularized fitting approaches. Finally, using two challenging test cases, we demonstrate that RosettaDEER leverages these experimental data for protein fold prediction more effectively than previous methods. These benchmarking results confirm that RosettaDEER can effectively leverage sparse experimental data for a wide array of modeling applications built into the Rosetta software suite.

中文翻译：

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快速模拟未处理的 DEER 衰变数据以预测蛋白质折叠


尽管采样和评分策略取得了进步，蒙特卡罗建模方法仍然难以从头准确预测大蛋白质、膜蛋白质或复杂拓扑蛋白质的结构。以前的方法已经通过利用定点自旋标记和电子顺磁共振波谱收集的稀疏距离数据来解决这些缺点，以改善蛋白质结构预测和细化结果。然而，现有的计算实现需要在降低分辨率的自旋标签的粗粒度模型和导致资源密集型模拟的显式模型之间进行折衷。这些方法还因其对距离分布的依赖而受到限制，距离分布是根据主要重新聚焦的回声衰减信号计算出来的，并且包含可能需要手动细化的不确定性。在这里，我们通过开发 RosettaDEER 来解决这些挑战，RosettaDEER 是 Rosetta 软件套件中的一种评分方法，能够模拟双电子-电子共振光谱衰变痕迹和自旋标签之间的距离分布，速度足以快速折叠蛋白质。我们证明了所得到的距离分布的准确性匹配或超过了计算密集型方法生成的距离分布的准确性。此外，即使数据收集的时间窗口被截断，这些分布生成的衰变痕迹也能概括分子间背景耦合参数。因此，RosettaDEER 可以通过使用无法使用正则化拟合方法准确转换为距离分布的衰减轨迹来区分折叠不良的模型和类原生模型。 最后，使用两个具有挑战性的测试案例，我们证明 RosettaDEER 比以前的方法更有效地利用这些实验数据进行蛋白质折叠预测。这些基准测试结果证实，RosettaDEER 可以有效地利用稀疏实验数据来构建 Rosetta 软件套件中内置的各种建模应用程序。