A single experimental method alone often fails to provide the resolution, accuracy, and coverage needed to model integral membrane proteins (IMPs). Integrating computation with experimental data is a powerful approach to supplement missing structural information with atomic detail. We combine RosettaNMR with experimentally-derived paramagnetic NMR restraints to guide membrane protein structure prediction. We demonstrate this approach using the disulfide bond formation protein B (DsbB), an α-helical IMP. Here, we attached a cyclen-based paramagnetic lanthanide tag to an engineered non-canonical amino acid (ncAA) using a copper-catalyzed azide-alkyne cycloaddition (CuAAC) click chemistry reaction. Using this tagging strategy, we collected 203 backbone H^N pseudocontact shifts (PCSs) for three different labeling sites and used these as input to guide de novo membrane protein structure prediction protocols in Rosetta. We find that this sparse PCS dataset combined with 44 long-range NOEs as restraints in our calculations improves structure prediction of DsbB by enhancements in model accuracy, sampling, and scoring. The inclusion of this PCS dataset improved the Cα-RMSD transmembrane segment values of the best-scoring and best-RMSD models from 9.57 Å and 3.06 Å (no NMR data) to 5.73 Å and 2.18 Å, respectively.

单独的实验方法通常无法提供积分膜蛋白 (IMP) 建模所需的分辨率、准确性和覆盖范围。将计算与实验数据相结合是用原子细节补充缺失的结构信息的有效方法。我们将 RosettaNMR 与实验衍生的顺磁 NMR 约束相结合来指导膜蛋白结构预测。我们使用二硫键形成蛋白 B (DsbB)（一种 α 螺旋 IMP）演示了这种方法。在这里，我们使用铜催化的叠氮化物-炔环加成（CuAAC）点击化学反应将基于cyclen的顺磁性镧系元素标签附加到工程化的非规范氨基酸（ncAA）上。使用这种标记策略，我们收集了三个不同标记位点的 203 个主链 H ^N伪接触位移 (PCS)，并将它们用作输入来指导Rosetta 中的从头膜蛋白结构预测方案。我们发现，这个稀疏 PCS 数据集与 44 个远程 NOE 相结合，作为我们计算中的限制，通过增强模型精度、采样和评分来改进 DsbB 的结构预测。包含该 PCS 数据集将最佳评分和最佳 RMSD 模型的 Cα-RMSD 跨膜片段值分别从 9.57 Å 和 3.06 Å（无 NMR 数据）提高到 5.73 Å 和 2.18 Å。