Seffernick Justin T, Harvey Sophie R, Wysocki Vicki H, Lindert Steffen
Department of Chemistry and Biochemistry and Resource for Native Mass Spectrometry Guided Structural Biology, Ohio State University, Columbus, Ohio 43210, United States.
ACS Cent Sci. 2019 Aug 28;5(8):1330-1341. doi: 10.1021/acscentsci.8b00912. Epub 2019 Jul 2.
Recently, mass spectrometry (MS) has become a viable method for elucidation of protein structure. Surface-induced dissociation (SID), colliding multiply charged protein complexes or other ions with a surface, has been paired with native MS to provide useful structural information such as connectivity and topology for many different protein complexes. We recently showed that SID gives information not only on connectivity and topology but also on relative interface strengths. However, SID has not yet been coupled with computational structure prediction methods that could use the sparse information from SID to improve the prediction of quaternary structures, i.e., how protein subunits interact with each other to form complexes. Protein-protein docking, a computational method to predict the quaternary structure of protein complexes, can be used in combination with subunit structures from X-ray crystallography and NMR in situations where it is difficult to obtain an experimental structure of an entire complex. While structure prediction can be successful, many studies have shown that inclusion of experimental data can greatly increase prediction accuracy. In this study, we show that the appearance energy (AE, defined as 10% fragmentation) extracted from SID can be used in combination with Rosetta to successfully evaluate protein-protein docking poses. We developed an improved model to predict measured SID AEs and incorporated this model into a scoring function that combines the RosettaDock scoring function with a novel SID scoring term, which quantifies agreement between experiments and structures generated from RosettaDock. As a proof of principle, we tested the effectiveness of these restraints on 57 systems using ideal SID AE data (AE determined from crystal structures using the predictive model). When theoretical AEs were used, the RMSD of the selected structure improved or stayed the same in 95% of cases. When experimental SID data were incorporated on a different set of systems, the method predicted near-native structures (less than 2 Å root-mean-square deviation, RMSD, from native) for 6/9 tested cases, while unrestrained RosettaDock (without SID data) only predicted 3/9 such cases. Score versus RMSD funnel profiles were also improved when SID data were included. Additionally, we developed a confidence measure to evaluate predicted model quality in the absence of a crystal structure.
最近，质谱法（MS）已成为阐明蛋白质结构的一种可行方法。表面诱导解离（SID），即将多重带电的蛋白质复合物或其他离子与表面碰撞，已与天然质谱联用，为许多不同的蛋白质复合物提供了有用的结构信息，如连接性和拓扑结构。我们最近表明，SID不仅能提供关于连接性和拓扑结构的信息，还能提供关于相对界面强度的信息。然而，SID尚未与计算结构预测方法相结合，而这些方法可以利用来自SID的稀疏信息来改进四级结构的预测，即蛋白质亚基如何相互作用形成复合物。蛋白质-蛋白质对接是一种预测蛋白质复合物四级结构的计算方法，在难以获得整个复合物实验结构的情况下，可与X射线晶体学和核磁共振得到的亚基结构结合使用。虽然结构预测可能会成功，但许多研究表明，纳入实验数据可大大提高预测准确性。在本研究中，我们表明从SID中提取的出现能量（AE，定义为10%碎片化）可与Rosetta结合使用，以成功评估蛋白质-蛋白质对接姿势。我们开发了一个改进模型来预测测量的SID AE，并将该模型纳入一个评分函数，该评分函数将RosettaDock评分函数与一个新的SID评分项相结合，该评分项量化了实验与RosettaDock生成的结构之间的一致性。作为原理验证，我们使用理想的SID AE数据（使用预测模型从晶体结构确定的AE）在57个系统上测试了这些限制的有效性。当使用理论AE时，在95%的情况下，所选结构的均方根偏差（RMSD）得到改善或保持不变。当将实验性SID数据纳入另一组系统时，该方法对6/9个测试案例预测出接近天然的结构（与天然结构的均方根偏差小于2 Å，RMSD），而无限制的RosettaDock（无SID数据）仅预测出3/9个此类案例。当纳入SID数据时，评分与RMSD漏斗图也得到了改善。此外，我们开发了一种置信度测量方法，以在没有晶体结构的情况下评估预测模型的质量。
