Department of Biomolecular Engineering, University of California, Santa Cruz, CA, 95064, USA.
Center for Computational and Genomic Medicine, Department of Pathology and Laboratory Medicine, The Children's Hospital of Philadelphia and Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, 3501 Civic Center Boulevard, Philadelphia, PA, 19104, USA.
Nat Commun. 2021 Jan 29;12(1):691. doi: 10.1038/s41467-021-20984-0.
Methyl-specific isotope labeling is a powerful tool to study the structure, dynamics and interactions of large proteins and protein complexes by solution-state NMR. However, widespread applications of this methodology have been limited by challenges in obtaining confident resonance assignments. Here, we present Methyl Assignments Using Satisfiability (MAUS), leveraging Nuclear Overhauser Effect cross-peak data, peak residue type classification and a known 3D structure or structural model to provide robust resonance assignments consistent with all the experimental inputs. Using data recorded for targets with known assignments in the 10-45 kDa size range, MAUS outperforms existing methods by up to 25,000 times in speed while maintaining 100% accuracy. We derive de novo assignments for multiple Cas9 nuclease domains, demonstrating that the methyl resonances of multi-domain proteins can be assigned accurately in a matter of days, while reducing biases introduced by manual pre-processing of the raw NOE data. MAUS is available through an online web-server.
甲基特异性同位素标记是一种强大的工具,可通过溶液态 NMR 研究大型蛋白质和蛋白质复合物的结构、动态和相互作用。然而,由于在获得可靠的共振分配方面存在挑战,该方法的广泛应用受到了限制。在这里,我们提出了利用满足性(MAUS)进行甲基分配,利用核奥弗豪瑟效应交叉峰数据、峰残基类型分类以及已知的 3D 结构或结构模型,提供与所有实验输入一致的稳健共振分配。使用在 10-45 kDa 大小范围内具有已知分配的目标记录的数据,MAUS 的速度比现有方法快高达 25,000 倍,同时保持 100%的准确性。我们为多个 Cas9 核酸酶结构域推导出了从头分配,证明了在几天内就可以准确地分配多结构域蛋白质的甲基共振,同时减少了对原始 NOE 数据进行手动预处理所引入的偏差。MAUS 可通过在线网络服务器获得。