Backbone-independent NMR resonance assignments of methyl probes in large proteins.
Backbone-independent NMR resonance assignments of methyl probes in large proteins.
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DOI：
10.1038/s41467-021-20984-0
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发表时间：
2021-01-29
影响因子：
16.6
通讯作者：
Sgourakis NG
中科院分区：
文献类型：
--
作者：
Nerli S;De Paula VS;McShan AC;Sgourakis NG
甲基特异性同位素标记技术是研究大分子蛋白质和蛋白质复合物结构、动力学和相互作用的有力工具。然而，这种方法的广泛应用受到了在获得自信的共振分配方面的挑战的限制。在这里，我们提出了使用可满足性（MAUS）的甲基取代，利用核Overhauser效应交叉峰数据，峰残基类型分类和已知的3D结构或结构模型，以提供与所有实验输入一致的稳健的共振分配。使用10-45 kDa大小范围内已知分配的目标记录的数据，MAUS在速度上比现有方法高出25，000倍，同时保持100%的准确性。我们推导了多个Cas9核酸酶结构域的从头分配，证明了多结构域蛋白质的甲基共振可以在几天内准确分配，同时减少了原始NOE数据的手动预处理引入的偏差。MAUS通过在线网络服务器提供。在这里，作者提出了甲基化的可满足性（MAUS），一种使用原始NOE数据分配甲基的方法。他们使用8种10-45 kDa大小范围内的蛋白质作为测试案例，并表明MAUS在高完整性水平下产生100%准确的分配。
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. Here, the authors present Methyl Assignments Using Satisfiability (MAUS), a method for the assignment of methyl groups using raw NOE data. They use eight proteins in the 10–45 kDa size range as test cases and show that MAUS yields 100% accurate assignments at high completeness levels.
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影响因子：
16.6
作者：
Behera SP;Dubey A;Chen WN;De Paula VS;Zhang M;Sgourakis NG;Bermel W;Wagner G;Coote PW;Arthanari H
通讯作者：
Arthanari H
影响因子：
15
作者：
East, Kyle W.;Newton, Jocelyn C.;Lisi, George P.
通讯作者：
Lisi, George P.
影响因子：
15
作者：
Gardner, KH;Kay, LE
通讯作者：
Kay, LE
DOI：
10.1145/6462.6502
发表时间：
1986-03-01
期刊：
COMPUTING SURVEYS
影响因子：
--
作者：
GALIL, Z
通讯作者：
GALIL, Z
影响因子：
13.6
作者：
Mas G;Guan JY;Crublet E;Debled EC;Moriscot C;Gans P;Schoehn G;Macek P;Schanda P;Boisbouvier J
通讯作者：
Boisbouvier J