Potluri Shobha, Yan Anthony K, Donald Bruce R, Bailey-Kellogg Chris
Department of Computer Science, Dartmouth College, Hanover, NH 03755, USA.
Protein Sci. 2007 Jan;16(1):69-81. doi: 10.1110/ps.062427307.
Assignment of nuclear Overhauser effect (NOE) data is a key bottleneck in structure determination by NMR. NOE assignment resolves the ambiguity as to which pair of protons generated the observed NOE peaks, and thus should be restrained in structure determination. In the case of intersubunit NOEs in symmetric homo-oligomers, the ambiguity includes both the identities of the protons within a subunit, and the identities of the subunits to which they belong. This paper develops an algorithm for simultaneous intersubunit NOE assignment and C(n) symmetric homo-oligomeric structure determinations, given the subunit structure. By using a configuration space framework, our algorithm guarantees completeness, in that it identifies structures representing, to within a user-defined similarity level, every structure consistent with the available data (ambiguous or not). However, while our approach is complete in considering all conformations and assignments, it avoids explicit enumeration of the exponential number of combinations of possible assignments. Our algorithm can draw two types of conclusions not possible under previous methods: (1) that different assignments for an NOE would lead to different structural classes, or (2) that it is not necessary to uniquely assign an NOE, since it would have little impact on structural precision. We demonstrate on two test proteins that our method reduces the average number of possible assignments per NOE by a factor of 2.6 for MinE and 4.2 for CCMP. It results in high structural precision, reducing the average variance in atomic positions by factors of 1.5 and 3.6, respectively.
核Overhauser效应(NOE)数据的归属是通过核磁共振确定结构的关键瓶颈。NOE归属解决了关于哪一对质子产生了观察到的NOE峰的模糊性问题,因此在结构确定中应加以限制。在对称同型寡聚体的亚基间NOE的情况下,这种模糊性既包括一个亚基内质子的身份,也包括它们所属亚基的身份。本文开发了一种算法,在已知亚基结构的情况下,用于同时进行亚基间NOE归属和C(n)对称同型寡聚体结构确定。通过使用配置空间框架,我们的算法保证了完整性,即它能识别出在用户定义的相似性水平内,与可用数据(无论是否模糊)一致的每一个结构所代表的结构。然而,虽然我们的方法在考虑所有构象和归属时是完整的,但它避免了对可能归属的指数数量组合进行显式枚举。我们的算法可以得出以前方法无法得出的两种类型的结论:(1)NOE的不同归属会导致不同的结构类别,或者(2)由于对结构精度影响不大,不必唯一地归属一个NOE。我们在两种测试蛋白上证明,我们的方法将MinE每个NOE的平均可能归属数量减少了2.6倍,将CCMP减少了4.2倍。它带来了很高的结构精度,分别将原子位置的平均方差降低了1.5倍和3.6倍。