Division of Computational Bioscience, Center for Information Technology, National Institutes of Health, Bethesda, Maryland 20892-5624, USA.
Protein Sci. 2012 Dec;21(12):1824-36. doi: 10.1002/pro.2163. Epub 2012 Oct 18.
Statistical potentials that embody torsion angle probability densities in databases of high-quality X-ray protein structures supplement the incomplete structural information of experimental nuclear magnetic resonance (NMR) datasets. By biasing the conformational search during the course of structure calculation toward highly populated regions in the database, the resulting protein structures display better validation criteria and accuracy. Here, a new statistical torsion angle potential is developed using adaptive kernel density estimation to extract probability densities from a large database of more than 10⁶ quality-filtered amino acid residues. Incorporated into the Xplor-NIH software package, the new implementation clearly outperforms an older potential, widely used in NMR structure elucidation, in that it exhibits simultaneously smoother and sharper energy surfaces, and results in protein structures with improved conformation, nonbonded atomic interactions, and accuracy.
统计势能够体现高质量 X 射线蛋白质结构数据库中的扭转角概率密度，补充了实验核磁共振（NMR）数据集不完整的结构信息。通过在结构计算过程中使构象搜索偏向于数据库中高丰度区域，可以使得到的蛋白质结构显示出更好的验证标准和准确性。在这里，使用自适应核密度估计从超过 106 个质量过滤的氨基酸残基的大型数据库中提取概率密度，开发了一种新的统计扭转角势。该新实现已整合到 Xplor-NIH 软件包中，明显优于在 NMR 结构阐明中广泛使用的旧势，因为它同时表现出更平滑和更锐利的能量表面，并产生具有改进构象、非键原子相互作用和准确性的蛋白质结构。
