Chemical Sciences Laboratory, Department of Chemistry and Biochemistry and National High Magnetic Field Laboratory, Florida State University, Tallahassee, FL 32306, USA.
J Biomol NMR. 2012 Nov;54(3):257-65. doi: 10.1007/s10858-012-9668-8. Epub 2012 Sep 13.
The combination of the wide availability of protein backbone and side-chain NMR chemical shifts with advances in understanding of their relationship to protein structure makes these parameters useful for the assessment of structural-dynamic protein models. A new chemical shift predictor (PPM) is introduced, which is solely based on physical-chemical contributions to the chemical shifts for both the protein backbone and methyl-bearing amino-acid side chains. To explicitly account for the effects of protein dynamics on chemical shifts, PPM was directly refined against 100 ns long molecular dynamics (MD) simulations of 35 proteins with known experimental NMR chemical shifts. It is found that the prediction of methyl-proton chemical shifts by PPM from MD ensembles is improved over other methods, while backbone Cα, Cβ, C', N, and H(N) chemical shifts are predicted at an accuracy comparable to the latest generation of chemical shift prediction programs. PPM is particularly suitable for the rapid evaluation of large protein conformational ensembles on their consistency with experimental NMR data and the possible improvement of protein force fields from chemical shifts.
蛋白质主链和侧链 NMR 化学位移的广泛可用性与对它们与蛋白质结构关系的理解的进步相结合，使得这些参数可用于评估结构动力学蛋白质模型。引入了一种新的化学位移预测器（PPM），该预测器仅基于对蛋白质主链和带甲基氨基酸侧链的化学位移的物理化学贡献。为了明确考虑蛋白质动力学对化学位移的影响，PPM 直接针对 35 种具有已知实验 NMR 化学位移的蛋白质的 100 ns 长分子动力学（MD）模拟进行了精细调整。结果发现，与其他方法相比，PPM 从 MD 集合中预测甲基质子化学位移的能力得到了提高，而主链 Cα、Cβ、C'、N 和 H（N）化学位移的预测精度与最新一代化学位移预测程序相当。PPM 特别适合快速评估与实验 NMR 数据一致的大型蛋白质构象集合，以及从化学位移改进蛋白质力场的可能性。
