Laboratory of Chemical Physics, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892-0520, USA.
J Biomol NMR. 2010 Sep;48(1):13-22. doi: 10.1007/s10858-010-9433-9. Epub 2010 Jul 14.
NMR chemical shifts provide important local structural information for proteins and are key in recently described protein structure generation protocols. We describe a new chemical shift prediction program, SPARTA+, which is based on artificial neural networking. The neural network is trained on a large carefully pruned database, containing 580 proteins for which high-resolution X-ray structures and nearly complete backbone and (13)C(beta) chemical shifts are available. The neural network is trained to establish quantitative relations between chemical shifts and protein structures, including backbone and side-chain conformation, H-bonding, electric fields and ring-current effects. The trained neural network yields rapid chemical shift prediction for backbone and (13)C(beta) atoms, with standard deviations of 2.45, 1.09, 0.94, 1.14, 0.25 and 0.49 ppm for delta(15)N, delta(13)C', delta(13)C(alpha), delta(13)C(beta), delta(1)H(alpha) and delta(1)H(N), respectively, between the SPARTA+ predicted and experimental shifts for a set of eleven validation proteins. These results represent a modest but consistent improvement (2-10%) over the best programs available to date, and appear to be approaching the limit at which empirical approaches can predict chemical shifts.
NMR 化学位移为蛋白质提供了重要的局部结构信息，是最近描述的蛋白质结构生成方案中的关键。我们描述了一种新的化学位移预测程序 SPARTA+，它基于人工神经网络。该神经网络是在一个经过精心修剪的大型数据库上进行训练的，该数据库包含 580 种具有高分辨率 X 射线结构和几乎完整的骨架和（13）C（β）化学位移的蛋白质。该神经网络经过训练，可以建立化学位移与蛋白质结构之间的定量关系，包括骨架和侧链构象、氢键、电场和环电流效应。经过训练的神经网络可以快速预测骨架和（13）C（β）原子的化学位移，对于一组 11 个验证蛋白质，对于 delta(15)N、delta(13)C'、delta(13)C(alpha)、delta(13)C(beta)、delta(1)H(alpha)和 delta(1)H(N)，SPARTA+预测的化学位移与实验值之间的标准偏差分别为 2.45、1.09、0.94、1.14、0.25 和 0.49 ppm，与迄今为止可用的最佳程序相比，这代表了适度但一致的改进（2-10%），并且似乎接近经验方法可以预测化学位移的极限。
