Protein structure refinement using a quantum mechanics-based chemical shielding predictor.
Protein structure refinement using a quantum mechanics-based chemical shielding predictor.
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DOI：
10.1039/c6sc04344e
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发表时间：
2017-03-01
期刊：
影响因子：
8.4
通讯作者：
Jensen JH
中科院分区：
文献类型：
--
作者：
Bratholm LA;Jensen JH
我们表明，在基于化学位移的结构精修消除了小结构错误（化学位移错误以红色显示）之后，基于 QM 的蛋白质主链和 CB 化学位移预测器与经验化学位移预测器具有相当的准确性。使用基于量子力学 (QM) 的方法准确预测蛋白质化学位移一直是 20 多年来深入研究的主题，但迄今为止，化学位移预测的经验方法已被证明更为准确。在本文中，我们证明了基于 QM 的蛋白质主链和 CB 化学位移预测器 (ProCS15, PeerJ, 2016, 3, e1344) 在基于化学位移的结构精修消除了小结构误差后，其准确性与经验化学位移预测器相当。我们提出了一种方法，通过该方法，基于量子化学的各向同性化学屏蔽值预测 (ProCS15) 可用于使用马尔可夫链蒙特卡罗 (MCMC) 模拟来细化蛋白质结构，从而将化学屏蔽值与实验化学位移概率相关联。使用力场几何优化的 X 射线结构作为起点进行了两种 MCMC 结构细化模拟：起始结构的模拟退火和恒温 MCMC 模拟，然后是代表性系综结构的模拟退火。 CHARMM 结构的退火使 CA-RMSD 平均改变 0.4 Å，但将 CA 和 N 的化学位移 RMSD 降低 1.0 和 0.7 ppm。构象平均对碳化学位移的整体一致性影响相对较小 (0.1–0.2 ppm)，但将氮化学位移的误差降低 0.4 ppm。如果包含氨基酸特异性偏移，则 ProCS15 预测的化学位移具有相对于与流行的经验化学位移预测器相当的实验的 RMSD 值。退火后的代表性整体结构的 CA-RMSD 相对于初始结构平均相差 2.0 Å，其中 6 种蛋白质的差异 >2.0 Å。在其中四种情况下，通过核磁共振确定，最大的结构差异出现在蛋白质的结构灵活区域，而在其余两种情况下，较大的结构变化可能是由于力场缺陷造成的。通过使用 ProCS15 对 CHARMM 结构进行退火，经验方法的整体准确性略有提高，这可能表明基于 ProCS15 的退火引入的微小结构变化提高了蛋白质结构的准确性。确定基于 QM 的化学位移预测可以提供与经验位移预测器相同的准确性，我们希望这可以帮助提高相关方法的准确性，例如 QM/MM 或线性缩放方法或从 QM 衍生的化学位移解释蛋白质结构动力学。
We show that a QM-based predictor of a protein backbone and CB chemical shifts is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors (errors in chemical shifts shown in red). The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1–0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.