Karp Jerome M, Eryilmaz Ertan, Cowburn David
Department of Biochemistry, Albert Einstein College of Medicine of Yeshiva University, 1300 Morris Park Avenue, Bronx, NY, 10461, USA.
J Biomol NMR. 2015 Jan;61(1):35-45. doi: 10.1007/s10858-014-9879-2. Epub 2014 Nov 22.
There has been a longstanding interest in being able to accurately predict NMR chemical shifts from structural data. Recent studies have focused on using molecular dynamics (MD) simulation data as input for improved prediction. Here we examine the accuracy of chemical shift prediction for intein systems, which have regions of intrinsic disorder. We find that using MD simulation data as input for chemical shift prediction does not consistently improve prediction accuracy over use of a static X-ray crystal structure. This appears to result from the complex conformational ensemble of the disordered protein segments. We show that using accelerated molecular dynamics (aMD) simulations improves chemical shift prediction, suggesting that methods which better sample the conformational ensemble like aMD are more appropriate tools for use in chemical shift prediction for proteins with disordered regions. Moreover, our study suggests that data accurately reflecting protein dynamics must be used as input for chemical shift prediction in order to correctly predict chemical shifts in systems with disorder.
长期以来，人们一直对能否从结构数据准确预测核磁共振（NMR）化学位移感兴趣。最近的研究集中在使用分子动力学（MD）模拟数据作为输入来改进预测。在这里，我们研究了内含肽系统化学位移预测的准确性，内含肽系统具有内在无序区域。我们发现，将MD模拟数据用作化学位移预测的输入，并不能始终如一地比使用静态X射线晶体结构提高预测准确性。这似乎是由无序蛋白质片段的复杂构象集合导致的。我们表明，使用加速分子动力学（aMD）模拟可改善化学位移预测，这表明像aMD这样能更好地对构象集合进行采样的方法，是用于具有无序区域蛋白质化学位移预测的更合适工具。此外，我们的研究表明，必须使用准确反映蛋白质动力学的数据作为化学位移预测的输入，以便正确预测具有无序区域系统中的化学位移。
