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用于预测蛋白质表面环结构的GB/SA溶剂化模型的优化

Optimization of the GB/SA solvation model for predicting the structure of surface loops in proteins.

作者信息

Szarecka Agnieszka, Meirovitch Hagai

机构信息

Department of Computational Biology, University of Pittsburgh School of Medicine, Pennsylvania 15260, USA.

出版信息

J Phys Chem B. 2006 Feb 16;110(6):2869-80. doi: 10.1021/jp055771+.

Abstract

Implicit solvation models are commonly optimized with respect to experimental data or Poisson-Boltzmann (PB) results obtained for small molecules, where the force field is sometimes not considered. In previous studies, we have developed an optimization procedure for cyclic peptides and surface loops in proteins based on the entire system studied and the specific force field used. Thus, the loop has been modeled by the simplified solvation function E(tot) = E(FF) (epsilon = 2r) + Sigma(i) sigma(i)A(i), where E(FF) (epsilon = nr) is the AMBER force field energy with a distance-dependent dielectric function, epsilon = nr, A(i) is the solvent accessible surface area of atom i, and sigma(i) is its atomic solvation parameter. During the optimization process, the loop is free to move while the protein template is held fixed in its X-ray structure. To improve on the results of this model, in the present work we apply our optimization procedure to the physically more rigorous solvation model, the generalized Born with surface area (GB/SA) (together with the all-atom AMBER force field) as suggested by Still and co-workers (J. Phys. Chem. A 1997, 101, 3005). The six parameters of the GB/SA model, namely, P(1)-P(5) and the surface area parameter, sigma (programmed in the TINKER package) are reoptimized for a "training" group of nine loops, and a best-fit set is defined from the individual sets of optimized parameters. The best-fit set and Still's original set of parameters (where Lys, Arg, His, Glu, and Asp are charged or neutralized) were applied to the training group as well as to a "test" group of seven loops, and the energy gaps and the corresponding RMSD values were calculated. These GB/SA results based on the three sets of parameters have been found to be comparable; surprisingly, however, they are somewhat inferior (e.g, of larger energy gaps) to those obtained previously from the simplified model described above. We discuss recent results for loops obtained by other solvation models and potential directions for future studies.

摘要

隐式溶剂化模型通常是根据小分子的实验数据或泊松-玻尔兹曼(PB)结果进行优化的,在这种情况下有时不考虑力场。在之前的研究中,我们基于所研究的整个系统和所使用的特定力场,为蛋白质中的环肽和表面环开发了一种优化程序。因此,环已通过简化的溶剂化函数E(tot) = E(FF) (epsilon = 2r) + Sigma(i) sigma(i)A(i)进行建模,其中E(FF) (epsilon = nr)是具有距离依赖介电函数epsilon = nr的AMBER力场能量,A(i)是原子i的溶剂可及表面积,sigma(i)是其原子溶剂化参数。在优化过程中,环可以自由移动,而蛋白质模板则固定在其X射线结构中。为了改进该模型的结果,在本工作中,我们按照斯蒂尔及其同事(《物理化学杂志A》1997年,101卷,3005页)的建议,将我们的优化程序应用于物理上更严格的溶剂化模型——广义玻恩表面积模型(GB/SA)(连同全原子AMBER力场)。GB/SA模型的六个参数,即P(1)-P(5)和表面积参数sigma(在TINKER软件包中编程)针对九个环的“训练”组进行重新优化,并从各个优化参数集中定义最佳拟合集。将最佳拟合集和斯蒂尔的原始参数集(其中赖氨酸、精氨酸、组氨酸、谷氨酸和天冬氨酸带电或被中和)应用于训练组以及七个环的“测试”组,并计算能量差距和相应的均方根偏差(RMSD)值。基于这三组参数的这些GB/SA结果已被发现具有可比性;然而,令人惊讶的是,它们在某种程度上不如先前从上述简化模型获得的结果(例如,能量差距更大)。我们讨论了通过其他溶剂化模型获得的环的最新结果以及未来研究的潜在方向。

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