Department of Biological Sciences, National University of Singapore, Singapore.
J Chem Phys. 2011 Aug 7;135(5):055104. doi: 10.1063/1.3621831.
We present an extension of the self-consistent mean field theory for protein side-chain modeling in which solvation effects are included based on the Poisson-Boltzmann (PB) theory. In this approach, the protein is represented with multiple copies of its side chains. Each copy is assigned a weight that is refined iteratively based on the mean field energy generated by the rest of the protein, until self-consistency is reached. At each cycle, the variational free energy of the multi-copy system is computed; this free energy includes the internal energy of the protein that accounts for vdW and electrostatics interactions and a solvation free energy term that is computed using the PB equation. The method converges in only a few cycles and takes only minutes of central processing unit time on a commodity personal computer. The predicted conformation of each residue is then set to be its copy with the highest weight after convergence. We have tested this method on a database of hundred highly refined NMR structures to circumvent the problems of crystal packing inherent to x-ray structures. The use of the PB-derived solvation free energy significantly improves prediction accuracy for surface side chains. For example, the prediction accuracies for χ(1) for surface cysteine, serine, and threonine residues improve from 68%, 35%, and 43% to 80%, 53%, and 57%, respectively. A comparison with other side-chain prediction algorithms demonstrates that our approach is consistently better in predicting the conformations of exposed side chains.
我们提出了一种扩展的自洽平均场理论，用于蛋白质侧链建模，其中包括基于泊松-玻尔兹曼（PB）理论的溶剂化效应。在这种方法中，蛋白质由其侧链的多个副本表示。每个副本都被分配一个权重，该权重根据蛋白质其余部分产生的平均场能量进行迭代细化，直到达到自洽。在每个循环中，计算多副本系统的变分自由能；该自由能包括蛋白质的内能，其中包括范德华和静电相互作用，以及使用 PB 方程计算的溶剂化自由能项。该方法仅在几个循环内收敛，在商用个人计算机上只需几分钟的中央处理单元时间。然后，将每个残基的预测构象设置为收敛后权重最高的副本。我们已经在一个包含数百个高度精炼 NMR 结构的数据库上测试了这种方法，以避免 X 射线结构中固有的晶体堆积问题。使用 PB 衍生的溶剂化自由能显著提高了表面侧链的预测准确性。例如，表面半胱氨酸、丝氨酸和苏氨酸残基的 χ(1)预测精度从 68%、35%和 43%分别提高到 80%、53%和 57%。与其他侧链预测算法的比较表明，我们的方法在预测暴露侧链的构象方面始终更好。
