Assignment of Protonation States in Proteins and Ligands: Combining pKa Prediction with Hydrogen Bonding Network Optimization
Assignment of Protonation States in Proteins and Ligands: Combining pKa Prediction with Hydrogen Bonding Network Optimization
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DOI：
10.1007/978-1-61779-465-0_25
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发表时间：
2012-01-01
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影响因子：
--
通讯作者：
Krieger, Barbara
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文献类型：
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作者：
Krieger, Elmar;Dunbrack, Roland;Krieger, Barbara
在分子建模的众多应用中，药物设计可能是对基础结构准确性要求最高的一类。在先导化合物优化过程中，理想情况下应该高精度地了解结合位点中每个原子的位置，以识别最有可能增加药物亲和力的化学修饰。不幸的是，普通分辨率下的 X 射线晶体学产生的电子密度图过于粗糙，因为化学元素及其质子化状态无法完全解析。本章介绍了通过设计一种可以检测和解决歧义的算法来填补缺失知识所需的步骤。首先，预测酸性基团和碱性基团的 pK(a) 值。其次，确定它们的潜在质子化状态，包括所有排列（例如考虑可以在磷酸基团的氧之间跳跃的质子）。第三，确定了那些可以采用具有基本相同电子密度的替代但无法区分的构象的原子团。第四，定位潜在的氢键供体和受体。最后，所有这些数据被组合成一个“配置能量函数”，其全局最小值是通过 SCWRL 算法找到的，该算法采用了死端消除和图论。结果，人们获得了蛋白质及其结合配体的完整模型，其中模糊基团旋转到最佳方向，并考虑当前 pH 值和氢键网络分配质子化状态。该算法的实现自 2008 年起作为 YASARA 建模和仿真程序的一部分提供。
Among the many applications of molecular modeling, drug design is probably the one with the highest demands on the accuracy of the underlying structures. During lead optimization, the position of every atom in the binding site should ideally be known with high precision to identify those chemical modifications that arc most likely to increase drug affinity. Unfortunately, X-ray crystallography at common resolution yields an electron density map that is too coarse, since the chemical elements and their protonation states cannot be fully resolved.This chapter describes the steps required to fill in the missing knowledge, by devising an algorithm that can detect and resolve the ambiguities. First, the pK(a) values of acidic and basic groups are predicted. Second, their potential protonation states are determined, including all permutations (considering for example protons that can jump between the oxygens of a phosphate group). Third, those groups of atoms are identified that can adopt alternative but indistinguishable conformations with essentially the same electron density. Fourth, potential hydrogen bond donors and acceptors are located. Finally, all these data are combined in a single "configuration energy function," whose global minimum is found with the SCWRL algorithm, which employs dead-end elimination and graph theory. As a result, one obtains a complete model of the protein and its bound ligand, with ambiguous groups rotated to the best orientation and with protonation states assigned considering the current pH and the H-bonding network. An implementation of the algorithm has been available since 2008 as part of the YASARA modeling & simulation program.