Predicting Substrates by Docking High-Energy Intermediates to Enzyme Structures

With the emergence of sequences and even structures for proteins of unknown function, structure-based prediction of enzyme activity has become a pragmatic as well as an interesting question. Here we investigate a method to predict substrates for enzymes of known structure by docking high-energy intermediate forms of the potential substrates. A database of such high-energy transition-state analogues was created from the KEGG metabolites. To reduce the number of possible reactions to consider, we restricted ourselves to enzymes of the amidohydrolase superfamily. We docked each metabolite into seven different amidohydrolases in both the ground-state and the high-energy intermediate forms. Docking the high-energy intermediates improved the discrimination between decoys and substrates significantly over the corresponding standard ground-state database, both by enrichment of the true substrates and by geometric fidelity. To test this method prospectively, we attempted to predict the enantioselectivity of a set of chiral substrates for phosphotriesterase, for both wild-type and mutant forms of this enzyme. The stereoselectivity ratios of the six enzymes considered for those four substrate enantiomer pairs differed over a range of 10- to 10 000-fold and underwent 20 switches in stereoselectivities for favored enantiomers, compared to the wild type. The docking of the high-energy intermediates correctly predicted the stereoselectivities for 18 of the 20 substrate/enzyme combinations when compared to subsequent experimental synthesis and testing. The possible applications of this approach to other enzymes are considered.随着功能未知蛋白质的序列乃至结构相继被解析，基于结构的酶活预测已成为兼具实际应用价值与研究意义的科学课题。本研究探索了一种方法，通过对潜在底物的高能中间体形式进行分子对接（docking），以预测已知结构酶的底物。我们从京都基因与基因组百科全书（KEGG）的代谢物中构建了这类高能过渡态类似物数据库。为缩小待考察反应的范围，本研究将研究对象限定为酰胺水解酶超家族（amidohydrolase superfamily）的酶。我们分别以基态与高能中间体形式，将每一种代谢物对接到7种不同的酰胺水解酶中。相较于对应的标准基态数据库，通过富集真实底物与提升几何匹配度两方面，高能中间体的分子对接显著提升了对诱饵分子与真实底物的区分能力。为前瞻性验证该方法的有效性，我们针对磷酸三酯酶（phosphotriesterase）的野生型与突变型两种形式，尝试预测一系列手性底物的对映选择性。针对4组底物对映异构体对，本研究涉及的6种酶的立体选择性比值差异跨度达10至10000倍，且与野生型酶相比，其中20组底物-酶组合的优势对映体立体选择性发生了反转。后续通过实验合成与验证发现，高能中间体分子对接可准确预测20组底物-酶组合中的18组的立体选择性。本研究同时探讨了该方法在其他酶类中的潜在应用场景。