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陆续被解析，基于结构的酶活性预测已成为兼具实用性与研究价值的重要课题。本研究旨在探索一种方法，通过对潜在底物的高能中间体形式进行分子对接，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）代谢物中构建了一类高能过渡态类似物数据库。为缩小待考察反应的范围，我们将研究对象限定为酰胺水解酶超家族的酶。我们将每种代谢物分别以基态与高能中间体形式，对接至7种不同的酰胺水解酶中。相较于标准基态数据库，对接高能中间体可通过提升真实底物富集度与优化几何贴合度两方面，显著增强对底物与诱饵分子的区分能力。为前瞻性验证该方法，我们针对磷酸三酯酶的野生型与突变体形式，尝试预测一组手性底物的对映选择性。针对这4对底物对映体，所考察的6种酶的立体选择性比值跨度达10至10000倍，且与野生型相比，其优势对映体的立体选择性发生了20次切换。相较于后续的实验合成与验证结果，高能中间体对接可准确预测20个底物/酶组合中的18个的立体选择性。最后，我们探讨了该方法在其他酶类中的潜在应用场景。