Vinkers H M, de Jonge M R, Daeyaert E D, Heeres J, Koymans L M H, van Lenthe J H, Lewi P J, Timmerman H, Janssen P A J
Center for Molecular Design, Janssen Pharmaceutica N.V., Antwerpsesteenweg 37, B-2350 Vosselaar, Belgium.
J Comput Aided Mol Des. 2003 Sep;17(9):567-81. doi: 10.1023/b:jcam.0000005748.19093.e8.
We have developed a computational approach in which an inhibitor's strength is determined from its interaction energy with a limited set of amino acid residues of the inhibited protein. We applied this method to HIV protease. The method uses a consensus structure built from X-ray crystallographic data. All inhibitors are docked into the consensus structure. Given that not every ligand-protein interaction causes inhibition, we implemented a genetic algorithm to determine the relevant set of residues. The algorithm optimizes the q2 between the sum of interaction energies and the observed inhibition constants. The best possible predictive model resulting has a q2 of 0.63. External validation by examining the predictivity for compounds not used in derivation of the model leads to a prediction accuracy between 0.9 and 1.5 log10 unit. Out of 198 residues in the whole protein, the best internally predictive model defines a subset of 20 residues and the best externally predictive model one of 9 residues. These residues are distributed over the subsites of the enzyme. This approach provides insight in which interactions are important for inhibiting HIV protease and it allows for quantitative prediction of inhibitor strength.
我们开发了一种计算方法，通过抑制剂与被抑制蛋白的有限氨基酸残基集之间的相互作用能来确定抑制剂的强度。我们将此方法应用于HIV蛋白酶。该方法使用由X射线晶体学数据构建的共有结构。所有抑制剂都对接至该共有结构中。鉴于并非每个配体-蛋白质相互作用都会导致抑制作用，我们实施了一种遗传算法来确定相关的残基集。该算法优化了相互作用能总和与观察到的抑制常数之间的q2。由此得到的最佳预测模型的q2为0.63。通过检查对模型推导中未使用的化合物的预测能力进行外部验证，预测准确度在0.9至1.5个log10单位之间。在整个蛋白质的198个残基中，最佳内部预测模型定义了一个由20个残基组成的子集，最佳外部预测模型定义了一个由9个残基组成的子集。这些残基分布在酶的亚位点上。这种方法有助于深入了解哪些相互作用对于抑制HIV蛋白酶很重要，并且能够对抑制剂强度进行定量预测。
