Novel Computational Methods for Modeling Cytochrome P450 Mediated Drug Metabolism

描述(申请人提供)：药物和其他外源物质的代谢由细胞色素P450酶(CYP)进行，是人体内一种重要的解毒和药物清除机制。尽管自2000年以来已经发表了几种X射线结构，但对药物代谢的可靠计算预测仍然是一个巨大的挑战。在这项拨款申请中，我们提出了一种综合的高通量方法，它结合了配体的电子性质和蛋白质的结构性质。我们的主要目标是开发和应用创新的基于结构的设计技术，以解决当前方法的严重缺陷。特别是，我们已经扩展了我们的新的对接概念，包括与配体-CYP相互作用相关的所有观察到的蛋白质柔性形式，影响结合姿势预测的熵贡献，并且我们将增加对配体-CYP络合物的即时溶剂化。为了提高对接质量，我们将优化为每个研究的CYP酶量身定做的评分函数的参数。结合最初对高效计算吸氢能量的关注，我们将预测与环磷酰胺结合的药物或候选药物的区域选择性代谢。基于所产生的对接姿势，将进行多维QSAR模拟，以准确量化结合亲和力作为衡量CYP抑制和更好地对结合模式进行排序。新的计算方法将应用于两种在药物代谢中起重要作用的CYP酶(CYP2C9，3A4)。生成的计算模型将存储在数据库中并公开提供。其他研究人员将被邀请通过安全的网络协议根据我们的CYP数据库筛选化合物，以预测药物代谢和抑制。其他研究人员提交的数据将对模型的性能和适用性提供宝贵的反馈。

DESCRIPTION (provided by applicant): The metabolism of drugs and other xenobiotics by cytochrome P450 enzymes (CYP) is an essential detoxification and drug clearance mechanism in humans. Despite the publication of several X-ray structures since 2000, reliable computational prediction of drug metabolism remains a huge challenge. In this grant application, we propose an integrative high-throughput approach that combines electronic properties of a ligand with structural properties of the protein. Our main aim is the development and application of innovative structure-based design techniques that address serious shortcomings of current approaches. In particular, we have extended our novel docking concept to incorporate all observed forms of protein flexibility relevant for ligand-CYP interactions, entropic contributions influencing the prediction of binding poses, and we will add on- the-fly solvation to ligand-CYP complexes. To improve the docking quality we will optimize the parameters of a scoring function tailor-made for each CYP enzyme studied. In combination with an initial focus on efficient calculation of hydrogen-abstraction energies we will predict regioselective metabolism of drugs or drug candidates binding to CYPs. Based on the resulting docking poses, multidimensional QSAR simulations will be performed for accurate quantification of binding affinity as a measure CYP-inhibition and better ranking of binding modes. The new computational methods will be applied to two CYP enzymes (CYP2C9, 3A4) important in drug metabolism. The generated computational models will be stored in a database and made publicly available. Other researchers will be invited to screen compounds against our CYP database via a secure Web protocol to predict drug metabolism and inhibition. The submission of data by other researchers will provide valuable feedback on the performance and applicability of the models.