GPCR的虚拟筛选：计算机化学基因组学方法
Virtual screening of GPCRs: an in silico chemogenomics approach
摘要：
G蛋白偶联受体（GPCR）超家族是目前最大的一类治疗靶点。因此，GPCR和小分子之间的相互作用的\textit{In silico}预测是药物发现过程中的关键步骤，由于难以表征大多数GPCR的3D结构，并且由于超家族的一些成员的已知配体的量有限，这仍然是一项艰巨的任务。化学基因组学，试图同时表征目标类的所有成员和所有小分子之间的相互作用，最近被提出作为一个有趣的替代传统的对接或基于配体的虚拟筛选策略。我们提出了计算机化学基因组学的新方法，并在GPCR的虚拟筛选上对其进行了验证。该方法代表了最近提出的机器学习策略的扩展，基于支持向量机（SVM），它提供了一个灵活的框架，将各种信息源的生物空间的目标和化学空间的小分子。我们研究了小分子的2D和3D描述符的使用，并测试了GPCR的各种描述符。例如，我们表明，将有关已知的目标家族的分层分类和有关其推断的结合口袋中的关键残基的信息，显着提高了我们的模型的预测精度。特别是，我们能够预测孤儿GPCR的配体，估计准确率为78.1%。
英文摘要：
The G-protein coupled receptor (GPCR) superfamily is currently the largest class of therapeutic targets. \textit{In silico} prediction of interactions between GPCRs and small molecules is therefore a crucial step in the drug discovery process, which remains a daunting task due to the difficulty to characterize the 3D structure of most GPCRs, and to the limited amount of known ligands for some members of the superfamily. Chemogenomics, which attempts to characterize interactions between all members of a target class and all small molecules simultaneously, has recently been proposed as an interesting alternative to traditional docking or ligand-based virtual screening strategies. We propose new methods for in silico chemogenomics and validate them on the virtual screening of GPCRs. The methods represent an extension of a recently proposed machine learning strategy, based on support vector machines (SVM), which provides a flexible framework to incorporate various information sources on the biological space of targets and on the chemical space of small molecules. We investigate the use of 2D and 3D descriptors for small molecules, and test a variety of descriptors for GPCRs. We show fo instance that incorporating information about the known hierarchical classification of the target family and about key residues in their inferred binding pockets significantly improves the prediction accuracy of our model. In particular we are able to predict ligands of orphan GPCRs with an estimated accuracy of 78.1%.
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