Small molecule affinity fingerprinting:: a tool for enzyme family subclassification, target identification, and inhibitor design
Small molecule affinity fingerprinting:: a tool for enzyme family subclassification, target identification, and inhibitor design
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DOI：
10.1016/s1074-5521(02)00238-7
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发表时间：
2002-10-01
影响因子：
--
通讯作者：
Bogyo, M
中科院分区：
文献类型：
--
作者：
Greenbaum, DC;Arnold, WD;Bogyo, M
仅根据一级序列信息将蛋白质分类为功能不同的家族仍然是一项艰巨的任务。我们在这里描述了一种方法来产生一组密切相关的酶，半胱氨酸蛋白酶的木瓜蛋白酶家族的小分子亲和指纹的大数据集。基于每种化合物通过基于共价活性的探针（ABP）阻断靶蛋白酶的活性位点标记的能力，生成抑制剂文库的结合数据。聚类算法用于基于蛋白酶的小分子亲和指纹将蛋白酶的参考组自动分类为亚家族。该方法还用于通过直接比较目标亲和指纹与蛋白酶参考文库的目标亲和指纹来鉴定复杂蛋白质组中由ABP修饰的半胱氨酸蛋白酶目标。最后，实验数据被用来指导一个计算方法，预测小分子抑制剂的基础上报告的晶体结构的发展。这种方法最终可以用于大的酶家族，以帮助设计基于有限的结构/功能信息的目标的选择性抑制剂。
Classifying proteins into functionally distinct families based only on primary sequence information remains a difficult task. We describe here a method to generate a large data set of small molecule affinity fingerprints for a group of closely related enzymes, the papain family of cysteine proteases. Binding data was generated for a library of inhibitors based on the ability of each compound to block active-site labeling of the target proteases by a covalent activity based probe (ABP). Clustering algorithms were used to automatically classify a reference group of proteases into subfamilies based on their small molecule affinity fingerprints. This approach was also used to identify cysteine protease targets modified by the ABP in complex proteomes by direct comparison of target affinity fingerprints with those of the reference library of proteases. Finally, experimental data were used to guide the development of a computational method that predicts small molecule inhibitors based on reported crystal structures. This method could ultimately be used with large enzyme families to aid in the design of selective inhibitors of targets based on limited structural/function information.