Wang Haidong, Segal Eran, Ben-Hur Asa, Li Qian-Ru, Vidal Marc, Koller Daphne
Computer Science Department, Stanford University, Serra Mall, Stanford, CA 94305, USA.
Genome Biol. 2007;8(9):R192. doi: 10.1186/gb-2007-8-9-r192.
We propose InSite, a computational method that integrates high-throughput protein and sequence data to infer the specific binding regions of interacting protein pairs. We compared our predictions with binding sites in Protein Data Bank and found significantly more binding events occur at sites we predicted. Several regions containing disease-causing mutations or cancer polymorphisms in human are predicted to be binding for protein pairs related to the disease, which suggests novel mechanistic hypotheses for several diseases.
我们提出了InSite,这是一种整合高通量蛋白质和序列数据以推断相互作用蛋白质对的特定结合区域的计算方法。我们将我们的预测结果与蛋白质数据库中的结合位点进行了比较,发现我们预测的位点发生的结合事件明显更多。在人类中,几个含有致病突变或癌症多态性的区域被预测为与该疾病相关的蛋白质对的结合区域,这为几种疾病提出了新的机制假说。