The Donnelly Centre, University of Toronto, Toronto, ON, Canada.
BMC Bioinformatics. 2013 Jan 21;14:27. doi: 10.1186/1471-2105-14-27.
PDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.
We developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training-testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.
We built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training-testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.
PDZ 结构域是一种识别简单线性氨基酸基序的结构蛋白域，通常位于蛋白质 C 末端，并介导离子通道调节、细胞极性和神经发育等重要生物过程中的蛋白质-蛋白质相互作用 (PPIs)。已经基于结构域和肽序列信息开发了 PDZ 结构域-肽相互作用预测器。由于已知结构域结构会影响结合特异性，因此我们假设可以使用结构信息来预测新的相互作用，而不是基于序列的预测器。
我们使用支持向量机 (SVM) 基于 PDZ 结构域结构和肽序列信息训练了一种新型 PDZ 结构域和 C 末端肽相互作用的计算预测器。使用广泛的交叉验证测试估计性能。我们使用基于结构的预测器来扫描人类蛋白质组中的 218 个 PDZ 结构域的配体，并表明预测结果与已知的 PDZ 结构域-肽相互作用和已审核数据库中的 PPIs 相对应。基于结构的预测器与基于序列的预测器互补，可发现独特的已知和新的 PPIs，并且对训练-测试结构域序列相似性的依赖性较小。我们使用我们的命中结果的功能富集分析来创建 PDZ 结构域生物学的预测图谱。该图谱突出了 PDZ 结构域在各种生物学过程中的参与，其中一些仅通过基于结构的预测器发现。基于此分析，我们预测了 PDZ 结构域在异生物质代谢中的新作用，并为包括伤口愈合和 Wnt 信号在内的其他过程提出了新的相互作用。
我们构建了一种 PDZ 结构域-肽相互作用的基于结构的预测器，可用于扫描 C 末端蛋白质组中的 PDZ 相互作用。我们还表明，基于结构的预测器在人类中发现了许多以前基于序列的预测器未发现的已知 PDZ 介导的 PPIs，并且对训练-测试结构域序列相似性的依赖性较小。使用这两种预测器，我们定义了人类 PDZ 结构域生物学的功能图谱，并预测了新的 PDZ 结构域功能。用户可以在 http://webservice.baderlab.org/domains/POW 访问我们的基于结构和以前的基于序列的预测器。
