Characterization of domain-peptide interaction interface: A case study on the amphiphysin-1 SH3 domain
Characterization of domain-peptide interaction interface: A case study on the amphiphysin-1 SH3 domain
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DOI：
10.1016/j.jmb.2007.12.054
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发表时间：
2008-02-29
影响因子：
5.6
通讯作者：
Wang, Wei
中科院分区：
文献类型：
--
作者：
Hou, Tingjun;Zhang, Wei;Wang, Wei
许多重要的蛋白质-蛋白质相互作用由肽识别模块结构域介导，例如Src同源性3（SH 3）、SH 2、PDZ和WW结构域。表征结构域-肽复合物的相互作用界面和预测模块结构域的结合特异性对于破译蛋白质-蛋白质相互作用网络至关重要。在这里，我们建议使用一个充满活力的分解分析，以表征结构域肽的相互作用和分子相互作用的能量组件（MIECs），包括货车德瓦尔斯，静电，和去溶剂化的结合界面上的残基对之间的能量。我们展示了一个概念验证的研究与它的肽配体相互作用的两栖蛋白-1 SH 3域。首先使用虚拟诱变对与884个肽复合的人两性素-1 SH 3结构域的结构进行建模，并通过分子力学（MM）最小化进行优化。接下来，使用MM/广义Born分解分析计算结构域和肽残基之间的MTEC。我们对MIEC进行了两种类型的统计分析，以证明它们对于预测肽的结合亲和力和将肽分类为结合剂和非结合剂的有用性。类别首先，结合偏最小二乘分析和遗传算法，我们拟合线性回归模型之间的MIEC和肽结合亲和力的训练数据集。然后，这些模型被用来预测结合亲和力的肽在测试数据集，预测值的相关系数为0.81和无符号的平均误差为0.39相比，实验测量的。偏最小二乘法-遗传算法分析的MIECs揭示了关键的相互作用的结合特异性的两栖physin-1 SH 3结构域。接着，采用支持向量机（SVM）基于训练集中肽的MIEC来构建分类模型。一个严格的训练验证程序被用来评估不同的核函数在SVM和不同的组合的MIEC的性能。最好的SVM分类器给出了令人满意的预测的测试集，由平均预测准确率为78%和91%的结合和非结合肽，分别表示。我们还表明，我们的方法在结合亲和力预测和结合剂/非结合剂分类的性能是上级的性能，传统的MM/Poisson-Boltzmarm溶剂可及表面积和MM/广义玻恩溶剂可及表面积计算。我们的研究表明，肽和SH 3结构域之间的MIECs的分析可以成功地表征结合界面，它提供了一个框架，以获得集成的预测模型，为不同的结构域-肽系统。(C)2008爱思唯尔有限公司保留所有权利。
Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MTECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder. categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/ non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmarm solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.(C) 2008 Elsevier Ltd. All rights reserved.