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首页> 外文期刊>Journal of Molecular Biology >Characterization of domain-peptide interaction interface: A case study on the amphiphysin-1 SH3 domain
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Characterization of domain-peptide interaction interface: A case study on the amphiphysin-1 SH3 domain

机译：域-肽相互作用界面的表征：以amphiphysin-1 SH3域为例

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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.
机译：许多重要的蛋白质间相互作用是由肽识别模块域介导的，例如Src同源3（SH3），SH2，PDZ和WW域。表征结构域-肽复合物的相互作用界面并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，我们建议使用高能分解分析来表征域-肽相互作用和分子相互作用能组分（MIEC），包括范德华力，静电和结合界面上残基对之间的去溶剂化能。我们显示了关于amphiphysin-1 SH3域与其肽配体相互作用的概念验证研究。首先使用虚拟诱变对与884肽复合的人amphiphysin-1 SH3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，以证明它们在预测肽的结合亲和力以及将肽分为结合剂和非结合剂方面有用。类别。首先，结合偏最小二乘分析和遗传算法，我们在训练数据集上的MIEC和肽结合亲和力之间拟合了线性回归模型。然后将这些模型用于预测测试数据集中肽的结合亲和力。与实验测量值相比，预测值的相关系数为0.81，无符号平均误差为0.39。 MIECs的部分最小二乘遗传算法分析揭示了两亲物1 SH3域的结合特异性的关键相互作用。接下来，使用支持向量机（SVM）根据训练集中肽段的MIEC建立分类模型。严格的培训-验证程序用于评估SVM中不同内核功能以及MIEC的不同组合的性能。最佳的SVM分类器对测试集给出了令人满意的预测，结合肽和非结合肽的平均预测准确率分别为78％和91％。我们还表明，我们的方法在结合亲和力预测和粘合剂/非粘合剂分类方面的性能均优于常规MM / Poisson-Boltzmarm溶剂可及表面积和MM /广义Born溶剂可及表面积计算的性能。我们的研究表明，对肽与SH3结构域之间的MIEC进行分析可以成功表征结合界面，并且它提供了一个框架，可以为不同的结构域-肽系统推导集成的预测模型。 （C）2008 Elsevier Ltd.保留所有权利。

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