Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains
Structure-Based Prediction of the Peptide Sequence Space Recognized by Natural and Synthetic PDZ Domains
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DOI：
10.1016/j.jmb.2010.07.032
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发表时间：
2010-09-17
影响因子：
5.6
通讯作者：
Kortemme, Tanja
中科院分区：
文献类型：
--
作者：
Smith, Colin A.;Kortemme, Tanja
蛋白质-蛋白质识别是生物功能的基石之一，经常由相互作用结构域的大家族成员介导。在这里，我们提出了一种计算的，基于结构的方法来预测由PDZ结构域识别的多肽的序列空间，PDZ结构域是识别蛋白质的最大家族之一。作为一个测试集，我们使用了大量最近的噬菌体展示数据，这些数据描述了169个自然产生的和工程的PDZ结构域的肽识别偏好。对于野生型PDZ结构域和单点突变，我们发现通过噬菌体展示最常观察到的氨基酸中有70%-80%是在排名前五的氨基酸中预测的。噬菌体对不同于原始晶体结构的氨基酸的识别偏好经常被识别出来。值得注意的是，在其中大约一半的情况下，我们的算法正确地捕获了这些偏好，表明它可以预测相对于起始结构增加结合亲和力的突变。我们还发现，我们可以计算概括突变时的特异性变化，这是成功的蛋白质-蛋白质界面特异性正向设计的关键测试。在所有评估的数据集中，我们发现，无论使用晶体或核磁共振结构作为起始构象，掺入骨架采样都大大提高了精度。最后，我们报道了在DREAM4肽识别结构域特异性预测挑战中，通过盲法测试成功预测了几个氨基酸的特异性变化。由于所建立的基本方法是基于结构的，这些结果表明，该方法可以更普遍地应用于其他具有结构信息但缺乏噬菌体展示数据的蛋白质-蛋白质界面的特异性预测和重新设计。(C)2010爱思唯尔有限公司。保留所有权利。
Protein-protein recognition, frequently mediated by members of large families of interaction domains, is one of the cornerstones of biological function. Here, we present a computational, structure-based method to predict the sequence space of peptides recognized by PDZ domains, one of the largest families of recognition proteins. As a test set, we use a considerable amount of recent phage display data that describe the peptide recognition preferences for 169 naturally occurring and engineered PDZ domains. For both wild-type PDZ domains and single point mutants, we find that 70-80% of the most frequently observed amino acids by phage display are predicted within the top five ranked amino acids. Phage display frequently identified recognition preferences for amino acids different from those present in the original crystal structure. Notably, in about half of these cases, our algorithm correctly captures these preferences, indicating that it can predict mutations that increase binding affinity relative to the starting structure. We also find that we can computationally recapitulate specificity changes upon mutation, a key test for successful forward design of protein-protein interface specificity. Across all evaluated data sets, we find that incorporation backbone sampling improves accuracy substantially, irrespective of using a crystal or NMR structure as the starting conformation. Finally, we report successful prediction of several amino acid specificity changes from blind tests in the DREAM4 peptide recognition domain specificity prediction challenge. Because the foundational methods developed here are structure based, these results suggest that the approach can be more generally applied to specificity prediction and redesign of other protein-protein interfaces that have structural information but lack phage display data. (C) 2010 Elsevier Ltd. All rights reserved.