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首页> 外文期刊>Journal of Molecular Biology >Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains.
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Structure-based prediction of the peptide sequence space recognized by natural and synthetic PDZ domains.

机译：天然和合成PDZ域识别的肽序列空间的基于结构的预测。

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摘要

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.
机译：蛋白质-蛋白质识别通常由相互作用域大家族的成员介导，是生物学功能的基石之一。在这里，我们提出了一种基于计算的，基于结构的方法来预测被PDZ域识别的肽的序列空间，PDZ域是识别蛋白最大的家族之一。作为测试集，我们使用了大量的近期噬菌体展示数据，这些数据描述了169个天然存在的和工程改造的PDZ域的肽段识别偏好。对于野生型PDZ域和单点突变体，我们发现通过噬菌体展示最常观察到的氨基酸中有70-80％在排名前5位的氨基酸中被预测。噬菌体展示经常识别出不同于原始晶体结构中存在的氨基酸的识别偏好。值得注意的是，在大约一半的情况下，我们的算法正确捕获了这些偏好，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，我们可以通过计算概括突变后的特异性变化，这是成功进行蛋白-蛋白界面特异性正向设计的关键测试。在所有评估的数据集中，我们发现并入主链采样可大大提高准确性，而与使用晶体或NMR结构作为起始构象无关。最后，我们报告了DREAM4肽识别域特异性预测挑战中的盲法测试成功预测了几种氨基酸特异性的变化。因为此处开发的基础方法是基于结构的，所以这些结果表明该方法可以更普遍地应用于具有结构信息但缺乏噬菌体展示数据的其他蛋白质-蛋白质界面的特异性预测和重新设计。

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