The ability to consistently distinguish real protein structures from computationally generated model decoys is not yet a solved problem. One route to distinguish real protein structures from decoys is to delineate the important physical features that specify a real protein. For example, it has long been appreciated that the hydrophobic cores of proteins contribute significantly to their stability. We used two sources to obtain datasets of decoys to compare with real protein structures: submissions to the biennial Critical Assessment of protein Structure Prediction competition, in which researchers attempt to predict the structure of a protein only knowing its amino acid sequence, and also decoys generated by 3DRobot, which have user‐specified global root‐mean‐squared deviations from experimentally determined structures. Our analysis revealed that both sets of decoys possess cores that do not recapitulate the key features that define real protein cores. In particular, the model structures appear more densely packed (because of energetically unfavorable atomic overlaps), contain too few residues in the core, and have improper distributions of hydrophobic residues throughout the structure. Based on these observations, we developed a feed‐forward neural network, which incorporates key physical features of protein cores, to predict how well a computational model recapitulates the real protein structure without knowledge of the structure of the target sequence. By identifying the important features of protein structure, our method is able to rank decoy structures with similar accuracy to that obtained by state‐of‐the‐art methods that incorporate many additional features. The small number of physical features makes our model interpretable, emphasizing the importance of protein packing and hydrophobicity in protein structure prediction.

中文翻译：

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利用蛋白质核心堆积的物理特征来区分真正的蛋白质和诱饵。

始终区分真实蛋白质结构和计算生成的模型诱饵的能力尚未解决。区分真实蛋白质结构和诱饵的一种途径是描绘指定真实蛋白质的重要​​物理特征。例如，人们很早就认识到蛋白质的疏水核心对其稳定性有显着贡献。我们使用两个来源来获取诱饵数据集，以与真实的蛋白质结构进行比较：提交给两年一度的蛋白质结构预测批判性评估竞赛，其中研究人员尝试在仅知道其氨基酸序列的情况下预测蛋白质的结构，以及生成的诱饵由 3DRobot 提供，它具有用户指定的与实验确定的结构的全局均方根偏差。我们的分析表明，两组诱饵都具有不能概括定义真正蛋白质核心的关键特征的核心。特别是，模型结构显得更加密集（由于能量上不利的原子重叠），核心中包含的残基太少，并且整个结构中疏水残基的分布不正确。基于这些观察，我们开发了一种前馈神经网络，它结合了蛋白质核心的关键物理特征，以预测计算模型在不了解目标序列结构的情况下重现真实蛋白质结构的程度。通过识别蛋白质结构的重要特征，我们的方法能够以与包含许多附加特征的最先进方法获得的相似精度对诱饵结构进行排序。少量的物理特征使我们的模型具有可解释性，强调了蛋白质堆积和疏水性在蛋白质结构预测中的重要性。