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Conservation helps to identify biologically relevant crystal contacts

机译：保护有助于识别生物学上相关的晶体接触

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Some crystal contacts are biologically relevant, most are not. We assess the utility of combining measures of size and conservation to discriminate between biological and non-biological contacts. Conservation and size information is calculated for crystal contacts in 53 families of homo-dimers and 65 families of monomers. Biological contacts are shown to be usually conserved and typically the largest contact in the crystal. A range of neural networks accepting different combinations and encodings of this information is used to answer the following questions: (1) is a given crystal contact biological, and (2) given all crystal contacts in a homodimer, which is the biological one? Predictions for (1) are performed on both homodimer and monomer datasets. The best performing neural network combined size and conservation inputs. For the homodimers, it correctly classified 48 out of 53 biological contacts and 364 out of 366 nonbiological contacts, giving a combined accuracy of 98.3%. A more robust performance statistic, the phi-coefficient, which accounts for imbalances in the dataset, gave a value of 0.92. Taking all 535 non-biological contacts from the 65 monomers, this predictor made erroneous classifications only 4.3% of the time. Predictions for (2) were performed on homodimers only. The best performing net-work achieved a prediction accuracy of 98.1% using size information alone. We conclude that in answering question (1) size and conservation combined discriminate biological from non-biological contacts better than either measure alone. For answering question (2), we conclude that in our dataset size is so powerful a discriminant that conservation adds little predictive benefit.
机译：一些晶体接触与生物学相关，而大多数与生物学无关。我们评估结合大小和保护措施来区分生物学和非生物学接触的效用。计算了53个同二聚体家族和65个单体家族中晶体接触的守恒和尺寸信息。已显示生物接触通常是保守的，通常是晶体中最大的接触。一系列接受此信息的不同组合和编码的神经网络用于回答以下问题：（1）是给定的晶体接触生物学对象，（2）给出了同型二聚体中的所有晶体接触对象，这是生物学的吗？对（1）的预测在同二聚体和单体数据集上均进行。性能最好的神经网络将大小和守恒输入结合在一起。对于同型二聚体，它对53种生物接触中的48种正确分类，对366种非生物接触中的364种正确分类，综合准确度为98.3％。更加可靠的性能统计数据phi系数可解决数据集中的不平衡问题，其值为0.92。从65个单体中提取所有535个非生物接触，该预测变量仅在4.3％的时间内做出了错误的分类。 （2）的预测仅对同型二聚体进行。仅使用尺寸信息，性能最佳的网络即可达到98.1％的预测精度。我们得出结论，在回答问题（1）时，规模和保护相结合比单独使用任何一种方法更好地将生物与非生物接触区分开。为了回答问题（2），我们得出结论，在我们的数据集中，判别力是如此之大，以至于保守性几乎没有增加预测价值。

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