PreDBA：一种用于预测蛋白质-DNA结合亲和力的异质集成方法。,Scientific Reports

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PreDBA：一种用于预测蛋白质-DNA结合亲和力的异质集成方法。
Scientific Reports ( IF 3.8 ) Pub Date : 2020-01-28 , DOI: 10.1038/s41598-020-57778-1
Wenyi Yang ₁ , Lei Deng _{1,

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Affiliation

蛋白质与DNA之间的相互作用在各种关键的自然过程中起着至关重要的作用，例如DNA复制，转录，剪接和修复。研究蛋白质与DNA的结合亲和力有助于了解蛋白质-DNA复合物的识别机制。由于通过实验测量的蛋白质-DNA结合亲和力数据仍然存在许多局限性，因此需要准确而可靠的计算方法。因此，我们在本文中提出了一种称为PreDBA的计算方法，该方法可以通过使用异类集成模型有效地预测蛋白质与DNA的结合亲和力。从相关文献中手动收集了一百种蛋白质-DNA复合物，作为蛋白质-DNA结合亲和力的数据集。然后，获得52个序列和结构特征。基于此，计算出这52个特征与蛋白质-DNA结合亲和力之间的相关性。此外，我们发现蛋白质与DNA的结合亲和力受化合物DNA分子结构的影响。根据与构成蛋白质-DNA复合物的蛋白质相关的DNA结构，我们将所有蛋白质-DNA化合物分为五类。在每个组中，基于获得的特征构建堆叠的异类集成模型。最后，基于绑定亲和力数据集，我们使用了留一法交叉验证对所提出的方法进行了全面评估。在这五个类别中，我们推荐的方法的Pearson相关系数值在0.735至0.926之间。

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PreDBA: A heterogeneous ensemble approach for predicting protein-DNA binding affinity.

The interaction between protein and DNA plays an essential function in various critical natural processes, like DNA replication, transcription, splicing, and repair. Studying the binding affinity of proteins to DNA helps to understand the recognition mechanism of protein-DNA complexes. Since there are still many limitations on the protein-DNA binding affinity data measured by experiments, accurate and reliable calculation methods are necessarily required. So we put forward a computational approach in this paper, called PreDBA, that can forecast protein-DNA binding affinity effectively by using heterogeneous ensemble models. One hundred protein-DNA complexes are manually collected from the related literature as a data set for protein-DNA binding affinity. Then, 52 sequence and structural features are obtained. Based on this, the correlation between these 52 characteristics and protein-DNA binding affinity is calculated. Furthermore, we found that the protein-DNA binding affinity is affected by the DNA molecule structure of the compound. We classify all protein-DNA compounds into five classifications based on the DNA structure related to the proteins that make up the protein-DNA complexes. In each group, a stacked heterogeneous ensemble model is constructed based on the obtained features. In the end, based on the binding affinity data set, we used the leave-one-out cross-validation to evaluate the proposed method comprehensively. In the five categories, the Pearson correlation coefficient values of our recommended method range from 0.735 to 0.926. We have demonstrated the advantages of the proposed method compared to other machine learning methods and currently existing protein-DNA binding affinity prediction approach.

更新日期：2020-01-29

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