PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features
PDA-Pred: Predicting the binding affinity of protein-DNA complexes using machine learning techniques and structural features
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DOI：
10.1016/j.ymeth.2023.03.002
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发表时间：
2023-03-23
期刊：
影响因子：
4.8
通讯作者：
Gromiha, M. Michael
中科院分区：
文献类型：
--
作者：
Harini, K.;Kihara, Daisuke;Gromiha, M. Michael
蛋白质-DNA相互作用在基因表达、复制和转录等多种生物学过程中发挥着重要作用。了解决定蛋白质-DNA复合物结合亲和力的重要特征并预测它们的亲和力对于阐明它们的识别机制是重要的。在这项工作中，我们收集了一组391蛋白质-DNA复合物的实验结合自由能（Δ G），并推导出几个基于结构的功能，如相互作用能，接触电位，结合位点残基的体积和表面积，DNA的基础步骤参数和不同类型的原子之间的接触。结合亲和力与蛋白质结构特征之间的关系分析表明，重要的因素主要取决于DNA链的数目以及蛋白质的功能和结构类别。具体而言，结合位点的性质，如DNA和蛋白质之间的原子接触的数量，蛋白质结合位点的体积和基于相互作用的特征，如相互作用能和接触电位是重要的理解的结合亲和力。此外，我们开发了多元回归方程，用于预测属于不同结构和功能类别的蛋白质-DNA复合物的结合亲和力。我们的方法表明，平均相关性和平均绝对误差分别为0.78和0.98千卡/摩尔，实验和预测的结合亲和力之间的折刀测试。我们已经开发了一个网络服务器PDA-PreD（蛋白质DNA结合亲和力预测器），用于预测蛋白质-DNA复合物的亲和力，它可以在https://web.iitm.ac.in/bioinfo2/pdapred/上免费获得
Protein-DNA interactions play an important role in various biological processes such as gene expression, replication, and transcription. Understanding the important features that dictate the binding affinity of protein DNA complexes and predicting their affinities is important for elucidating their recognition mechanisms. In this work, we have collected the experimental binding free energy (Delta G) for a set of 391 Protein-DNA complexes and derived several structure-based features such as interaction energy, contact potentials, volume and surface area of binding site residues, base step parameters of the DNA and contacts between different types of atoms. Our analysis on relationship between binding affinity and structural features revealed that the important factors mainly depend on the number of DNA strands as well as functional and structural classes of proteins. Specifically, binding site properties such as number of atom contacts between the DNA and protein, volume of protein binding sites and interaction-based features such as interaction energies and contact potentials are important to un-derstand the binding affinity. Further, we developed multiple regression equations for predicting the binding affinity of protein-DNA complexes belonging to different structural and functional classes. Our method showed an average correlation and mean absolute error of 0.78 and 0.98 kcal/mol, respectively, between the experi-mental and predicted binding affinities on a jack-knife test. We have developed a webserver, PDA-PreD (Protein DNA Binding affinity predictor), for predicting the affinity of protein-DNA complexes and it is freely available at https://web.iitm.ac.in/bioinfo2/pdapred/