Department of Information Engineering, University of Padua, Via Gradenigo 6/A, 35131, Padua, Italy.
School of Science and Tecnology, University of Camerino, Via Madonna delle Carceri, 8, 62032, Camerino, Italy.
BMC Bioinformatics. 2022 Mar 20;23(1):96. doi: 10.1186/s12859-022-04624-y.
Protein-protein interactions have pivotal roles in life processes, and aberrant interactions are associated with various disorders. Interaction site identification is key for understanding disease mechanisms and design new drugs. Effective and efficient computational methods for the PPI prediction are of great value due to the overall cost of experimental methods. Promising results have been obtained using machine learning methods and deep learning techniques, but their effectiveness depends on protein representation and feature selection.
We define a new abstraction of the protein structure, called hierarchical representations, considering and quantifying spatial and sequential neighboring among amino acids. We also investigate the effect of molecular abstractions using the Graph Convolutional Networks technique to classify amino acids as interface and no-interface ones. Our study takes into account three abstractions, hierarchical representations, contact map, and the residue sequence, and considers the eight functional classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0. The performance of our method, evaluated using standard metrics, is compared to the ones obtained with some state-of-the-art protein interface predictors. The analysis of the performance values shows that our method outperforms the considered competitors when the considered molecules are structurally similar.
The hierarchical representation can capture the structural properties that promote the interactions and can be used to represent proteins with unknown structures by codifying only their sequential neighboring. Analyzing the results, we conclude that classes should be arranged according to their architectures rather than functions.
蛋白质-蛋白质相互作用在生命过程中起着关键作用，异常相互作用与各种疾病有关。相互作用位点的识别是理解疾病机制和设计新药的关键。由于实验方法的总成本，有效的和高效的用于预测 PPI 的计算方法具有很大的价值。使用机器学习方法和深度学习技术已经取得了有希望的结果，但它们的有效性取决于蛋白质表示和特征选择。
我们定义了一种蛋白质结构的新抽象，称为层次表示，考虑并量化了氨基酸之间的空间和顺序邻近性。我们还使用图卷积网络技术研究了分子抽象的效果，以将氨基酸分类为界面和非界面。我们的研究考虑了三种抽象，层次表示、接触图和残基序列，并考虑了从蛋白质-蛋白质对接基准 5.0 中提取的八种功能类别的蛋白质。使用标准指标评估我们方法的性能，并与一些最先进的蛋白质界面预测器获得的性能进行比较。对性能值的分析表明，当考虑的分子在结构上相似时，我们的方法优于考虑的竞争对手。
层次表示可以捕获促进相互作用的结构特性，并且可以通过仅对其顺序邻近性进行编码来表示具有未知结构的蛋白质。通过分析结果，我们得出结论，应该根据它们的结构而不是功能来安排类。
