Motivation

Protein-protein interactions are important for many biological processes. Theoretical understanding of the structurally determining factors of interaction sites will help to understand the underlying mechanism of protein-protein interactions. Taking advantage of advanced mathematical methods to correctly predict interaction sites will be useful. Although some previous studies have been devoted to the interaction interface of protein monomer and the interface residues between chains of protein dimers, very few studies about the interface residues prediction of protein multimers, including trimers, tetramer and even more monomers in a large protein complex. As we all know, a large number of proteins function with the form of multibody protein complexes. And the complexity of the protein multimers structure causes the difficulty of interface residues prediction on them. So, we hope to build a method for the prediction of protein tetramer interface residue pairs.

Results

Here, we developed a new deep network based on LSTM network combining with graph to predict protein tetramers interaction interface residue pairs. On account of the protein structure data is not the same as the image or video data which is well-arranged matrices, namely the Euclidean Structure mentioned in many researches. Because the Non-Euclidean Structure data can't keep the translation invariance, and we hope to extract some spatial features from this kind of data applying on deep learning, an algorithm combining with graph was developed to predict the interface residue pairs of protein interactions based on a topological graph building a relationship between vertexes and edges in graph theory combining multilayer Long Short-Term Memory network. First, selecting the training and test samples from the Protein Data Bank, and then extracting the physicochemical property features and the geometric features of surface residue associated with interfacial properties. Subsequently, we transform the protein multimers data to topological graphs and predict protein interaction interface residue pairs using the model. In addition, different types of evaluation indicators verified its validity.

中文翻译：

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用LSTM结合图表示法预测四聚体蛋白质复合物界面残基对。

动机

蛋白质-蛋白质相互作用对许多生物学过程都很重要。对相互作用位点的结构决定因素的理论理解将有助于理解蛋白质相互作用的潜在机制。利用先进的数学方法正确预测相互作用部位将很有用。尽管一些先前的研究致力于蛋白质单体的相互作用界面和蛋白质二聚体链之间的界面残基，但很少有关于蛋白质多聚体的界面残基预测的研究，包括大型蛋白质复合物中的三聚体，四聚体甚至更多单体。众所周知，大量蛋白质以多体蛋白质复合物的形式起作用。蛋白质多聚体结构的复杂性导致难以预测它们的界面残基。因此，我们希望建立一种预测蛋白质四聚体界面残基对的方法。

结果

在这里，我们开发了一个基于LSTM网络的新深度网络，并与图形结合以预测蛋白质四聚体相互作用界面残基对。由于蛋白质的结构数据与图像或视频数据不同，它们是矩阵的良好排列，即许多研究中提到的欧几里德结构。由于非欧结构结构数据无法保持翻译不变性，并且我们希望在深度学习中从此类数据中提取一些空间特征，因此开发了一种结合图的算法来预测蛋白质相互作用的界面残基对结合多层长短期记忆网络的图论中建立顶点和边之间关系的拓扑图的研究。首先，从蛋白质数据库中选择训练和测试样本，然后提取与界面性质有关的表面残留物的理化性质特征和几何特征。随后，我们将蛋白质多聚体数据转换为拓扑图，并使用该模型预测蛋白质相互作用界面残基对。此外，不同类型的评估指标也证明了其有效性。