Rapid advancements in protein sequencing technology have resulted in gaps between proteins with identified sequences and those with mapped structures. Although sequence-based predictions offer insights, they can be incomplete due to the absence of structural details. Conversely, structure-based methods face challenges with respect to newly sequenced proteins. The AlphaFold Multimer has remarkable accuracy in predicting the structure of protein complexes. However, it cannot distinguish whether the input protein sequences can interact. Nonetheless, by analyzing the information in the models predicted by the AlphaFold Multimer, we propose a highly accurate method for predicting protein interactions. This study focuses on the use of deep neural networks, specifically to analyze protein complex structures predicted by the AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image-processing techniques, vital 3D structural details were extracted from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, this study leveraged image recognition approaches by integrating Densely Connected Convolutional Networks (DenseNet) and Deep Residual Network (ResNet) within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods, such as SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibited notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. The SpatialPPI code is available at: .

中文翻译：

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SpatialPPI：使用 AlphaFold Multimer 进行三维空间蛋白质-蛋白质相互作用预测

蛋白质测序技术的快速进步导致了已识别序列的蛋白质与已绘制结构的蛋白质之间的差距。尽管基于序列的预测提供了见解，但由于缺乏结构细节，它们可能不完整。相反，基于结构的方法面临着新测序蛋白质的挑战。 AlphaFold Multimer 在预测蛋白质复合物的结构方面具有极高的准确性。然而，它无法区分输入的蛋白质序列是否可以相互作用。尽管如此，通过分析 AlphaFold Multimer 预测的模型中的信息，我们提出了一种高度准确的方法来预测蛋白质相互作用。本研究重点关注深度神经网络的使用，特别是分析 AlphaFold Multimer 预测的蛋白质复杂结构。通过转换原子坐标并利用复杂的图像处理技术，从蛋白质复合物中提取了重要的 3D 结构细节。认识到评估蛋白质相互作用中残基距离的重要性，本研究通过在 3D 卷积网络中集成密集连接卷积网络 (DenseNet) 和深度残差网络 (ResNet)，利用图像识别方法进行蛋白质 3D 结构分析。当与 SpeedPPI、D-script、DeepTrio 和 PEPPI 等领先的蛋白质-蛋白质相互作用预测方法进行基准测试时，我们提出的名为 SpatialPPI 的方法表现出显着的功效，强调了 3D 空间处理在推进结构生物学领域的前景。 。 SpatialPPI 代码可从以下位置获取： 。