SpatialPPI: three-dimensional space protein-protein interaction prediction with AlphaFold Multimer

Abstract

The rapid advancement of protein sequencing technology has resulted in a gap 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. On the other hand, structure-based methods face challenges with newly sequenced proteins. AlphaFold emerges as a potential solution, especially in predicting protein-protein interactions. This research delves into using deep neural networks, specifically analyzing protein complex structures as predicted by AlphaFold Multimer. By transforming atomic coordinates and utilizing sophisticated image processing techniques, the study extracts vital 3D structural details from protein complexes. Recognizing the significance of evaluating residue distances in protein interactions, the study leverages image recognition approaches, notably integrating DenseNet and ResNet within 3D convolutional networks for protein 3D structure analysis. When benchmarked against leading protein-protein interaction prediction methods like SpeedPPI, D-script, DeepTrio, and PEPPI, our proposed method, named SpatialPPI, exhibits notable efficacy, emphasizing the promising role of 3D spatial processing in advancing the realm of structural biology. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/571766v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@14fa761org.highwire.dtl.DTLVardef@761293org.highwire.dtl.DTLVardef@1bd9508org.highwire.dtl.DTLVardef@1837a8e_HPS_FORMAT_FIGEXP M_FIG C_FIG