Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.09.17.301200v1?rss=1 Authors: Baranwal, M., Magner, A., Saldinger, J., Turali-Emre, E. S., Kozarekar, S., Elvati, P., VanEpps, J. S., Kotov, N., Violi, A., Hero, A. O. Abstract: Development of new methods for analysis of protein-protein interactions (PPIs) at molecular and nanometer scales gives insights into intracellular signaling pathways and will improve understanding of protein functions, as well as other nanoscale structures of biological and abiological origins. Recent advances in computational tools, particularly the ones involving modern deep learning algorithms, have been shown to complement experimental approaches for describing and rationalizing PPIs. However, most of the existing works on PPI predictions use protein-sequence information, and thus have difficulties in accounting for the three-dimensional organization of the protein chains. In this study, we address this problem and describe a PPI analysis method based on a graph attention network, named Struct2Graph , for identifying PPIs directly from the structural data of folded protein globules. Our method is capable of predicting the PPI with an accuracy of 98.89% on the balanced set consisting of an equal number of positive and negative pairs. On the unbalanced set with the ratio of 1:10 between positive and negative pairs, Struct2Graph achieves a five-fold cross validation average accuracy of 99.42%. Moreover, unsupervised prediction of the interaction sites by Struct2Graph for phenol-soluble modulins are found to be in concordance with the previously reported binding sites for this family. Copy rights belong to original authors. Visit the link for more info