TY - JOUR T1 - InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network JF - bioRxiv DO - 10.1101/2020.09.07.285957 SP - 2020.09.07.285957 AU - Isak Johansson-Åkhe AU - Claudio Mirabello AU - Björn Wallner Y1 - 2020/01/01 UR - http://biorxiv.org/content/early/2020/10/23/2020.09.07.285957.abstract N2 - Motivation Peptide-protein interactions between a smaller or disordered peptide stretch and a folded receptor make up a large part of all protein-protein interactions. A common approach for modelling such interactions is to exhaustively sample the conformational space by fast-fourier-transform docking, and then refine a top percentage of decoys. Commonly, methods capable of ranking the decoys for selection in short enough time for larger scale studies rely on first-principle energy terms such as electrostatics, Van der Waals forces, or on pre-calculated statistical pairwise potentials.Results We present InterPepRank for peptide-protein complex scoring and ranking. InterPepRank is a machine-learning based method which encodes the structure of the complex as a graph; with physical pairwise interactions as edges and evolutionary and sequence features as nodes. The graph-network is trained to predict the LRMSD of decoys by using edge-conditioned graph convolutions on a large set of peptide-protein complex decoys. InterPepRank is tested on a massive independent test set with no targets sharing CATH annotation nor 30% sequence identity with any target in training or validation data. On this set, InterPepRank has a median AUC of 0.86 for finding coarse peptide-protein complexes with LRMSD<4Å. This is an improvement compared to other state-of-the-art ranking methods that have a median AUC of circa 0.69. When included as selection-method for selecting decoys for refinement in a previously established peptide docking pipeline, InterPepRank improves the number of Medium and High quality models produced by 80% and 40%, respectively.Availability The program is available from: http://wallnerlab.org/InterPepRankContact Björn Wallner bjorn.wallner{at}liu.seSupplementary information Supplementary data are available at BioRxiv online.Competing Interest StatementThe authors have declared no competing interest. ER -