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InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network

Isak Johansson-Åkhe, Claudio Mirabello, Björn Wallner
doi: https://doi.org/10.1101/2020.09.07.285957
Isak Johansson-Åkhe
1Linköpings Universitet
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Claudio Mirabello
2Linköping University
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Björn Wallner
2Linköping University
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  • For correspondence: bjorn.wallner@liu.se
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Abstract

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/InterPepRank

Contact Björn Wallner bjorn.wallner{at}liu.se

Supplementary information Supplementary data are available at BioRxiv online.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Test-set greatly expanded, affecting Figures 3 and 4, with minor additions to Results and Discussion as necessary. Sections throughout the paper, especially regarding Methods, have been clarified.

  • https://doi.org/10.17044/scilifelab.13134756

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted October 23, 2020.
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InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Isak Johansson-Åkhe, Claudio Mirabello, Björn Wallner
bioRxiv 2020.09.07.285957; doi: https://doi.org/10.1101/2020.09.07.285957
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InterPepRank: Assessment of Docked Peptide Conformations by a Deep Graph Network
Isak Johansson-Åkhe, Claudio Mirabello, Björn Wallner
bioRxiv 2020.09.07.285957; doi: https://doi.org/10.1101/2020.09.07.285957

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