Fast and accurate genome-wide predictions and structural modeling of protein-protein interactions using Galaxy

Abstract

Background: Protein-protein interactions play a crucial role in almost all cellular processes. Identifying interacting proteins reveals insight into living organisms and yields novel drug targets for disease treatment. Here, we present a publicly available, automated pipeline to predict genome-wide protein-protein interactions and produce high-quality multimeric structural models.

Results: Application of our method to the Human and Yeast genomes yield protein-protein interaction networks similar in quality to common experimental methods. We identified and modeled Human proteins likely to interact with the papain-like protease of SARS-CoV2's non-structural protein 3. We also produced models of SARS-CoV2's spike protein (S) interacting with myelin-oligodendrocyte glycoprotein receptor and dipeptidyl peptidase-4.

Conclusions: The presented method is capable of confidently identifying interactions while providing high-quality multimeric structural models for experimental validation. The interactome modeling pipeline is available at usegalaxy.org and usegalaxy.eu.

Keywords: Galaxy workflow; Protein–protein interactions; Structural modeling.

Fig. 1

Human (left) and Yeast (right) protein–protein prediction results and comparison. Showing Matthew’s correlation coefficients (MCCs) as produced by comparing SPRING predictions with different protein–protein interaction experiments available in the BioGRID database. Each experimental method serves as a positive validation set for every other method

Fig. 2

Putative ubiquitin-like substrates (colored) of SARS-CoV2 papain-like protease (green)

Fig. 3

Putative binding modes of SARS-CoV2 receptor-binding domain (green) and A the top-ranking model of myelin-oligodendrocyte glycoprotein (cyan) with the homologue template of PDB entry 7C8V (pink) and B a cluster of secondary models (orange). C Display of both binding modes in complex with SARS-CoV2 receptor-binding domain

Fig. 4

Putative binding mode between SARS-CoV2 (S) receptor-binding domain (RBD) (navy) and dipeptidyl peptidase-4 (DPP4) (cyan) with known MERS-CoV and DPP4 critical binding sites highlighted (red). The multimeric template framework is PDB entry 4L72 (pink), with the MERS-CoV receptor-binding domain (RBD) (orange)

Fig. 5

Schematic overview of the presented pipeline, illustrating the main input data sets, tools and outputs