Large-scale docking predicts that sORF-encoded peptides may function through protein-peptide interactions in Arabidopsis thaliana

PLoS One. 2018 Oct 15;13(10):e0205179. doi: 10.1371/journal.pone.0205179. eCollection 2018.

Abstract

Several recent studies indicate that small Open Reading Frames (sORFs) embedded within multiple eukaryotic non-coding RNAs can be translated into bioactive peptides of up to 100 amino acids in size. However, the functional roles of the 607 Stress Induced Peptides (SIPs) previously identified from 189 Transcriptionally Active Regions (TARs) in Arabidopsis thaliana remain unclear. To provide a starting point for functional annotation of these plant-derived peptides, we performed a large-scale prediction of peptide binding sites on protein surfaces using coarse-grained peptide docking. The docked models were subjected to further atomistic refinement and binding energy calculations. A total of 530 peptide-protein pairs were successfully docked. In cases where a peptide encoded by a TAR is predicted to bind at a known ligand or cofactor-binding site within the protein, it can be assumed that the peptide modulates the ligand or cofactor-binding. Moreover, we predict that several peptides bind at protein-protein interfaces, which could therefore regulate the formation of the respective complexes. Protein-peptide binding analysis further revealed that peptides employ both their backbone and side chain atoms when binding to the protein, forming predominantly hydrophobic interactions and hydrogen bonds. In this study, we have generated novel predictions on the potential protein-peptide interactions in A. thaliana, which will help in further experimental validation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Arabidopsis / metabolism*
  • Arabidopsis Proteins / metabolism*
  • Hydrogen Bonding
  • Hydrophobic and Hydrophilic Interactions
  • Molecular Docking Simulation
  • Molecular Dynamics Simulation
  • Open Reading Frames*
  • Peptides / metabolism*
  • Protein Binding*

Substances

  • Arabidopsis Proteins
  • Peptides

Grants and funding

This work has been supported by the KU Leuven Research Fund. NS is a doctoral fellow (1112318N) of the Research Foundation – Flanders (FWO). The computational resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation – Flanders (FWO) and the Flemish Government – department EWI.