Poster Presentation The 43rd Lorne Conference on Protein Structure and Function 2018

Using in silico peptide exchange and alanine scanning to identify and characterize protein-protein interaction motifs (#139)

Sobia Idrees 1 , Richard J Edwards 1
  1. School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW, Australia

Most biological processes are mediated through complex networks of transient protein interactions where a globular domain in one protein interacts with a short linear motif (SLiM) on another protein known as Domain Motif Interactions (DMIs). SLiMs are short stretches of amino acids (~3-10) which are involved in post translational modifications (PTMs), protein-protein Interactions (PPIs), cell regulation and cell compartment targeting. These SLiMs correspond to a small contact interface with their interacting domain partners, which makes it challenging to achieve a high prediction specificity. To complement sequence-based approaches, we are developing prediction validation methods using structural modelling. We are combining known DMI data from 3DID (Mosca, Ceol et al. 2014) and ELM (Dinkel, Van Roey et al. 2016) with in silico peptide exchange and alanine scanning experiments using FoldX  (Kiel and Serrano 2014). First, we will establish whether predicted changes in binding affinity for known motifs can be used to discriminate real motif occurrences from non-binding peptide sequences. If successful, we will apply the method to predictions of motifs in viral proteins that interact with human domains via molecular mimicry. The outcome of this analysis can help differentiate real interaction motifs from false positives, and will be useful for initial validation of predicted DMIs. Using predictions of binding affinity changes for different amino acid substitutions to improve motif pattern definitions will also be explored.

  1. Dinkel, H., K. Van Roey, S. Michael, M. Kumar, B. Uyar, B. Altenberg, V. Milchevskaya, M. Schneider, H. Kuhn, A. Behrendt, S. L. Dahl, V. Damerell, S. Diebel, S. Kalman, S. Klein, A. C. Knudsen, C. Mader, S. Merrill, A. Staudt, V. Thiel, L. Welti, N. E. Davey, F. Diella and T. J. Gibson (2016). "ELM 2016-data update and new functionality of the eukaryotic linear motif resource." Nucleic Acids Res 44(D1): D294-300.
  2. Kiel, C. and L. Serrano (2014). "Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations." Mol Syst Biol 10: 727.
  3. Mosca, R., A. Ceol, A. Stein, R. Olivella and P. Aloy (2014). "3did: a catalog of domain-based interactions of known three-dimensional structure." Nucleic Acids Res 42(Database issue): D374-379.