Protein-protein interactions are central to cell function. Numerous studies have provided large-scale information on human protein-protein interactions. However, many interactions remain to be discovered, and low affinity, conditional and cell type-specific interactions disproportionately under-represented. I will present our efforts towards finding the short linear motif (SLiM)-based interactions of a variety of protein domains. I will present an optimized proteomic peptide-phage display (ProP-PD) library that tiles all disordered regions of the human proteome and allows the screening of ~1,000,000 overlapping peptides in a single binding assay, and the tools and guidelines we have defined for processing the data (1). Using the approach we identified gt;2,000 interaction pairs for 35 known SLiM-binding domains and confirmed the quality of the produced data by complementary biophysical or cell-based assays. The amino acid resolution binding site information can be used to pin-point functionally important disease mutations and phosphorylation events in intrinsically disordered regions of the proteome. Finally, I will describe how we developed the approach for large-scale discovery of coronavirus-host factor protein interactions (2). We screened more than 130 protein domains for binding to viral peptides, and translated the high-resolution information on direct virus-host interactions to a specific peptide-based antiviral inhibitor of an interaction between the G3BP1/2 proteins and an ΦxFG peptide motif in the SARS-CoV-2 nucleocapsid (N) protein. ProP-PD may thus be used both to illuminate the motif-based part of the interactome and to uncover leads for innovative inhibitor design.
References: Benz et al., (2022) Proteome-scale mapping of binding sites in the unstructured regions of the human proteome. Mol Syst Biol. 2022 Jan;18(1):e10584. Kruse et al., (2021) Large scale discovery of coronavirus-host factor protein interaction motifs reveals SARS-CoV-2 specific mechanisms and vulnerabilities. Nat Commun. 2021 Nov 19;12(1):6761.
Support or Funding Information
This work was supported by the Swedish Foundation for Strategic research (SB16-0039) and the Swedish Research Council (2020-03380).
