Protein Interactions

Modeling specificity of peptide recognition domain families

People involved: Erik Verschueren

Keywords: protein-peptide interactions, signaling pathways, systems biology, protein structure

Modular protein interaction domains form the cornerstone of eukaryotic signaling pathways. These domain families have adapted themselves through evolution to selectively recognize short linear polypeptide motifs in other proteins.

In my work I study how two peptide recognition domains, the PDZ domain and SH3 domain, have optimized their specificity landscape to minimize cross-reactivity while retaining the core binding motifs that characterize them as a family.

Recent developments in high-throughput binding assays have uncovered the complexity of these recognition domains, making these methods the tool of choice for specificity studies. Nevertheless, we demonstrate that three-dimensional structures of protein-peptide interactions are a valuable, complementary resource to truly understand the subtle interface changes that enable these domains to selectively bind their ligands. Unfortunately, structural coverage of peptide recognition modules in complex with their ligands is low and computational algorithms have not fully tackled the challenges of peptide structure prediction yet.

We propose the use of large libraries of polypeptide fragments to improve peptide prediction methods. We recently found that the majority of protein-peptide architectures can be represented as interactions between fragments in single, unrelated structures and developed an algorithm that predicts the structure of peptides bound to their partner using these interaction patterns. We project that fragment-based structure prediction methods hold great promise to further explore, understand the complexity and ultimately engineer protein-peptide interactions in signaling pathways.