Coevolution of PDZ domain-ligand interactions analyzed by high-throughput phage display and deep sequencing.

By Andreas M. Ernst, David Gfeller, Zhengyan Kan, S. Seshagiri, Philip M. Kim, Gary D. Bader, S. Sidhu. 2010

132 citations recorded by the source corpus.

The determinants of binding specificities of peptide recognition domains and their evolution remain important problems in molecular systems biology. Here, we present a new methodology to analyze the coevolution between a domain and its ligands by combining high-throughput phage display with deep sequencing. First, from a library of PDZ domains with diversity introduced at ten positions in the binding site, we evolved domains for binding to 15 distinct peptide ligands. Interestingly, for a given peptide many different functional domains emerged, which exhibited only limited sequence homology, showing that many different binding sites can recognize a given peptide. Subsequently, we used peptide-phage libraries and deep sequencing to map the specificity profiles of these evolved domains at high resolution, and we found that the domains recognize their cognate peptides with high affinity but low specificity. Our analysis reveals two aspects of evolution of new binding specificities. First, we were able to identify some common features amongst domains raised against a common peptide. Second, our analysis suggests that cooperative interactions between multiple binding site residues lead to a diversity of binding profiles with considerable plasticity. The details of intramolecular cooperativity remain to be elucidated, but nonetheless, we have established a general methodology that can be used to explore protein evolution in a systematic yet rapid manner.

Subfields: Molecular Biology

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Citations in the corpus

Protein structure prediction from sequence variation (2012, methodology): “Guided evolution or selection in the laboratory is a potentially powerful tool for focused expansion of the sequence repertoire in any particular protein family 57 .”
Enrich: software for analysis of protein function by enrichment and depletion of variants (2011, methodology): “Availability and Implementation: Enrich is implemented in Python and is available under a FreeBSD license at http://depts .washington.edu/sfields/software/enrich/.”
Measuring the activity of protein variants on a large scale using deep mutational scanning (2014, methodology): “8| Amplify DNA from each library by preparing the reaction mixture detailed in the in-text table below and implementing the PCR program described in the table that follows.”
Methods in Enzymology: “Flexible backbone sampling methods to model and design protein alternative conformations” (2013, unknown): “Additionally, as more datasets on mutant binding domains are collected , there is potential to look for covariation between the sequences tolerated between receptor and peptide positions (Ernst et al., 2010).”