Quantification of PDZ domain specificity, prediction of ligand affinity and rational design of super-binding peptides

Abstract

Transient macromolecular complexes are often formed by protein-protein interaction domains (e.g. PDZ, SH2, SH3, WW) which recognize linear sequence motifs with in vitro affinities typically in the micromolar range. The analysis of the resulting interaction networks requires a quantification of domain specificity and selectivity towards all possible ligands with physiologically relevant affinity. As representative examples, we determined specificity as a function of ligand sequence-dependent affinity contributions by statistical analysis of peptide library screens for the AF6, ERBIN and SNA1 (alpha-1-syntrophin) PDZ domains. For this purpose, the three PDZ domains were first screened for binding with a peptide library comprising 6223 human C termini created by SPOT synthesis. Based on the detected ligand preferences, we designed focused peptide libraries (profile libraries). These libraries were used to quantify the affinity contributions of the four C-terminal ligand residues by means of ANOVA models (analysis of variance) relating the C-terminal ligand sequences to the corresponding dissociation constants. Our models agreed well with experimentally determined dissociation constants and allowed us to design super binding peptides. The latter were shown experimentally to bind to their cognate PDZ domains with the highest affinity. In addition, we determined structure-activity relationships and thereby rationalized the position-specific affinity contributions. Furthermore, we used the statistical models to predict the dissociation constants for the complete ligand sequence space and thus determined the specificity overlap for the three investigated PDZ domains (). Altogether, we present an efficient method for profiling protein-protein interaction domains that provides a biophysical picture of specificity and selectivity. This approach allows the rational design of functional experiments and provides a basis for simulating interaction networks in the field of systems biology.