Enhanced prediction of Src homology 2 (SH2) domain binding potentials using a fluorescence polarization-derived c-Met, c-Kit, ErbB, and androgen receptor interactome

Abstract

Many human diseases are associated with aberrant regulation of phosphoprotein signaling networks. Src homology 2 (SH2) domains represent the major class of protein domains in metazoans that interact with proteins phosphorylated on the amino acid residue tyrosine. Although current SH2 domain prediction algorithms perform well at predicting the sequences of phosphorylated peptides that are likely to result in the highest possible interaction affinity in the context of random peptide library screens, these algorithms do poorly at predicting the interaction potential of SH2 domains with physiologically derived protein sequences. We employed a high throughput interaction assay system to empirically determine the affinity between 93 human SH2 domains and phosphopeptides abstracted from several receptor tyrosine kinases and signaling proteins. The resulting interaction experiments revealed over 1000 novel peptide-protein interactions and provided a glimpse into the common and specific interaction potentials of c-Met, c-Kit, GAB1, and the human androgen receptor. We used these data to build a permutation-based logistic regression classifier that performed considerably better than existing algorithms for predicting the interaction potential of several SH2 domains.

Fig. 1.

Recruitment profiles for GAB1, MET, KIT, and AR. Comprehensive SH2 domain recruitment potential of the adaptor protein GAB1, the MET and KIT receptor tyrosine kinases, and the human AR as determined by high throughput fluorescence polarization. Color-coded heat maps represent K_D values for FP interactions between SH2 and PTB domains and phosphopeptides representing all potential phosphotyrosine sites for which a peptide could be successfully synthesized. Black boxes indicate interactions that are too weak to be detected by the assay. Sequences of peptides used are indicated for each receptor site, where d denotes the pre-charged Asp residue on the peptide synthesis resin and not a naturally occurring Asp. NS refers to peptides that were unable to be synthesized. NI refers to synthesized peptides that produced no positive hits in the study; therefore, we cannot confirm nor deny interactions at these sites with our assay. Rows of the heat maps for these peptides are grayed out to indicate that our FP assay could neither confirm nor deny positive or negative interactions from these peptides.

Fig. 2.

Comparison of recruitment profiles for MET as determined by protein microarrays versus fluorescence polarization. Color-coded heat maps represent K_D values for FP interactions between SH2 and PTB domains and phosphopeptides representing all potential phosphotyrosine sites for which a peptide could be successfully synthesized in previously published protein microarray studies as well as this study. Black boxes indicate interactions that are too weak to be detected by the assay. Sequences of peptides used are indicated for each receptor site, where d denotes the pre-charged aspartic acid residue on the peptide synthesis resin and not a naturally occurring Asp. NS refers to peptides that were unable to be synthesized or, in the case of the protein microarray study, not queried at all. NI refers to synthesized peptides that produced no positive hits in the respective studies; therefore, we cannot confirm nor deny interactions at these sites with either assay. Rows of the heat maps for these peptides are grayed out to indicate that the protein microarray assay or our FP assay could neither confirm nor deny positive or negative interactions from these peptides.

Fig. 3.

Relative binding energy and site enrichment for SH2 and PTB domains by signaling proteins. A, relative binding free energies of interactions described by FP were summed across each receptor or adaptor protein. B, each receptor or adaptor was assessed for enrichment or depletion of binding sites for a given SH2 or PTB domain and is depicted by Z-score transformation of raw data.

Fig. 4.

Comparison of the recruitment of proteins representing different molecular function categories. A, relative binding free energies of interactions described by FP for the ErbB family, GAB1, MET, KIT, and AR were summed across all domains in each listed ontology and then divided by the number of domains in that ontology to determine an average recruitment potential for a particular molecular function group. B, each receptor or adaptor was assessed for enrichment or depletion of binding sites for a given ontology. Data are depicted by Z-score transforming the observed number of binding sites each receptor/adaptor had for a particular ontology relative to the average number of sites that bound the ontology across all receptors/adaptors assessed in our FP assay.

Fig. 5.

Phosphosite contribution on GAB1, MET, KIT, and AR for the recruitment of molecular function groups. Relative binding free energies were calculated for each phosphosite and individual protein domain and then summed according to the classification of the domain in each molecular functional ontology group. The binding energy for the ontology at each site was subsequently divided by the total binding energy summed across the entire receptor or adaptor protein and presented as a percentage of total binding activity of that receptor or adaptor.

Fig. 6.

Residue enrichment at positions relative to tyrosine that contribute to the recruitment of SH2 domains. A, representative plots depict log₁₀ transforms of the p values of each residue at each position relative to 100 permutations. The number of peptides that interacted with each domain is indicated at the top of each plot. B, ROC curve obtained by plotting the false-positive rate (1 − Specificity, x axis) by the true-positive rate (Sensitivity, y axis) for PEBL predictions of our FP interaction data. C, histogram depicting the number of SH2 domains for which each position relative to phosphotyrosine was selected as an important variable for binary interaction classification by random forests.