The Development and Application of a Quantitative Peptide Microarray Based Approach to Protein Interaction Domain Specificity Space

Protein interaction domain (PID) linear peptide motif interactions direct diverse cellular processes in a specific and coordinated fashion. PID specificity, or the interaction selectivity derived from affinity preferences between possible PID-peptide pairs is the basis of this ability. Here, we develop an integrated experimental and computational cellulose peptide conjugate microarray (CPCMA) based approach for the high throughput analysis of PID specificity that provides unprecedented quantitative resolution and reproducibility. As a test system, we quantify the specificity preferences of four Src Homology 2 domains and 124 physiological phosphopeptides to produce a novel quantitative interactome. The quantitative data set covers a broad affinity range, is highly precise, and agrees well with orthogonal biophysical validation, in vivo interactions, and peptide library trained algorithm predictions. In contrast to preceding approaches, the CPCMAs proved capable of confidently assigning interactions into affinity categories, resolving the subtle affinity contributions of residue correlations, and yielded predictive peptide motif affinity matrices. Unique CPCMA enabled modes of systems level analysis reveal a physiological interactome with expected node degree value decreasing as a function of affinity, resulting in minimal high affinity binding overlap between domains; uncover that Src Homology 2 domains bind ligands with a similar average affinity yet strikingly different levels of promiscuity and binding dynamic range; and parse with unprecedented quantitative resolution contextual factors directing specificity. The CPCMA platform promises broad application within the fields of PID specificity, synthetic biology, specificity focused drug design, and network biology.Protein interaction domains (PIDs)¹ often compete for the same linear motif binding sites across a range of affinities, resulting in many potential interactions that may enable the rapid assembly and disassembly of signaling proteins in response to external and internal cues (1, 2). PID-peptide interactions have small binding interfaces, resulting in moderate affinity interactions mediated primarily by a few amino acid “hot-spots” within motifs specific for a particular PID family (3–5). The power of individual residues to direct interactions, the absence of structural constraint for linear motifs, and the modularity of PIDs has enabled the rapid evolution of these networks resulting in many large multimember PID families in higher eukaryotes (6–9). For these large families dedicated to the recognition of similar ligands, PID specificity—or the interaction selectivity derived from affinity preferences between possible PID-peptide pairs—underpins the effective conveyance of specific cell signals. High throughput interaction mapping efforts are used to decipher how this PID “specificity space” is populated, thereby providing insight into protein function and the principles of network architecture and evolution (10–15). The extent of binding overlap or interaction promiscuity within and between PID families for physiological ligands, the affinity range of overlapping interactions, and the biological relevancy of these interactions are important questions thus far poorly resolved by existing high throughput methods. Here, we develop and apply a quantitative high throughput method capable of addressing these questions.Peptide arrays (16, 17), degenerate libraries (18, 19), and phage display (20) are the most frequently applied high throughput approaches for investigating PID specificity. Phage display and degenerate library approaches sample a large ligand space and can produce consensus selectivity motifs that represent the most preferred residues at every position panned. This selectivity data is used to predict interactions, often via position specific scoring matrices (PSSMs) (21–23). However, neither approach can explicitly measure nonbinding events and only large phage display data sets can resolve a limited subset of high-affinity contextual binding information (24). Nonbinding information and contextual interplay, that is, correlated contributions between ligand positions to binding affinity, play important roles in defining the specificity landscapes for multiple PID families (25–27). Having explicit nonbinding or low-affinity information available helps uncover contextual binding information, and improves the accuracy of interaction priority assignment between multiple competing PIDs. Correspondingly, the availability of nonbinding and contextual information improves interaction prediction performance (28, 29). Peptide arrays using physiological ligands do not have these limitations, yet may under-sample PID specificity space because of smaller library sizes. Newly emerging ultrahigh density peptide arrays avoid this particular limitation and are capable of sampling the entire proteome (30). However, a common limitation for all of these techniques is their dependence on nonquantitative interaction information.A comprehensive understanding of PID specificity space requires the quantitative assessment of pairwise interactions across a broad dynamic range of affinities. Common low-throughput biophysical techniques used to measure protein-peptide interaction affinities require highly pure and often large amounts of interactants along with prior knowledge of their interaction. To facilitate discovery and lessen the stringency of the purity and/or quantity requirements of interacting molecules, multiple quantitative high throughput methodologies have been developed. Thus far, the protein microarray (PMA) (31) and high-throughput fluorescence polarization (HTFP) (11, 32) quantitative approaches have been used to examine PID specificity. Unfortunately, PMAs suffer from poor sensitivity, reproducibility, and measurement discrepancies (32, 33). The alternative HTFP assay is more sensitive than PMAs, but also has poor reproducibility and is biased toward high affinity interactions (32). Further, PMAs and HTFP have minimal K_D sensitivities of 2 and 20 μm, respectively. This boundary limits their scope of application considering the importance of moderate affinity interactions for many PID families (34) and the general importance of these interactions in directing emergent phenomena such as ultrasensitivity and interaction gating (35, 36).The Src Homology 2 (SH2) domain phosphotyrosine interactome is a system of prominent physiological importance that has been the subject of multiple preceding interaction mapping and platform development efforts (17, 19, 31, 32, 37, 38). Tyrosine phosphorylation and its recognition by SH2 domains is a uniquely metazoan adaptation (39), prominently involved in the transduction of growth signals (1, 2, 40). SH2 domain specificity has been leveraged in multiple contexts to rewire signaling pathways (15), and specificity dysregulation is associated with multiple diseases, including cancer (40–42). Despite the importance of directing specific signals, many SH2 domains share sequence preferences, resulting in substantial binding crossover over a broad dynamic range of affinities (27). Yet, the extent of this crossover and the peptide motif characteristics responsible for its direction are poorly resolved. In light of these biological attributes and the availability of preceding information to guide platform development and assess its performance, we chose a significantly novel set of 124 physiological phosphopeptides and four SH2 domains as a pilot interactome for our arrays.Here we develop a cellulose peptide conjugate microarray (CPCMA) based approach that is the first broadly applicable protein interaction mapping approach capable of explicitly capturing binding and nonbinding information, resolving the contextual interplay between amino acids that contribute to binding, and providing a quantitative measure of interaction affinity. We optimize multiple production and incubation parameters for the arrays and develop an experimental and computational pipeline to control for systematic artifacts and robustly estimate affinities, ultimately producing a physiological interactome of 368 measurements between 92 peptides and four domains. In contrast to preceding platforms, we find our approach is highly sensitive, extremely precise, and consistent with multiple forms of orthogonal biophysical and in vivo validation. Leveraging unique modes of analysis enabled by the CPCMAs, we demonstrate that SH2 domains vary considerably in promiscuity and binding dynamic range in a manner aligned with biological function. Consistent with recent evidence that pathway crosstalk is subjected to negative selection (10–12, 43) we demonstrate for the first time that the degree distribution of the interactome is a function of affinity, and therefore little binding overlap is found among the highest affinity interactions, suggesting that such sites may be important points of in vivo coregulation. We highlight a few such interactions and corroborate others using available domain-site specific in vivo interaction information. Next, in order to provide insight into the specificity determinants responsible for this segregation we produce for the first time predictive domain specific binding motif affinity matrices. Lastly, we demonstrate the unique ability of the CPCMAs to resolve contextual contributions to binding, and highlight examples where contextual information contributes to binding specificity.Quantitative analysis of systems-level PID properties is a necessary prerequisite for improved mathematical modeling of signal transduction systems, specificity focused drug design, and engineering advanced synthetic biology circuits. Thus, our quantitative CPCMA based approach should have broad application in future network biology and PID specificity efforts.