Elucidation of the binding preferences of peptide recognition modules: SH3 and PDZ domains

Peptide-binding domains play a critical role in regulation of cellular processes by mediating protein interactions involved in signalling. In recent years, the development of large-scale technologies has enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. These efforts have provided significant insights into the binding specificities of these modular domains. Many research groups have taken advantage of this unprecedented volume of specificity data and have developed a variety of new algorithms for the prediction of binding specificities of peptide-binding domains and for the prediction of their natural binding targets. This knowledge has also been applied to the design of synthetic peptide-binding domains in order to rewire protein-protein interaction networks. Here, we describe how these experimental technologies have impacted on our understanding of peptide-binding domain specificities and on the elucidation of their natural ligands. We discuss SH3 and PDZ domains as well characterized examples, and we explore the feasibility of expanding high-throughput experiments to other peptide-binding domains.     

Analysis of PDZ domain-ligand interactions using carboxyl-terminal phage display

PDZ domains mediate protein-protein interactions at specialized subcellular sites, such as epithelial cell tight junctions and neuronal post-synaptic densities. Because most PDZ domains bind extreme carboxyl-terminal sequences, the phage display method has not been amenable to the study of PDZ domain binding specificities. For the first time, we demonstrate the functional display of a peptide library fused to the carboxyl terminus of the M13 major coat protein. We used this library to analyze carboxyl-terminal peptide recognition by two PDZ domains. For each PDZ domain, the library provided specific ligands with sub-micromolar binding affinities. Synthetic peptides and homology modeling were used to dissect and rationalize the binding interactions. Our results establish carboxyl-terminal phage display as a powerful new method for mapping PDZ domain binding specificity.     

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

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.     

Reexamination of the recognition preference of the specificity pocket of the Abl SH3 domain

Src homology-3 (SH3) domains mediate important protein-protein interactions in a variety of normal and pathological cellular processes, thus providing an attractive target for the selective interference of SH3-dependent signaling events that govern these processes. Most SH3 domains recognize proline-rich peptides with low affinity and poor selectivity, and the goal to design potent and specific ligands for various SH3 domains remains elusive. Better understanding of the molecular basis for SH3 domain recognition is needed in order to design such ligands with potency and specificity. In this report, we seek to define a clear recognition preference of the specificity pocket of the Abl SH3 domain using targeted synthetic peptide libraries. High-resolution affinity panning coupled with mass spectrometric readout allows for quick identification of Trp as the preferred fourth residue in the decapeptide ligand APTWSPPPPP, which binds to Abl SH3 four times stronger than does the decapeptide containing Tyr or Phe in the fourth position. This finding is in contrast to several reports that Tyr is the only residue selected from phage displayed peptide libraries that interacts with the specificity pocket of Abl SH3. This simple, unbiased approach can fine-tune the affinity and selectivity of both natural and unnatural SH3 ligands whose consensus binding sequence has been pre-defined by combinatorial library methods.     

PDZ domains and their binding partners: structure, specificity, and modification

PDZ domains are abundant protein interaction modules that often recognize short amino acid motifs at the C-termini of target proteins. They regulate multiple biological processes such as transport, ion channel signaling, and other signal transduction systems. This review discusses the structural characterization of PDZ domains and the use of recently emerging technologies such as proteomic arrays and peptide libraries to study the binding properties of PDZ-mediated interactions. Regulatory mechanisms responsible for PDZ-mediated interactions, such as phosphorylation in the PDZ ligands or PDZ domains, are also discussed. A better understanding of PDZ protein-protein interaction networks and regulatory mechanisms will improve our knowledge of many cellular and biological processes.     

An integrated machine learning system to computationally screen protein databases for protein binding peptide ligands

A fairly large set of protein interactions is mediated by families of peptide binding domains, such as Src homology 2 (SH2), SH3, PDZ, major histocompatibility complex, etc. To identify their ligands by experimental screening is not only labor-intensive but almost futile in screening low abundance species due to the suppression by high abundance species. An ideal way of studying protein-protein interactions is to use high throughput computational approaches to screen protein sequence databases to direct the validating experiments toward the most promising peptides. Predictors with only good cross-validation were not good enough to screen protein databases. In the current study we built integrated machine learning systems using three novel coding methods and screened the Swiss-Prot and GenBank protein databases for potential ligands of 10 SH3 and three PDZ domains. A large fraction of predictions has already been experimentally confirmed by other independent research groups, indicating a satisfying generalization capability for future applications in identifying protein interactions.     

Identification of Src, Fyn, Lyn, PI3K and Abl SH3 domain ligands using phage display libraries.

Many proteins involved in intracellular signal transduction contain a small, 50-60 amino acid domain, termed the Src homology 3 (SH3) domain. This domain appears to mediate critical protein-protein interactions that are involved in responses to extracellular signals. Previous studies have shown that the SH3 domains from several proteins recognize short, contiguous amino acid sequences that are rich in proline residues. While all SH3 recognition sequences identified to date share a conserved P-X-X-P motif, the sequence recognition specificity of individual SH3 domains is poorly understood. We have employed a novel modification of phage display involving biased libraries to identify peptide ligands of the Src, Fyn, Lyn, PI3K and Abl SH3 domains. With biased libraries, we probed SH3 recognition over a 12 amino acid window. The Src SH3 domain prefers the sequence XXXRPLPPLPXP, Fyn prefers XXXRPLPP(I/L)PXX, Lyn prefers RXXRPLPPLPXP, PI3K prefers RXXRPLPPLPP while the Abl SH3 domain selects phage containing the sequence PPPYPPPP(I/V)PXX. We have also analysed the binding properties of Abl and Src SH3 ligands. We find that although the phage-displayed Abl and Src SH3 ligands are proline rich, they are distinct. In surface plasmon resonance binding assays, these SH3 domains displayed highly selective binding to their cognate ligands when the sequences were displayed on the surface of the phage or as synthetic peptides. The selection of these high affinity SH3 peptide ligands provides valuable information on the recognition motifs of SH3 domains, serve as new tools to interfere with the cellular functions of SH3 domain-mediated processes and form the basis for the design of SH3-specific inhibitors of disease pathways.     

The importance of being proline: the interaction of proline-rich motifs in signaling proteins with their cognate domains.

Acommon focus among molecular and cellular biologists is the identification of proteins that interact with each other. Yeast two-hybrid, cDNA expression library screening, and coimmunoprecipitation experiments are powerful methods for identifying novel proteins that bind to one's favorite protein for the purpose of learning more regarding its cellular function. These same techniques, coupled with truncation and mutagenesis experiments, have been used to define the region of interaction between pairs of proteins. One conclusion from this work is that many interactions occur over short regions, often less than 10 amino acids in length within one protein. For example, mapping studies and 3-dimensional analyses of antigen-antibody interactions have revealed that epitopes are typically 4-7 residues long (1). Other examples include protein-interaction modules, such as Src homology (SH) 2 and 3 domains, phosphotyrosine binding domains (PTB), postsynaptic density/disc-large/ZO1 (PDZ) domains, WW domains, Eps15 homology (EH) domains, and 14-3-3 proteins that typically recognize linear regions of 3-9 amino acids. Each of these domains has been the subject of recent reviews published elsewhere (2 3 4 5 6 7). Among the primary structures of many ligands for protein-protein interactions, the amino acid proline is critical. In particular, SH3, WW, and several new protein-interaction domains prefer ligand sequences that are proline-rich. In addition, even though ligands for EH domains and 14-3-3 domains are not proline-rich, they do include a single proline residue. This review highlights the analysis of those protein-protein interactions that involve proline residues, the biochemistry of proline, and current drug discovery efforts based on proline peptidomimetics.-Kay, B. K., Williamson, M. P., Sudol, M. The importance of being proline: the interaction of proline-rich motifs in signaling proteins with their cognate domains.     

A specificity map for the PDZ domain family

PDZ domains are protein-protein interaction modules that recognize specific C-terminal sequences to assemble protein complexes in multicellular organisms. By scanning billions of random peptides, we accurately map binding specificity for approximately half of the over 330 PDZ domains in the human and Caenorhabditis elegans proteomes. The domains recognize features of the last seven ligand positions, and we find 16 distinct specificity classes conserved from worm to human, significantly extending the canonical two-class system based on position -2. Thus, most PDZ domains are not promiscuous, but rather are fine-tuned for specific interactions. Specificity profiling of 91 point mutants of a model PDZ domain reveals that the binding site is highly robust, as all mutants were able to recognize C-terminal peptides. However, many mutations altered specificity for ligand positions both close and far from the mutated position, suggesting that binding specificity can evolve rapidly under mutational pressure. Our specificity map enables the prediction and prioritization of natural protein interactions, which can be used to guide PDZ domain cell biology experiments. Using this approach, we predicted and validated several viral ligands for the PDZ domains of the SCRIB polarity protein. These findings indicate that many viruses produce PDZ ligands that disrupt host protein complexes for their own benefit, and that highly pathogenic strains target PDZ domains involved in cell polarity and growth.     

An oriented peptide array library (OPAL) strategy to study protein-protein interactions

One of the major questions in signal transduction is how the specificities of protein-protein interactions determine the assembly of distinct signaling complexes in response to stimuli. Several peptide library methods have been developed and widely used to study protein-protein interactions. These approaches primarily rely on peptide or DNA sequencing to identify the peptide or consensus motif for binding and may prove too costly or difficult to accommodate high throughput applications. We report here an oriented peptide array library (OPAL) approach that should facilitate high throughput proteomic analysis of protein-protein interactions. OPAL integrates the principles of both the oriented peptide libraries and array technologies. Hundreds of pools of oriented peptide libraries are synthesized as amino acid scan arrays. We demonstrate that these arrays can be used to map the specificities of a variety of interactions, including antibodies, protein domains such Src homology 2 domains, and protein kinases.     

Phage display can select over-hydrophobic sequences that may impair prediction of natural domain-peptide interactions

MOTIVATION: The phage display peptide selection approach is widely used for defining binding specificities of globular domains. PDZ domains recognize partner proteins via C-terminal motifs and are often used as a model for interaction predictions. Here, we investigated to which extent phage display data that were recently published for 54 human PDZ domains can be applied to the prediction of human PDZ-peptide interactions. RESULTS: Promising predictions were obtained for one-third of the 54 PDZ domains. For the other two-thirds, we detected in the phage display peptides an important bias for hydrophobic amino acids that seemed to impair correct predictions. Therefore, phage display-selected peptides may be over-hydrophobic and of high affinity, while natural interaction motifs are rather hydrophilic and mostly combine low affinity with high specificity. We suggest that potential amino acid composition bias should systematically be investigated when applying phage display data to the prediction of specific natural domain-linear motif interactions.     

Proteome scanning to predict PDZ domain interactions using support vector machines

Background  

PDZ domains mediate protein-protein interactions involved in important biological processes through the recognition of short linear motifs in their target proteins. Two recent independent studies have used protein microarray or phage display technology to detect PDZ domain interactions with peptide ligands on a large scale. Several computational predictors of PDZ domain interactions have been developed, however they are trained using only protein microarray data and focus on limited subsets of PDZ domains. An accurate predictor of genomic PDZ domain interactions would allow the proteomes of organisms to be scanned for potential binders. Such an application would require an accurate and precise predictor to avoid generating too many false positive hits given the large amount of possible interactors in a given proteome. Once validated these predictions will help to increase the coverage of current PDZ domain interaction networks and further our understanding of the roles that PDZ domains play in a variety of biological processes.     

Comprehensive Assessment of the Relationship Between Site−2 Specificity and Helix α2 in the Erbin PDZ Domain

PDZ domains are key players in signalling pathways. These modular domains generally recognize short linear C-terminal stretches of sequences in proteins that organize the formation of complex multi-component assemblies. The development of new methodologies for the characterization of the molecular principles governing these interactions is critical to fully understand the functional diversity of the family and to elucidate biological functions for family members. Here, we applied an in vitro evolution strategy to explore comprehensively the capacity of PDZ domains for specific recognition of different amino acids at a key position in C-terminal peptide ligands. We constructed a phage-displayed library of the Erbin PDZ domain by randomizing the binding site⁻² and adjacent residues, which are all contained in helix α2, and we selected for variants binding to a panel of peptides representing all possible position⁻² residues. This approach generated insights into the basis for the common natural class I and II specificities, demonstrated an alternative basis for a rare natural class III specificity for Asp⁻², and revealed a novel specificity for Arg⁻² that has not been reported in natural PDZ domains. A structure of a PDZ-peptide complex explained the minimum requirement for switching specificity from class I ligands containing Thr/Ser⁻² to class II ligands containing hydrophobic residues at position⁻². A second structure explained the molecular basis for the specificity for ligands containing Arg⁻². Overall, the evolved PDZ variants greatly expand our understanding of site⁻² specificities and the variants themselves may prove useful as building blocks for synthetic biology.     

Target-assisted iterative screening reveals novel interactors for PSD95, Nedd4, Src,Abl and Crk proteins

A new in vitro screening method has been developed and applied to a commercial phage-displayed cDNA library to search for novel protein-protein interactions. PDZ, WW and SH3 domains from PSD95, Nedd4, Src, Abl and Crk proteins were used as targets. 12 novel putative and 2 previously reported interactions were identified in test screens. The novel screening format, dubbed TAIS (target-assisted iterative screening), is discussed as an alternative platform to existing technologies for a pair-wise characterization of protein-protein interactions.     

SH3-SPOT: an algorithm to predict preferred ligands to different members of the SH3 gene family

We have developed a procedure to predict the peptide binding specificity of an SH3 domain from its sequence. The procedure utilizes information extracted from position-specific contacts derived from six SH3/peptide or SH3/protein complexes of known structure. The framework of SH3/peptide contacts defined on the structure of the complexes is used to build a residue-residue interaction database derived from ligands obtained by panning peptide libraries displayed on filamentous phage.The SH3-specific interaction database is a multidimensional array containing frequencies of position-specific contacts. As input, SH3-SPOT requires the sequence of an SH3 domain and of a query decapeptide ligand. The array, that we call the SH3-specific matrix, is then used to evaluate the probability that the peptide would bind the given SH3 domain. This procedure is fast enough to be applied to the entire protein sequence database.Panning experiments were performed to search putative specific ligands of different SH3 domains in a database of decapeptides, or in a database of protein sequences. The procedure ranked some of the natural partners of interaction of a number of SH3 domains among the best ligands of the approximately 5. 6x10(9) different decapeptides in the SWISSPROT database. We expect the predictive power of the method to increase with the enrichment of the SH3-specific matrix by interaction data derived from new complex structures or from the characterization of new ligands. The procedure was developed using the SH3 domain family as test case but its application can easily be extended to other families of protein domains (such as, SH2, MHC, EH, PDZ, etc.).     

The multiple‐specificity landscape of modular peptide recognition domains

Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks.     

Energetic determinants of internal motif recognition by PDZ domains

PDZ domains are protein-protein interaction modules that organize intracellular signaling complexes. Most PDZ domains recognize specific peptide motifs followed by a required COOH-terminus. However, several PDZ domains have been found which recognize specific internal peptide motifs. The best characterized example is the syntrophin PDZ domain which, in addition to binding peptide ligands with the consensus sequence -E-S/T-X-V-COOH, also binds the neuronal nitric oxide synthase (nNOS) PDZ domain in a manner that does not depend on its precise COOH-terminal sequence. In the structure of the syntrophin-nNOS PDZ heterodimer complex, the two PDZ domains interact in a head-to-tail fashion, with an internal sequence from the nNOS PDZ domain binding precisely at the peptide binding groove of the syntrophin PDZ domain. To understand the energetic basis of this alternative mode of PDZ recognition, we have undertaken an extensive mutagenic and biophysical analysis of the nNOS PDZ domain and its interaction with the syntrophin PDZ domain. Our data indicate that the presentation of the nNOS internal motif within the context of a rigid beta-hairpin conformation is absolutely essential to binding; amino acids crucial to the structural integrity of the hairpin are as important or more important than residues that make direct contacts. The results reveal the general rules of PDZ recognition of diverse ligand types.     

High-throughput analysis of peptide-binding modules

Modular protein interaction domains (PIDs) that recognize linear peptide motifs are found in hundreds of proteins within the human genome. Some PIDs such as SH2, 14-3-3, Chromo, and Bromo domains serve to recognize posttranslational modification (PTM) of amino acids (such as phosphorylation, acetylation, methylation, etc.) and translate these into discrete cellular responses. Other modules such as SH3 and PSD-95/Discs-large/ZO-1 (PDZ) domains recognize linear peptide epitopes and serve to organize protein complexes based on localization and regions of elevated concentration. In both cases, the ability to nucleate-specific signaling complexes is in large part dependent on the selectivity of a given protein module for its cognate peptide ligand. High-throughput (HTP) analysis of peptide-binding domains by peptide or protein arrays, phage display, mass spectrometry, or other HTP techniques provides new insight into the potential protein-protein interactions prescribed by individual or even whole families of modules. Systems level analyses have also promoted a deeper understanding of the underlying principles that govern selective protein-protein interactions and how selectivity evolves. Lastly, there is a growing appreciation for the limitations and potential pitfalls associated with HTP analysis of protein-peptide interactomes. This review will examine some of the common approaches utilized for large-scale studies of PIDs and suggest a set of standards for the analysis and validation of datasets from large-scale studies of peptide-binding modules. We will also highlight how data from large-scale studies of modular interaction domain families can provide insight into systems level properties such as the linguistics of selective interactions.     

Role of electrostatic interactions in PDZ domain ligand recognition

PDZ domains are protein-protein interaction modules that normally recognize short C-terminal peptides. The apparent requirement for a ligand with a free terminal carboxylate group has led to the proposal that electrostatic interactions with the terminus play a significant role in recognition. However, this model has been called into question by the more recent finding that PDZ domains can recognize some internal peptide motifs that occur within a specific secondary structure context. Although these motifs bind at the same interface, they lack a terminal charge. Here we have investigated the role of electrostatics in PDZ-mediated recognition in the mouse alpha1-syntrophin PDZ domain by examining the salt dependence of binding to both terminal and internal ligands and the effects of mutating a conserved basic residue previously proposed to play a role in electrostatic recognition. These studies indicate that direct electrostatic interactions with the peptide terminus do not play a significant energetic role in binding. Additional chemical modification studies of the peptide terminus support a model in which steric and hydrogen bonding complementarity play a primary role in recognition specificity. Peptides with a free carboxy terminus, or presented within a specific structural context, can satisfy these requirements.     

Characterization of PDZ domain-peptide interaction interface based on energetic patterns

PDZ domain is one of the abundant modular domains that recognize short peptide sequences to mediate protein-protein interactions. To decipher the binding specificity of PDZ domain, we analyzed the interactions between 11 mouse PDZ domains and 2387 peptides using a method called MIEC-SVM, which energetically characterizes the domain-peptide interaction using molecular interaction energy components (MIECs) and predicts binding specificity using support vector machine (SVM). Cross-validation and leave-one-domain-out test showed that the MIEC-SVM using all 44 PDZ-peptide residue pairs at the interaction interface outperformed the sequence-based methods in the literature. A further feature (residue pair) selection procedure illustrated that 16 residue pairs were uninformative to the binding specificity, even though they contributed significantly (~50%) to the binding energy. If only using the 28 informative residue pairs, the performance of the MIEC-SVM on predicting the PDZ binding specificity was significantly improved. This analysis suggests that the informative and uninformative residue interactions between the PDZ domain and the peptide may represent those contributing to binding specificity and affinity, respectively. We performed additional structural and energetic analyses to shed light on understanding how the PDZ-peptide recognition is established. The success of the MIEC-SVM method on PDZ domains in this study and SH3 domains in our previous studies illustrates its generality on characterizing protein-peptide interactions and understanding protein recognition from a structural and energetic viewpoint.