PDZ Domain-peptide Research Articles

Crystallographic Studies of PDZ Domain-Peptide Interactions of the Scribble Polarity Module.

The determination of high-resolution crystal structures of cell polarity regulatory proteins bound to their functional interactors has proven to be invaluable for deciphering the underlying molecular mechanisms. Here we describe methods to identify suitable complexes of cell polarity protein domains bound to interacting ligands with subsequent preparation of such complexes for X-ray crystallographic analysis.

Study of PDZ-Peptide and PDZ-Lipid Interactions by Surface Plasmon Resonance/BIAcore.

Surface plasmon resonance (SPR)/BIAcore technology enables the characterization of molecular interactions, including determination of affinities and kinetics. InBIAcore, one of the interaction partners (the ligand) is immobilized on a chip and the other (the analyte) is provided in solution. BIAcore allows to study association and dissociation rates in real time without the use of labeling. BIAcore can be applied to molecular interactions involving small compounds and biological macromolecules such as proteins, lipids, nucleic acids, or carbohydrates. Here we describe protocols for the measurements of PDZ domain-peptide (oriented biotinylated peptides), PDZ domain-liposomes (lipid membranes), and PDZ-lipid-peptide tripartite interactions.

A Multireporter Bacterial 2-Hybrid Assay for the High-Throughput and Dynamic Assay of PDZ Domain-Peptide Interactions.

The accurate determination of protein-protein interactions has been an important focus of molecular biology toward which much progress has been made due to the continuous development of existing and new technologies. However, current methodscan have limitations, including scale and restriction to high affinity interactions, limiting our understanding of a large subset of these interactions. Here, we describe a modified bacterial-hybrid assay that employs combined selectable and scalable reporters that enable the sensitive screening of large peptide libraries followed by the sorting of positive interactions by the level of reporter output. We have applied this tool to characterize a set of human and E.coli PDZ domains. Our results are consistent with prior characterization of these proteins, and the improved sensitivity increases our ability to predict known and novel in vivo binding partners. This approach allows for the recovery of a wide range of affinities with a high throughput method that does not sacrifice the scale of the screen.

Structural basis for PDZ domain interactions in the post-synaptic density scaffolding protein Shank3.

The Shank proteins are crucial scaffolding elements of the post-synaptic density (PSD). One of the best-characterized domains in Shank is the PDZ domain, which binds to C-terminal segments of several other PSD proteins. We carried out a detailed structural analysis of Shank3 PDZ domain-peptide complexes, to understand determinants of binding affinity towards different ligand proteins. Ligand peptides from four different proteins were cocrystallized with the Shank3 PDZ domain, and binding affinities were determined calorimetrically. In addition to conserved class I interactions between the first and third C-terminal peptide residue and Shank3, side chain interactions of other residues in the peptide with the PDZ domain are important factors in defining affinity. Structural conservation suggests that the binding specificities of the PDZ domains from different Shanks are similar. Two conserved buried water molecules in PDZ domains may affect correct local folding of ligand recognition determinants. The solution structure of a tandem Shank3 construct containing the SH3 and PDZ domains showed that the two domains are close to each other, which could be of relevance, when recognizing and binding full target proteins. The SH3 domain did not affect the affinity of the PDZ domain towards short target peptides, and the schizophrenia-linked Shank3 mutation R536W in the linker between the domains had no effect on the structure or peptide interactions of the Shank3 SH3-PDZ unit. Our data show the spatial arrangement of two adjacent Shank domains and pinpoint affinity determinants for short PDZ domain ligands with limited sequence homology.

Discovery of a small-molecule inhibitor of Dvl-CXXC5 interaction by computational approaches.

The Wnt/β-catenin signaling pathway plays a significant role in the control of osteoblastogenesis and bone formation. CXXC finger protein 5 (CXXC5) has been recently identified as a negative feedback regulator of osteoblast differentiation through a specific interaction with Dishevelled (Dvl) protein. It was reported that targeting the Dvl-CXXC5 interaction could be a novel anabolic therapeutic target for osteoporosis. In this study, complex structure of Dvl PDZ domain and CXXC5 peptide was simulated with molecular dynamics (MD). Based on the structural analysis of binding modes of MD-simulated Dvl PDZ domain with CXXC5 peptide and crystal Dvl PDZ domain with synthetic peptide-ligands, we generated two different pharmacophore models and applied pharmacophore-based virtual screening to discover potent inhibitors of the Dvl-CXXC5 interaction for the anabolic therapy of osteoporosis. Analysis of 16 compounds selected by means of a virtual screening protocol yielded four compounds that effectively disrupted the Dvl-CXXC5 interaction in the fluorescence polarization assay. Potential compounds were validated by fluorescence spectroscopy and nuclear magnetic resonance. We successfully identified a highly potent inhibitor, BMD4722, which directly binds to the Dvl PDZ domain and disrupts the Dvl-CXXC5 interaction. Overall, CXXC5-Dvl PDZ domain complex based pharmacophore combined with various traditional and simple computational methods is a promising approach for the development of modulators targeting the Dvl-CXXC5 interaction, and the potent inhibitor BMD4722 could serve as a starting point to discover or design more potent and specific the Dvl-CXXC5 interaction disruptors.

Protein purification using PDZ affinity chromatography.

PDZ domains function in nature as protein-binding domains within scaffold and membrane-associated proteins. They comprise approximately 90 residues and undergo specific, high-affinity interactions with complementary C-terminal peptide sequences, other PDZ domains, and/or phospholipids. We have previously shown that the specific, strong interactions of PDZ domains with their ligands make them well suited for use in affinity chromatography. This unit provides protocols for the PDZ affinity chromatography procedure that are applicable for the purification of proteins that contain PDZ domains or PDZ domain-binding ligands, either naturally or introduced by genetic engineering. We detail the preparation of affinity resins composed of PDZ domains or PDZ domain peptide ligands coupled to solid supports. These resins can be used to purify proteins containing endogenous or genetically introduced PDZ domains or ligands, eluting the proteins with free PDZ domain peptide ligands.

Pre-metazoan origins and evolution of the cadherin adhesome.

ABSTRACTVertebrate adherens junctions mediate cell–cell adhesion via a “classical” cadherin–catenin “core” complex, which is associated with and regulated by a functional network of proteins, collectively named the cadherin adhesome (“cadhesome”). The most basal metazoans have been shown to conserve the cadherin–catenin “core”, but little is known about the evolution of the cadhesome. Using a bioinformatics approach based on both sequence and structural analysis, we have traced the evolution of this larger network in 26 organisms, from the uni-cellular ancestors of metazoans, through basal metazoans, to vertebrates. Surprisingly, we show that approximately 70% of the cadhesome, including proteins with similarity to the catenins, predate metazoans. We found that the transition to multicellularity was accompanied by the appearance of a small number of adaptor proteins, and we show how these proteins may have helped to integrate pre-metazoan sub-networks via PDZ domain–peptide interactions. Finally, we found the increase in network complexity in higher metazoans to have been driven primarily by expansion of paralogs. In summary, our analysis helps to explain how the complex protein network associated with cadherin at adherens junctions first came together in the first metazoan and how it evolved into the even more complex mammalian cadhesome.

Quantification of the transferability of a designed protein specificity switch reveals extensive epistasis in molecular recognition.

Reengineering protein-protein recognition is an important route to dissecting and controlling complex interaction networks. Experimental approaches have used the strategy of "second-site suppressors," where a functional interaction is inferred between two proteins if a mutation in one protein can be compensated by a mutation in the second. Mimicking this strategy, computational design has been applied successfully to change protein recognition specificity by predicting such sets of compensatory mutations in protein-protein interfaces. To extend this approach, it would be advantageous to be able to "transplant" existing engineered and experimentally validated specificity changes to other homologous protein-protein complexes. Here, we test this strategy by designing a pair of mutations that modulates peptide recognition specificity in the Syntrophin PDZ domain, confirming the designed interaction biochemically and structurally, and then transplanting the mutations into the context of five related PDZ domain-peptide complexes. We find a wide range of energetic effects of identical mutations in structurally similar positions, revealing a dramatic context dependence (epistasis) of designed mutations in homologous protein-protein interactions. To better understand the structural basis of this context dependence, we apply a structure-based computational model that recapitulates these energetic effects and we use this model to make and validate forward predictions. Although the context dependence of these mutations is captured by computational predictions, our results both highlight the considerable difficulties in designing protein-protein interactions and provide challenging benchmark cases for the development of improved protein modeling and design methods that accurately account for the context.

Simultaneous prediction of binding free energy and specificity for PDZ domain–peptide interactions

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with ROSETTA. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several ROSETTA protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

Probing the Role of Backbone Hydrogen Bonds in Protein–Peptide Interactions by Amide-to-Ester Mutations

One of the most frequent protein-protein interaction modules in mammalian cells is the postsynaptic density 95/discs large/zonula occludens 1 (PDZ) domain, involved in scaffolding and signaling and emerging as an important drug target for several diseases. Like many other protein-protein interactions, those of the PDZ domain family involve formation of intermolecular hydrogen bonds: C-termini or internal linear motifs of proteins bind as β-strands to form an extended antiparallel β-sheet with the PDZ domain. Whereas extensive work has focused on the importance of the amino acid side chains of the protein ligand, the role of the backbone hydrogen bonds in the binding reaction is not known. Using amide-to-ester substitutions to perturb the backbone hydrogen-bonding pattern, we have systematically probed putative backbone hydrogen bonds between four different PDZ domains and peptides corresponding to natural protein ligands. Amide-to-ester mutations of the three C-terminal amides of the peptide ligand severely affected the affinity with the PDZ domain, demonstrating that hydrogen bonds contribute significantly to ligand binding (apparent changes in binding energy, ΔΔG = 1.3 to >3.8 kcal mol(-1)). This decrease in affinity was mainly due to an increase in the dissociation rate constant, but a significant decrease in the association rate constant was found for some amide-to-ester mutations suggesting that native hydrogen bonds have begun to form in the transition state of the binding reaction. This study provides a general framework for studying the role of backbone hydrogen bonds in protein-peptide interactions and for the first time specifically addresses these for PDZ domain-peptide interactions.

A sequence-based computational approach to predicting PDZ domain-peptide interactions

The PDZ domain is one of the most ubiquitous protein domains that is involved in coordinating signaling complex formation and protein networking by reversibly interacting with multiple binding partners. It has been linked to many devastating diseases such as avian influenza, Fraser syndrome, Usher syndrome and Dejerine–Sottas neuropathy. Understanding the selectivity of PDZ domains can help elucidate how defects in PDZ proteins and their binding partners lead to human diseases. Since experimental methods to determine the interaction specificity of the PDZ domains are expensive and labor intensive, an accurate computational method is thus needed. Our developed support vector machine-based predictor using dipeptide composition is shown to qualitatively predict PDZ domain-peptide interaction with a high accuracy rate. Furthermore, since most of the dipeptide compositions are redundant and irrelevant, we propose a new hybrid feature selection technique to select only a subset of these compositions for interaction prediction. The experimental results show that only approximately 25% of dipeptide features are needed and that our method improves the prediction results significantly. The selected dipeptide features are also analyzed and shown to play important roles in specificity patterns of PDZ domains. Our method is based only on primary sequence information, and it can be used for the research of drug target and drug design in identifying PDZ domain-ligand interactions. This article is part of a Special Issue entitled: Computational Proteomics, Systems Biology & Clinical Implications. Guest Editor: Yudong Cai.

Protein-Peptide Complex Prediction through Fragment Interaction Patterns

The number of protein-peptide interactions in a cell isso large that experimental determination of all these complex structures would be a daunting task. Although homology modeling and refinement protocols have vastly improved the number and quality of predicted structural models, ab initio methods are still challenged by both the large number of possible docking sites and the conformational space accessible to flexible peptides. We present a method that addresses these challenges by sampling the entire accessible surface of a protein with a reduced conformational space of interacting backbone fragment pairs from unrelated structures. We demonstrate its potential by predicting ab initio the bound structure for a variety of protein-peptide complexes. In addition, we show the potential of our method forthe discovery of domain interaction sites and domain-domain docking.

Predicting PDZ domain mediated protein interactions from structure

BackgroundPDZ domains are structural protein domains that recognize simple linear amino acid motifs, often at protein C-termini, and mediate protein-protein interactions (PPIs) in important biological processes, such as ion channel regulation, cell polarity and neural development. PDZ domain-peptide interaction predictors have been developed based on domain and peptide sequence information. Since domain structure is known to influence binding specificity, we hypothesized that structural information could be used to predict new interactions compared to sequence-based predictors.ResultsWe developed a novel computational predictor of PDZ domain and C-terminal peptide interactions using a support vector machine trained with PDZ domain structure and peptide sequence information. Performance was estimated using extensive cross validation testing. We used the structure-based predictor to scan the human proteome for ligands of 218 PDZ domains and show that the predictions correspond to known PDZ domain-peptide interactions and PPIs in curated databases. The structure-based predictor is complementary to the sequence-based predictor, finding unique known and novel PPIs, and is less dependent on training–testing domain sequence similarity. We used a functional enrichment analysis of our hits to create a predicted map of PDZ domain biology. This map highlights PDZ domain involvement in diverse biological processes, some only found by the structure-based predictor. Based on this analysis, we predict novel PDZ domain involvement in xenobiotic metabolism and suggest new interactions for other processes including wound healing and Wnt signalling.ConclusionsWe built a structure-based predictor of PDZ domain-peptide interactions, which can be used to scan C-terminal proteomes for PDZ interactions. We also show that the structure-based predictor finds many known PDZ mediated PPIs in human that were not found by our previous sequence-based predictor and is less dependent on training–testing domain sequence similarity. Using both predictors, we defined a functional map of human PDZ domain biology and predict novel PDZ domain function. Users may access our structure-based and previous sequence-based predictors at http://webservice.baderlab.org/domains/POW.

Computational Design of a PDZ Domain Peptide Inhibitor that Rescues CFTR Activity

The cystic fibrosis transmembrane conductance regulator (CFTR) is an epithelial chloride channel mutated in patients with cystic fibrosis (CF). The most prevalent CFTR mutation, ΔF508, blocks folding in the endoplasmic reticulum. Recent work has shown that some ΔF508-CFTR channel activity can be recovered by pharmaceutical modulators (“potentiators” and “correctors”), but ΔF508-CFTR can still be rapidly degraded via a lysosomal pathway involving the CFTR-associated ligand (CAL), which binds CFTR via a PDZ interaction domain. We present a study that goes from theory, to new structure-based computational design algorithms, to computational predictions, to biochemical testing and ultimately to epithelial-cell validation of novel, effective CAL PDZ inhibitors (called “stabilizers”) that rescue ΔF508-CFTR activity. To design the “stabilizers”, we extended our structural ensemble-based computational protein redesign algorithm to encompass protein-protein and protein-peptide interactions. The computational predictions achieved high accuracy: all of the top-predicted peptide inhibitors bound well to CAL. Furthermore, when compared to state-of-the-art CAL inhibitors, our design methodology achieved higher affinity and increased binding efficiency. The designed inhibitor with the highest affinity for CAL (kCAL01) binds six-fold more tightly than the previous best hexamer (iCAL35), and 170-fold more tightly than the CFTR C-terminus. We show that kCAL01 has physiological activity and can rescue chloride efflux in CF patient-derived airway epithelial cells. Since stabilizers address a different cellular CF defect from potentiators and correctors, our inhibitors provide an additional therapeutic pathway that can be used in conjunction with current methods.

Serine 77 in the PDZ Domain of PICK1 Is a Protein Kinase Cα Phosphorylation Site Regulated by Lipid Membrane Binding

PICK1 (protein interacting with C kinase 1) contains an N-terminal protein binding PDZ domain and a C-terminal lipid binding BAR domain. PICK1 plays a key role in several physiological processes, including synaptic plasticity. However, little is known about the cellular mechanisms governing the activity of PICK1 itself. Here we show that PICK1 is a substrate in vitro both for PKCα (protein kinase Cα), as previously shown, and for CaMKIIα (Ca(2+)-calmodulin-dependent protein kinase IIα). By mutation of predicted phosphorylation sites, we identify Ser77 in the PDZ domain as a major phosphorylation site for PKCα. Mutation of Ser77 reduced the level of PKCα-mediated phosphorylation ~50%, whereas no reduction was observed upon mutation of seven other predicted sites. Addition of lipid vesicles increased the level of phosphorylation of Ser77 10-fold, indicating that lipid binding is critical for optimal phosphorylation. Binding of PKCα to the PICK1 PDZ domain was not required for phosphorylation, but a PDZ domain peptide ligand reduced the overall level of phosphorylation ~30%. The phosphomimic S77D reduced the extent of cytosolic clustering of eYFP-PICK1 in COS7 cells and thereby conceivably its lipid binding and/or polymerization capacity. We propose that PICK1 is phosphorylated at Ser77 by PKCα preferentially when bound to membrane vesicles and that this phosphorylation in turn modulates its cellular distribution.

Characterization of PDZ domain–peptide interactions using an integrated protocol of QM/MM, PB/SA, and CFEA analyses

Protein-protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains. Characterizing the interaction interface of domain-peptide complexes and analyzing binding specificity for modular domains are critical for deciphering protein-protein interaction networks. In this article, we report the successful use of an integrated computational protocol to dissect the energetic profile and structural basis of peptide binding to third PDZ domain (PDZ3) from the PSD-95 protein. This protocol employs rigorous quantum mechanics/molecular mechanics (QM/MM), semi-empirical Poisson-Boltzmann/surface area (PB/SA), and empirical conformational free energy analysis (CFEA) to quantitatively describe and decompose systematic energy changes arising from, respectively, noncovalent interaction, desolvation effect, and conformational entropy loss associated with the formation of 30 affinity-known PDZ3-peptide complexes. We show that the QM/MM-, PB/SA-, and CFEA-derived energy components can work together fairly well in reproducing experimentally measured affinity after a linearly weighting treatment, albeit they are not compatible with each other directly. We also demonstrate that: (1) noncovalent interaction and desolvation effect donate, respectively, stability and specificity to complex architecture, while entropy loss contributes modestly to binding; (2) P(0) and P(-2) of peptide ligand are the most important positions for determining both the stability and specificity of the PDZ3-peptide complex, P(-1) and P(-3) can confer substantial stability (but not specificity) for the complex, and N-terminal P(-4) and P(-5) have only a very limited effect on binding.

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 217 [corrected] 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.

A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain–peptide interaction from primary sequence

Motivation: Predicting protein interactions involving peptide recognition domains is essential for understanding the many important biological processes they mediate. It is important to consider the binding strength of these interactions to help us construct more biologically relevant protein interaction networks that consider cellular context and competition between potential binders.Results: We developed a novel regression framework that considers both positive (quantitative) and negative (qualitative) interaction data available for mouse PDZ domains to quantitatively predict interactions between PDZ domains, a large peptide recognition domain family, and their peptide ligands using primary sequence information. First, we show that it is possible to learn from existing quantitative and negative interaction data to infer the relative binding strength of interactions involving previously unseen PDZ domains and/or peptides given their primary sequence. Performance was measured using cross-validated hold out testing and testing with previously unseen PDZ domain–peptide interactions. Second, we find that incorporating negative data improves quantitative interaction prediction. Third, we show that sequence similarity is an important prediction performance determinant, which suggests that experimentally collecting additional quantitative interaction data for underrepresented PDZ domain subfamilies will improve prediction.Availability and Implementation: The Matlab code for our SemiSVR predictor and all data used here are available at http://baderlab.org/Data/PDZAffinity.Contact: gary.bader@utoronto.ca; dengnaiyang@cau.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

All-Atom Monte Carlo Approach to Protein–Peptide Binding

We develop a procedure for exploring the free energy landscape of protein–peptide binding at atomic detail and apply it to PDZ domain–peptide interactions. The procedure involves soft constraints on receptor proteins providing limited chain flexibility, including backbone motions. Peptide chains are left fully flexible and kept in spatial proximity of the protein through periodic boundary conditions. By extensive Monte Carlo simulations, full representative conformational ensembles at temperatures where bound and unbound states coexist are obtained. To make this approach computationally feasible, we develop an effective all-atom energy function centering on hydrophobicity, hydrogen bonding, and electrostatic interactions. Our initial focus is a set of 11 PDZ domain–peptide pairs with experimentally determined complex structures. Minimum-energy conformations are found to be highly similar to the respective native structures in eight of the cases (all-atom peptide RMSDs < 6 Å). Having achieved that, we turn to a more complete characterization of the bound peptide state through a clustering scheme applied on the full ensembles of peptide structures. We find a significant diversity among bound peptide conformations for several PDZ domains, in particular involving the N terminal side of the peptide chains. Our computational model is then tested further on a set of nine PDZ domain–peptide pairs where the peptides are not originally present in the experimentally determined structures. We find a similar success rate in terms of the nativeness of minimum-energy conformations. Finally, we investigate the ability of our approach to capture variations in binding affinities for different peptide sequences. This is done in particular for a set of related sequences binding to the third PDZ domain of PSD-95 with encouraging results.

Erratum: Corrigendum: Predicting PDZ domain–peptide interactions from primary sequences

Nat. Biotechnol. 26, 1041–1045 (2008); published online 17 August 2008; corrected after print 9 October 2008 In the version of this article initially published, a negative sign was omitted before the expression P unified(a, b) in the text on page 1043, col. 1, line 20 and the color key in Figure 2 was upside down.