Peptide Targeting of PDZ-Dependent Interactions as Pharmacological Intervention in Immune-Related Diseases
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ABSTRACT:
SUBMITTER: Gutierrez-Gonzalez L
PROVIDER: S-EPMC8588348 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND: PDZ domains are one of the most promiscuous protein recognition modules that bind with short linear peptides and play an important role in cellular signaling. Recently, few high-throughput techniques (e.g. protein microarray screen, phage display) have been applied to determine in-vitro binding specificity of PDZ domains. Currently, many computational methods are available to predict PDZ-peptide interactions but they often provide domain specific models and/or have a limited domain coverage. RESULTS: Here, we composed the largest set of PDZ domains derived from human, mouse, fly and worm proteomes and defined binding models for PDZ domain families to improve the domain coverage and prediction specificity. For that purpose, we first identified a novel set of 138 PDZ families, comprising of 548 PDZ domains from aforementioned organisms, based on efficient clustering according to their sequence identity. For 43 PDZ families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach. The advantage of family-wise models is that they can also be used to determine the binding specificity of a newly characterized PDZ domain with sufficient sequence identity to the known families. Since most current experimental approaches provide only positive data, we have to cope with the class imbalance problem. Thus, to enrich the negative class, we introduced a powerful semi-supervised technique to generate high confidence non-interaction data. We report competitive predictive performance with respect to state-of-the-art approaches. CONCLUSIONS: Our approach has several contributions. First, we show that domain coverage can be increased by applying accurate clustering technique. Second, we developed an approach based on a semi-supervised strategy to get high confidence negative data. Third, we allowed high order correlations between the amino acid positions in the binding peptides. Fourth, our method is general enough and will easily be applicable to other peptide recognition modules such as SH2 domains and finally, we performed a genome-wide prediction for 101 human and 102 mouse PDZ domains and uncovered novel interactions with biological relevance. We make all the predictive models and genome-wide predictions freely available to the scientific community.
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Project description:The PDZ domain is an interaction motif that recognizes and binds the C-terminal peptides of target proteins. PDZ domains are ubiquitous in nature and help assemble multiprotein complexes that control cellular organization and signaling cascades. We present an optimized energy function to predict the binding free energy (??G) of PDZ domain/peptide interactions computationally. Geometry-optimized models of PDZ domain/peptide interfaces were built using ROSETTA: , and protein and peptide side chain and backbone degrees of freedom are minimized simultaneously. Using leave-one-out cross-validation, ROSETTA: 's energy function is adjusted to reproduce experimentally determined ??G values with a correlation coefficient of 0.66 and a standard deviation of 0.79 kcal mol(-1). The energy function places an increased weight on hydrogen bonding interactions when compared to a previously developed method to analyze protein/protein interactions. Binding free enthalpies (??H) and entropies (?S) are predicted with reduced accuracies of R?=?0.60 and R?=?0.17, respectively. The computational method improves prediction of PDZ domain specificity from sequence and allows design of novel PDZ domain/peptide interactions.
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Project description:PDZ domains constitute one of the largest families of interaction domains and function by binding the C termini of their target proteins. Using Bayesian estimation, we constructed a three-dimensional extension of a position-specific scoring matrix that predicts to which peptides a PDZ domain will bind, given the primary sequences of the PDZ domain and the peptides. The model, which was trained using interaction data from 82 PDZ domains and 93 peptides encoded in the mouse genome, successfully predicts interactions involving other mouse PDZ domains, as well as PDZ domains from Drosophila melanogaster and, to a lesser extent, PDZ domains from Caenorhabditis elegans. The model also predicts the differential effects of point mutations in peptide ligands on their PDZ domain-binding affinities. Overall, we show that our approach captures, in a single model, the binding selectivity of the PDZ domain family.
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Project description:PDZ (Postsynaptic density protein, Disc large, Zona occludens) domains are protein-protein interaction modules that predominate in submembranous scaffolding proteins. Recently, we showed that the PDZ domains of syntenin-1 also interact with phosphatidylinositol 4,5-bisphosphate (PIP2) and that this interaction controls the recruitment of the protein to the plasma membrane. Here we evaluate the general importance of PIP2-PDZ domain interactions. We report that most PDZ proteins bind weakly to PIP2, but that syntenin-2, the closest homolog of syntenin-1, binds with high affinity to PIP2 via its PDZ domains. Surprisingly, these domains target syntenin-2 to nuclear PIP2 pools, in nuclear speckles and nucleoli. Targeting to these sites is abolished by treatments known to affect these PIP2 pools. Mutational and domain-swapping experiments indicate that high-affinity binding to PIP2 requires both PDZ domains of syntenin-2, but that its first PDZ domain contains the nuclear PIP2 targeting determinants. Depletion of syntenin-2 disrupts the nuclear speckles-PIP2 pattern and affects cell survival and cell division. These findings show that PIP2-PDZ domain interactions can directly contribute to subnuclear assembly processes.
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Project description: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.
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Project description:Cancerous tissues undergo extensive changes to their cellular environments that differentiate them from healthy tissues. These changes include changes in extracellular pH and Ca2+ concentrations, and the exposure of phosphatidylserine (PS) to the extracellular environment, which can modulate the interaction of peptides and proteins with the plasma membrane. Deciphering the molecular mechanisms of such interactions is critical for advancing the knowledge-based design of cancer-targeting molecular tools, such as pH-low insertion peptide (pHLIP). Here, we explore the effects of PS, Ca2+ , and peptide protonation state on the interactions of pHLIP with lipid membranes. Cellular studies demonstrate that exposed PS on the plasma membrane promotes pHLIP targeting. The magnitude of this effect is dependent on extracellular Ca2+ concentration, indicating that divalent cations play an important role in pHLIP targeting in vivo. The targeting mechanism is further explored with a combination of fluorescence and circular dichroism experiments in model membranes and microsecond-timescale all-atom molecular dynamics simulations. Our results demonstrate that Ca2+ is engaged in coupling peptide-lipid interactions in the unprotonated transmembrane conformation of pHLIP. The simulations reveal that while the pH-induced insertion leads to a strong depletion of PS around pHLIP, the Ca2+ -induced insertion has the opposite effect. Thus, extracellular levels of Ca2+ are crucial to linking cellular changes in membrane lipid composition with the selective targeting and insertion of pHLIP. The characterized Ca2+ -dependent coupling between pHLIP sidechains and PS provides atomistic insights into the general mechanism for lipid-coupled regulation of protein-membrane insertion by divalent cations.
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Project description:High-affinity binders are desirable tools to probe the function that specific protein-protein interactions play in cell. In the process of seeking a general strategy to design high-affinity binders, we found a clue from the βPIX (p21-activated kinase interacting exchange factor)-Shank PDZ interaction in synaptic assembly: three PDZ-binding sites are clustered by a parallel coiled-coil trimer but bind to Shank PDZ protein with 1:1 stoichiometry (1 trimer/1 PDZ). Inspired by this architecture, we proposed that peptide dendrimer, mimicking the ligand clustering in βPIX, will also show enhanced binding affinity, yet with 1:1 stoichiometry. This postulation has been proven here, as we synthesized a set of monomeric, dimeric and trimeric peptides and measured their binding affinity and stoichiometry with Shank PDZ domains by isothermal titration calorimetry, native mass spectrometry and surface plasmon resonance. This affinity enhancement, best explained by proximity effect, will be useful to guide the design of high-affinity blockers for protein-protein interactions.
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