The multiple-specificity landscape of modular peptide recognition domains

Using large scale experimental datasets, the authors show how modular protein interaction domains such as PDZ, SH3 or WW domains, frequently display unexpected multiple binding specificity. The observed multiple specificity leads to new structural insights and accurately predicts new protein interactions

Predicting PDZ domain mediated protein interactions from structure

BACKGROUND: PDZ 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. RESULTS: We 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. CONCLUSIONS: We 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

Putting into Practice Domain-Linear Motif Interaction Predictions for Exploration of Protein Networks

PDZ domains recognise short sequence motifs at the extreme C-termini of proteins. A model based on microarray data has been recently published for predicting the binding preferences of PDZ domains to five residue long C-terminal sequences. Here we investigated the potential of this predictor for discovering novel protein interactions that involve PDZ domains. When tested on real negative data assembled from published literature, the predictor displayed a high false positive rate (FPR). We predicted and experimentally validated interactions between four PDZ domains derived from the human proteins MAGI1 and SCRIB and 19 peptides derived from human and viral C-termini of proteins. Measured binding intensities did not correlate with prediction scores, and the high FPR of the predictor was confirmed. Results indicate that limitations of the predictor may arise from an incomplete model definition and improper training of the model. Taking into account these limitations, we identified several novel putative interactions between PDZ domains of MAGI1 and SCRIB and the C-termini of the proteins FZD4, ARHGAP6, NET1, TANC1, GLUT7, MARCH3, MAS, ABC1, DLL1, TMEM215 and CYSLTR2. These proteins are localised to the membrane or suggested to act close to it and are often involved in G protein signalling. Furthermore, we showed that, while extension of minimal interacting domains or peptides toward tandem constructs or longer peptides never suppressed their ability to interact, the measured affinities and inferred specificity patterns often changed significantly. This suggests that if protein fragments interact, the full length proteins are also likely to interact, albeit possibly with altered affinities and specificities. Therefore, predictors dealing with protein fragments are promising tools for discovering protein interaction networks but their application to predict binding preferences within networks may be limited

Data-Driven Prediction and Design of bZIP Coiled-Coil Interactions

Selective dimerization of the basic-region leucine-zipper (bZIP) transcription factors presents a vivid example of how a high degree of interaction specificity can be achieved within a family of structurally similar proteins. The coiled-coil motif that mediates homo- or hetero-dimerization of the bZIP proteins has been intensively studied, and a variety of methods have been proposed to predict these interactions from sequence data. In this work, we used a large quantitative set of 4,549 bZIP coiled-coil interactions to develop a predictive model that exploits knowledge of structurally conserved residue-residue interactions in the coiled-coil motif. Our model, which expresses interaction energies as a sum of interpretable residue-pair and triplet terms, achieves a correlation with experimental binding free energies of R = 0.68 and significantly out-performs other scoring functions. To use our model in protein design applications, we devised a strategy in which synthetic peptides are built by assembling 7-residue native-protein heptad modules into new combinations. An integer linear program was used to find the optimal combination of heptads to bind selectively to a target human bZIP coiled coil, but not to target paralogs. Using this approach, we designed peptides to interact with the bZIP domains from human JUN, XBP1, ATF4 and ATF5. Testing more than 132 candidate protein complexes using a fluorescence resonance energy transfer assay confirmed the formation of tight and selective heterodimers between the designed peptides and their targets. This approach can be used to make inhibitors of native proteins, or to develop novel peptides for applications in synthetic biology or nanotechnology.National Institutes of Health (U.S.) (Award GM067681

Evolutionarily conserved bias of amino-acid usage refines the definition of PDZ-binding motif

<p>Abstract</p> <p>Background</p> <p>The interactions between PDZ (PSD-95, Dlg, ZO-1) domains and PDZ-binding motifs play central roles in signal transductions within cells. Proteins with PDZ domains bind to PDZ-binding motifs almost exclusively when the motifs are located at the carboxyl (C-) terminal ends of their binding partners. However, it remains little explored whether PDZ-binding motifs show any preferential location at the C-terminal ends of proteins, at genome-level.</p> <p>Results</p> <p>Here, we examined the distribution of the type-I (x-x-S/T-x-I/L/V) or type-II (x-x-V-x-I/V) PDZ-binding motifs in proteins encoded in the genomes of five different species (human, mouse, zebrafish, fruit fly and nematode). We first established that these PDZ-binding motifs are indeed preferentially present at their C-terminal ends. Moreover, we found specific amino acid (AA) bias for the 'x' positions in the motifs at the C-terminal ends. In general, hydrophilic AAs were favored. Our genomics-based findings confirm and largely extend the results of previous interaction-based studies, allowing us to propose refined consensus sequences for all of the examined PDZ-binding motifs. An ontological analysis revealed that the refined motifs are functionally relevant since a large fraction of the proteins bearing the motif appear to be involved in signal transduction. Furthermore, co-precipitation experiments confirmed two new protein interactions predicted by our genomics-based approach. Finally, we show that influenza virus pathogenicity can be correlated with PDZ-binding motif, with high-virulence viral proteins bearing a refined PDZ-binding motif.</p> <p>Conclusions</p> <p>Our refined definition of PDZ-binding motifs should provide important clues for identifying functional PDZ-binding motifs and proteins involved in signal transduction.</p

Design of protein-protein interaction specificity using computational methods and experimental library screening

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Cataloged from PDF version of thesis.Includes bibliographical references.Computational design of protein-protein interaction specificity is a powerful tool to examine and expand our understanding about how protein sequence determines interaction specificity. It also has many applications in basic bioscience and biotechnology. One of the major challenges for design is that current scoring functions relying on general physical principles do not always make reliable predictions about interaction specificity. In this thesis I described application of two approaches to address this problem. The first approach sought to improve scoring functions with experimental interaction specificity data related to the protein family of design interest. I used this approach to design inhibitor peptides against the viral bZIP protein BZLF 1. Specificity against design self-interaction was considered in the study. The second approach exploited the power of experimental library screening to characterize a large number of designed sequences at once, increasing the overall probability of identifying successful designs. I presented a novel framework for such library design approach and applied it to the design of anti-apoptotic Bcl-2 proteins with novel interaction specificity toward BH3 peptides. Finally I proposed how these two approaches can be combined together to further enhance our design capabilities.by Tsan-Chou Scott Chen.Ph.D

Determining protein interaction specificity of native and designed bZIP family transcription factors

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Biology, 2012.Page 428 blank. Cataloged from PDF version of thesis.Includes bibliographical references.Protein-protein interactions are important for almost all cellular functions. Knowing which proteins interact with one another is important for understanding protein function as well as for being able to disrupt their interactions. The basic leucine-zipper transcription factors (bZIPs) are a class of eukaryotic transcription factors that form either homodimers or heterodimers that bind to DNA in a site-specific manner. bZIPs are similar in sequence and structure, yet bZIP protein-protein interactions are specific, and this specificity is important for determining which DNA sites are bound. bZIP proteins have a simple structure that makes them experimentally tractable and well suited for developing models of interaction specificity. While current models perform well at being able to distinguish interactions from non-interactions, they are not fully accurate or able to predict interaction affinity. Our current understanding of protein interaction specificity is limited by the small number of large, high-quality interaction data sets that can be analyzed. For my thesis work I took a biophysical approach to experimentally measure the interactions of many native and designed bZIP and bZIP-like proteins in a high-throughput manner. The first method I used involved protein arrays containing small spots of bZIP-derived peptides immobilized on glass slides, which were probed with fluorescently labeled candidate protein partners. To improve upon this technique, I developed a solution-based FRET assay. In this experiment, two different dye-labeled versions of each protein are purified and mixed together at multiple concentrations to generate binding curves that quantify the affinity of each pair-wise interaction. Using the array assay, I identified novel interactions between human proteins and virally encoded bZIPs, characterized peptides designed to bind specifically to native bZIPs, and measured the interactions of a large set of synthetic bZIP-like coiled coils. Using the solution-based FRET assay, I quantified the bZIP interaction networks of five metazoan species and observed conservation as well as rewiring of interactions throughout evolution. Together, these studies have identified new interactions, created peptide reagents, identified sequence determinants of interaction specificity, and generated large amounts of interaction data that will help in the further understanding of bZIP protein interaction specificity.by Aaron W. Reinke.Ph.D

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