Domain Analysis and Motif Matcher (DAMM): A Program to Predict Selectivity Determinants in Monosiga brevicollis PDZ Domains Using Human PDZ Data

Choanoflagellates are single-celled eukaryotes with complex signaling pathways. They are considered the closest non-metazoan ancestors to mammals and other metazoans and form multicellular-like states called rosettes. The choanoflagellate Monosiga brevicollis contains over 150 PDZ domains, an important peptide-binding domain in all three domains of life (Archaea, Bacteria, and Eukarya). Therefore, an understanding of PDZ domain signaling pathways in choanoflagellates may provide insight into the origins of multicellularity. PDZ domains recognize the C-terminus of target proteins and regulate signaling and trafficking pathways, as well as cellular adhesion. Here, we developed a computational software suite, Domain Analysis and Motif Matcher (DAMM), that analyzes peptide-binding cleft sequence identity as compared with human PDZ domains and that can be used in combination with literature searches of known human PDZ-interacting sequences to predict target specificity in choanoflagellate PDZ domains. We used this program, protein biochemistry, fluorescence polarization, and structural analyses to characterize the specificity of A9UPE9_MONBE, a M. brevicollis PDZ domain-containing protein with no homology to any metazoan protein, finding that its PDZ domain is most similar to those of the DLG family. We then identified two endogenous sequences that bind A9UPE9 PDZ with <100 μM affinity, a value commonly considered the threshold for cellular PDZ–peptide interactions. Taken together, this approach can be used to predict cellular targets of previously uncharacterized PDZ domains in choanoflagellates and other organisms. Our data contribute to investigations into choanoflagellate signaling and how it informs metazoan evolution.     

Scaffold-independent analysis of RNA-protein interactions: the Nova-1 KH3-RNA complex

We describe a method to analyze the sequence specificity of an RNA-binding domain. The method, which we have named scaffold-independent analysis, reports on the specificity for each nucleotide position within an RNA target, uncoupled from the surrounding structural and sequence context. We expect this information to improve our understanding of protein-RNA interfaces in ssRNA binding domains (e.g., KH or RRM domains) and to be useful to the design of novel protein-RNA recognition surfaces. Our NMR binding assays using the third KH domain of the Nova-1 protein provide a proof-of-principle for the method and novel information on the specificity of this domain for its RNA targets.     

A systematic family-wide investigation reveals that ~30% of mammalian PDZ domains engage in PDZ-PDZ interactions

PDZ domains are independently folded modules that typically mediate protein-protein interactions by binding to the C termini of their target proteins. However, in a few instances, PDZ domains have been reported to dimerize with other PDZ domains. To investigate this noncanonical-binding mode further, we used protein microarrays comprising virtually every mouse PDZ domain to systematically query all possible PDZ-PDZ pairs. We then used fluorescence polarization to retest and quantify interactions and coaffinity purification to test biophysically validated interactions in the context of their full-length proteins. Overall, we discovered 37 PDZ-PDZ interactions involving 46 PDZ domains (~30% of all PDZ domains tested), revealing that dimerization is a more frequently used binding mode than was previously appreciated. This suggests that many PDZ domains evolved to form multiprotein complexes by simultaneously interacting with more than one ligand.     

A fluorescence polarization based screening assay for identification of small molecule inhibitors of the PICK1 PDZ domain

PDZ (PSD-95/Discs-large/ZO-1 homology) domains represent putative targets in several diseases including cancer, stroke, addiction and neuropathic pain. Here we describe the application of a simple and fast screening assay based on fluorescence polarization (FP) to identify inhibitors of the PDZ domain in PICK1 (protein interacting with C kinase 1). We screened 43,380 compounds for their ability to inhibit binding of an Oregon Green labeled C-terminal dopamine transporter peptide (OrG-DAT C13) to purified PICK1 in solution. The assay was highly reliable with excellent screening assay parameters (Z'≈0.7 and Z≈0.6). Out of ~200 compounds that reduced FP to less than 80% of the control wells, six compounds were further characterized. The apparent affinities of the compounds were determined in FP competition binding experiments and ranged from ~5.0 μM to ~193 μM. Binding to the PICK1 PDZ domain was confirmed for five of the compounds (CSC-03, CSC-04, CSC-43, FSC-231 and FSC-240) in a non-fluorescence based assay by their ability to inhibit pull-down of PICK1 by a C-terminal DAT GST fusion protein. CSC-03 displayed the highest apparent affinity (5.0 μM) in the FP assay, and was according to fluorescence resonance energy transfer (FRET) experiments capable of inhibiting the interaction between the C-terminus of the GluR2 subunit of the AMPA-type glutamate receptor and PICK1 in live cells. Additional experiments suggested that CSC-03 most likely is an irreversible inhibitor but with specificity for PICK1 since it did not bind three different PDZ domains of PSD-95. Summarized, our data suggest that FP based screening assays might be a widely applicable tool in the search for small molecule inhibitors of PDZ domain interactions.     

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.     

A flexible docking procedure for the exploration of peptide binding selectivity to known structures and homology models of PDZ domains

PDZ domains are important scaffolding modules that typically bind to the C-termini of their interaction partners. Several structures of such complexes have been solved, revealing a conserved binding site in the PDZ domain and an extended conformation of the bound peptide. A compendium of information regarding PDZ complexes demonstrates that dissimilar C-terminal peptides bind to the same PDZ domain, and different PDZ domains can bind the same peptides. A detailed understanding of the PDZ-peptide recognition is needed to elucidate this complexity. To this end, we have designed a family of docking protocols for PDZ domains (termed PDZ-DocScheme) that is based on simulated annealing molecular dynamics and rotamer optimization, and is applicable to the docking of long peptides (20-40 rotatable bonds) to both known PDZ structures and to the more complicated problem of homology models of these domains. The resulting protocol reproduces the structures of PDZ complexes with peptides 4-8 amino acids long within 1-2 A from the experimental structure when the docking is performed to the original structure. If the structure of the target PDZ domain is an apo structure or a homology model, the docking protocol yields structures within 3 A in 9 out of 12 test cases. The automated docking procedure PDZ-DocScheme can serve in the generation of a structural context for validation of PDZ domain specificity from mutagenesis and ligand binding data.     

Prediction of number and position of domain boundaries in multi-domain proteins by use of amino acid sequence alone

Prediction of protein domain boundaries is an important step for the prediction of three-dimensional structure. The simple method PDP has been elaborated for prediction of the number and position of domain boundaries in multi-domain proteins by use of amino acid sequence alone. The method uses an optimized scale based on the statistics of appearance of amino acid residues at domain boundaries. Our method demonstrates promising results in comparison to other methods that do not use homologous sequences. From the database of proteins that are targets from CASP6 (Critical Assessment of Techniques for Protein Structure Prediction) our program correctly assigned the number of domains for approximately 80% of one domain proteins and approximately 50% for two-domain proteins. Our method offers three main advantages: it is very simple, it is fast, and it uses a minimal number of parameters in comparison with other methods.     

Uncovering quantitative protein interaction networks for mouse PDZ domains using protein microarrays

One of the principal challenges in systems biology is to uncover the networks of protein-protein interactions that underlie most biological processes. To date, experimental efforts directed at this problem have largely produced only qualitative networks that are replete with false positives and false negatives. Here, we describe a domain-centered approach--compatible with genome-wide investigations--that enables us to measure the equilibrium dissociation constant (K(D)) of recombinant PDZ domains for fluorescently labeled peptides that represent physiologically relevant binding partners. Using a pilot set of 22 PDZ domains, 4 PDZ domain clusters, and 20 peptides, we define a gold standard dataset by determining the K(D) for all 520 PDZ-peptide combinations using fluorescence polarization. We then show that microarrays of PDZ domains identify interactions of moderate to high affinity (K(D) < or = 10 microM) in a high-throughput format with a false positive rate of 14% and a false negative rate of 14%. By combining the throughput of protein microarrays with the fidelity of fluorescence polarization, our domain/peptide-based strategy yields a quantitative network that faithfully recapitulates 85% of previously reported interactions and uncovers new biophysical interactions, many of which occur between proteins that are co-expressed. From a broader perspective, the selectivity data produced by this effort reveal a strong concordance between protein sequence and protein function, supporting a model in which interaction networks evolve through small steps that do not involve dramatic rewiring of the network.     

An improved method for the synthesis of cellulose membrane-bound peptides with free C termini is useful for PDZ domain binding studies

SPOT synthesis permits parallel synthesis and screening of thousands of cellulose membrane-bound peptides to study protein-protein interactions in a proteomic context. Recognition of C-terminal residues is one of the most common binding features of PDZ domains. Unfortunately, most solid support-bound peptide libraries lack a free C terminus due to C-terminal fixation on the solid support. To overcome this restriction, we developed a robust methodology based on our previous strategy for generating peptides with authentic C termini. To validate this improved method, we screened a human peptide library of 6223 C termini with the syntrophin PDZ domain. Furthermore, using the same library, new peptide ligands derived from membrane proteins and receptors were found for the ERBIN PDZ domain. Finally, we identified the protein kinase breakpoint cluster region, which is known as a negative regulator of cell proliferation and oncogenic transformation, as an ERBIN ligand.     

Thermodynamic basis for promiscuity and selectivity in protein-protein interactions: PDZ domains, a case study

Like other protein-protein interaction domains, PDZ domains are involved in many key cellular processes. These processes often require that specific multiprotein complexes be assembled, a task that PDZ domains accomplish by binding to specific peptide motifs in target proteins. However, a growing number of experimental studies show that PDZ domains (like other protein-protein interaction domains) can engage in a variety of interactions and bind distinct peptide motifs. Such promiscuity in ligand recognition raises intriguing questions about the molecular and thermodynamic mechanisms that can sustain it. To identify possible sources of promiscuity and selectivity underlying PDZ domain interactions, we performed molecular dynamics simulations of 20 to 25 ns on a set of 12 different PDZ domain complexes (for the proteins PSD-95, Syntenin, Erbin, GRIP, NHERF, Inad, Dishevelled, and Shank). The electrostatic, nonpolar, and configurational entropy binding contributions were evaluated using the MM/PBSA method combined with a quasi-harmonic analysis. The results revealed that PDZ domain interactions are characterized by overwhelmingly favorable nonpolar contributions and almost negligible electrostatic components, a mix that may readily sustain promiscuity. In addition, despite the structural similarity in fold and in recognition modes, the entropic and other dynamical aspects of binding were remarkably variable not only across PDZ domains but also for the same PDZ domain bound to distinct ligands. This variability suggests that entropic and dynamical components can play a role in determining selectivity either of PDZ domain interactions with peptide ligands or of PDZ domain complexes with downstream effectors.     

Scaffold proteins and the regeneration of visual pigments

CRALBP, cellular retinaldehyde-binding protein, is a retinoid-binding protein necessary for efficient regeneration of rod and cone visual pigments. The C terminus of CRALBP binds to the PDZ domains of EBP50/NHERF-1, which in turn bind to ezrin and actin, proteins localized to the apical processes of the retinal pigment epithelium. In this study, we examined structural features associated with the interaction of the two proteins. The C-terminal amino-acid sequence of 11 orthologous CRALBPs is either ENTAL, ENTAF or EDTAL. Peptides ending in each of these sequences inhibited the interaction of CRALBP and EBP50/NHERF-1 with the use of an overlay assay. Molecular modeling showed that both NTAL and NTAF formed similar networks of H bonds with PDZ1 of EBP50/ NHERF-1, and the side chains of both C-terminal Leu and Phe fit into the peptide-binding groove of PDZ1x CRALBP.11-cis-retinal and EBP50/NHERF-1 migrated as single components when analyzed individually by gel filtration and as a complex when mixed together before gel filtration. Complex formation was abolished by preincubation of EBP50/NHERF-1 with peptide EVENTAL. The ligand absorption spectrum of the complex was identical with that of CRALBP x 11-cis-retinal, demonstrating that complex formation did not perturb the ligand-binding domain of CRALBP.     

Identifying and reducing error in cluster‐expansion approximations of protein energies

Protein design involves searching a vast space for sequences that are compatible with a defined structure. This can pose significant computational challenges. Cluster expansion is a technique that can accelerate the evaluation of protein energies by generating a simple functional relationship between sequence and energy. The method consists of several steps. First, for a given protein structure, a training set of sequences with known energies is generated. Next, this training set is used to expand energy as a function of clusters consisting of single residues, residue pairs, and higher order terms, if required. The accuracy of the sequence‐based expansion is monitored and improved using cross‐validation testing and iterative inclusion of additional clusters. As a trade‐off for evaluation speed, the cluster‐expansion approximation causes prediction errors, which can be reduced by including more training sequences, including higher order terms in the expansion, and/or reducing the sequence space described by thecluster expansion. This article analyzes the sources of error and introduces a method whereby accuracy can be improved by judiciously reducing the described sequence space. The method is applied to describe the sequence–stability relationship for several protein structures: coiled‐coil dimers and trimers, a PDZ domain, and T4 lysozyme as examples with computationally derived energies, and SH3 domains in amphiphysin‐1 and endophilin‐1 as examples where the expanded pseudo‐energies are obtained from experiments. Our open‐source software package Cluster Expansion Version 1.0 allows users to expand their own energy function of interest and thereby apply cluster expansion to custom problems in protein design. © 2010 Wiley Periodicals, Inc. J Comput Chem, 2010     

Co‐Evolutionary Fitness Landscapes for Sequence Design

Efficient and accurate models to predict the fitness of a sequence would be extremely valuable in protein design. We have explored the use of statistical potentials for the coevolutionary fitness landscape, extracted from known protein sequences, in conjunction with Monte Carlo simulations, as a tool for design. As proof of principle, we created a series of predicted high‐fitness sequences for three different protein folds, representative of different structural classes: the GA (all‐α) and GB (α/β) binding domains of streptococcal protein G, and an SH3 (all‐β) domain. We found that most of the designed proteins can fold stably to the target structure, and a structure for a representative of each for GA, GB and SH3 was determined. Several of our designed proteins were also able to bind to native ligands, in some cases with higher affinity than wild‐type. Thus, a search using a statistical fitness landscape is a remarkably effective tool for finding novel stable protein sequences.     

PPM-Dom: A novel method for domain position prediction

Domains are the structural basis of the physiological functions of proteins, and the prediction of which is an advantageous process on the study of protein structure and function. This article proposes a new complete automatic prediction method, PPM-Dom (Domain Position Prediction Method), for predicting the particular positions of domains in a target protein via its atomic coordinate. The presented method integrates complex networks, community division, and fuzzy mean operator (FMO). The whole sequences are divided into potential domain regions by the complex network and community division, and FMO allows the final determination for the domain position. This method will suffice to predict regions that will form a domain structure and those that are unstructured based on completely new atomic coordinate information of the query sequence, and be able to separate different domains in the same query sequence from each other. On evaluating the performance using an independent testing dataset, PPM-Dom reached 91.41% for prediction accuracy, 96.12% for sensitivity and 92.86% for specificity. The tool bag of PPM-Dom is freely available at http://cic.scu.edu.cn/bioinformatics/PPMDom.zip.     

Targeting specific PDZ domains of PSD-95; structural basis for enhanced affinity and enzymatic stability of a cyclic peptide

A cyclic peptide, Tyr-Lys-c[-Lys-Thr-Glu(betaAla)-]-Val, incorporating a beta-Ala lactam side chain linker and designed to target the PDZ domains of the postsynaptic density protein 95 (PSD-95), has been synthesized and structurally characterized by NMR while free and bound to the PDZ1 domain of PSD-95. While bound, the lactam linker of the peptide makes a number of unique contacts outside the canonical PDZ binding motif, providing a novel target for PDZ-domain specificity as well as producing a 10-fold enhancement in binding affinity. Additionally, the cyclization greatly enhances the enzymatic stability, increasing the duration that the peptide inhibits the association between PSD-95 and glutamate receptors, effectively inhibiting the clustering of kainate receptors for over 14 hr after application. Highly specific regulation of kainate receptor action may provide a novel route for treatment of drug addiction and epilepsy.     

Targeting metastable coiled-coil domains by computational design

Approximately 30% of eukaryotic genomes are predicted to encode partially unfolded proteins. Many of these unstructured domains contact multiple partners in short-lived interactions critical for cellular homeostasis. Understanding the functional implications of these transient binding events is a current challenge that could be addressed with designed peptide inhibitors. Most current protein design methodologies, however, target only structurally well-defined, stable structures. To address this limitation, we implemented a computational design strategy that alternates between a fixed backbone sequence search for binding specificity and structural optimization of the designed interfaces. We applied this method to create specific peptide inhibitors of the C-terminal metastable coiled-coil domain of the essential yeast septin Cdc12p. Specific binding of the designed sequences was demonstrated by circular dichroism and equilibrium ultracentrifugation. Our results validate computational methods to design specific peptide ligands to protein domains lacking intrinsic structural stability and set the stage for functional analysis of Cdc12p coiled coil function in vivo.