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.     

Characterization of domain-peptide interaction interface: a case study on the amphiphysin-1 SH3 domain

Many important protein-protein interactions are mediated by peptide recognition modular domains, such as the Src homology 3 (SH3), SH2, PDZ, and WW domains. Characterizing the interaction interface of domain-peptide complexes and predicting binding specificity for modular domains are critical for deciphering protein-protein interaction networks. Here, we propose the use of an energetic decomposition analysis to characterize domain-peptide interactions and the molecular interaction energy components (MIECs), including van der Waals, electrostatic, and desolvation energy between residue pairs on the binding interface. We show a proof-of-concept study on the amphiphysin-1 SH3 domain interacting with its peptide ligands. The structures of the human amphiphysin-1 SH3 domain complexed with 884 peptides were first modeled using virtual mutagenesis and optimized by molecular mechanics (MM) minimization. Next, the MIECs between domain and peptide residues were computed using the MM/generalized Born decomposition analysis. We conducted two types of statistical analyses on the MIECs to demonstrate their usefulness for predicting binding affinities of peptides and for classifying peptides into binder and non-binder categories. First, combining partial least squares analysis and genetic algorithm, we fitted linear regression models between the MIECs and the peptide binding affinities on the training data set. These models were then used to predict binding affinities for peptides in the test data set; the predicted values have a correlation coefficient of 0.81 and an unsigned mean error of 0.39 compared with the experimentally measured ones. The partial least squares-genetic algorithm analysis on the MIECs revealed the critical interactions for the binding specificity of the amphiphysin-1 SH3 domain. Next, a support vector machine (SVM) was employed to build classification models based on the MIECs of peptides in the training set. A rigorous training-validation procedure was used to assess the performances of different kernel functions in SVM and different combinations of the MIECs. The best SVM classifier gave satisfactory predictions for the test set, indicated by average prediction accuracy rates of 78% and 91% for the binding and non-binding peptides, respectively. We also showed that the performance of our approach on both binding affinity prediction and binder/non-binder classification was superior to the performances of the conventional MM/Poisson-Boltzmann solvent-accessible surface area and MM/generalized Born solvent-accessible surface area calculations. Our study demonstrates that the analysis of the MIECs between peptides and the SH3 domain can successfully characterize the binding interface, and it provides a framework to derive integrated prediction models for different domain-peptide systems.     

A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity

MOTIVATION: Unravelling the rules underlying protein-protein and protein-ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein-protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain-peptide interactions. RESULTS: We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain-peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the 'curse of dimension'. Our results display an accuracy >90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data.     

Evolving specificity from variability for protein interaction domains

An important question in modular domain-peptide interactions, which play crucial roles in many biological processes, is how the diverse specificities exhibited by different members of a domain family are encoded in a common scaffold. Analysis of the Src homology (SH) 2 family has revealed that its specificity is determined, in large part, by the configuration of surface loops that regulate ligand access to binding pockets. In a distinct manner, SH3 domains employ loops for ligand recognition. The PDZ domain, in contrast, achieves specificity by co-evolution of binding-site residues. Thus, the conformational and sequence variability afforded by surface loops and binding sites provides a general mechanism by which to encode the wide spectrum of specificities observed for modular protein interaction domains.     

Bayesian Modeling of the Yeast SH3 Domain Interactome Predicts Spatiotemporal Dynamics of Endocytosis Proteins

SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast, Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.     

Proteome-wide detection of Abl1 SH3-binding peptides by integrating computational prediction and peptide microarray

Protein-protein interactions are essential for regulating almost all aspects of cellular functions. Many of these interactions are mediated by weak and transient protein domain-peptide binding, but they are often under-represented in high throughput screening of protein-protein interactions using techniques such as yeast two-hybrid and mass spectrometry. On the other hand, computational predictions and in vitro binding assays are valuable in providing clues of in vivo interactions. We present here a systematic approach that integrates computer modeling and a peptide microarray technology to identify binding peptides of the SH3 domain of the tyrosine kinase Abl1 in the human proteome. Our study provides a comprehensive list of candidate interacting partners for the Abl1 protein, among which the presence of numerous methyltransferases and RNA splicing proteins may suggest a novel function of Abl1 in chromatin remodeling and RNA processing. This study illustrates a powerful approach for integrating computational and experimental methods to detect protein interactions mediated by domain-peptide recognition.     

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 peptides using a method called MIECSVM, 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.     

New approaches to high-throughput structure characterization of SH3 complexes: the example of Myosin-3 and Myosin-5 SH3 domains from S. cerevisiae

SH3 domains are small protein modules that are involved in protein-protein interactions in several essential metabolic pathways. The availability of the complete genome and the limited number of clearly identifiable SH3 domains make the yeast Saccharomyces cerevisae an ideal proteomic-based model system to investigate the structural rules dictating the SH3-mediated protein interactions and to develop new tools to assist these studies. In the present work, we have determined the solution structure of the SH3 domain from Myo3 and modeled by homology that of the highly homologous Myo5, two myosins implicated in actin polymerization. We have then implemented an integrated approach that makes use of experimental and computational methods to characterize their binding properties. While accommodating their targets in the classical groove, the two domains have selectivity in both orientation and sequence specificity of the target peptides. From our study, we propose a consensus sequence that may provide a useful guideline to identify new natural partners and suggest a strategy of more general applicability that may be of use in other structural proteomic studies.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

SH3 Domain-Peptide Binding Energy Calculations Based on Structural Ensemble and Multiple Peptide Templates

SH3 domains mediate signal transduction by recognizing short peptides. Understanding of the driving forces in peptide recognitions will help us to predict the binding specificity of the domain-peptide recognition and to understand the molecular interaction networks of cells. However, accurate calculation of the binding energy is a tough challenge. In this study, we propose three ideas for improving our ability to predict the binding energy between SH3 domains and peptides: (1) utilizing the structural ensembles sampled from a molecular dynamics simulation trajectory, (2) utilizing multiple peptide templates, and (3) optimizing the sequence-structure mapping. We tested these three ideas on ten previously studied SH3 domains for which SPOT analysis data were available. The results indicate that calculating binding energy using the structural ensemble was most effective, clearly increasing the prediction accuracy, while the second and third ideas tended to give better binding energy predictions. We applied our method to the five SH3 targets in DREAM4 Challenge and selected the best performing method.     

Genome-wide prediction of SH2 domain targets using structural information and the FoldX algorithm

Current experiments likely cover only a fraction of all protein-protein interactions. Here, we developed a method to predict SH2-mediated protein-protein interactions using the structure of SH2-phosphopeptide complexes and the FoldX algorithm. We show that our approach performs similarly to experimentally derived consensus sequences and substitution matrices at predicting known in vitro and in vivo targets of SH2 domains. We use our method to provide a set of high-confidence interactions for human SH2 domains with known structure filtered on secondary structure and phosphorylation state. We validated the predictions using literature-derived SH2 interactions and a probabilistic score obtained from a naive Bayes integration of information on coexpression, conservation of the interaction in other species, shared interaction partners, and functions. We show how our predictions lead to a new hypothesis for the role of SH2 domains in signaling.     

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.     

Tandem SH2 binding sites mediate the RasGAP-RhoGAP interaction: a conformational mechanism for SH3 domain regulation.

Many cellular signaling proteins contain SH3 (Src homology 3) domains that mediate protein interactions via specific proline-containing peptides. Unlike SH2 domains, whose interactions with tyrosine-containing peptides are promoted by phosphorylation of the SH2 binding site, the regulatory mechanism for SH3 interactions is unclear. p120 RasGAP (GTPase-activating protein), which contains an SH3 domain flanked by two SH2 domains, forms an abundant SH2-mediated complex with p190 RhoGAP in cells expressing activated tyrosine kinases. We have identified two closely linked tyrosine-containing peptides in p190 that bind simultaneously to the RasGAP SH2 domains upon p190 phosphorylation. This interaction is expected to bring the two SH2 domains into close proximity. Consequently, RasGAP undergoes a conformational change that results in a 100-fold increase in the accessibility of the target binding surface of its SH3 domain. These results indicate that the tandem arrangement of SH2 and SH3 domains found in a variety of cellular signaling proteins can provide a conformational mechanism for regulating SH3-dependent interactions through tyrosine phosphorylation. In addition, it appears that the role of p190 in the RasGAP signaling complex is to promote additional protein interactions with RasGAP via its SH3 domain.     

Specificity of the binding of synapsin I to Src homology 3 domains

Synapsins are synaptic vesicle-associated phosphoproteins involved in synapse formation and regulation of neurotransmitter release. Recently, synapsin I has been found to bind the Src homology 3 (SH3) domains of Grb2 and c-Src. In this work we have analyzed the interactions between synapsins and an array of SH3 domains belonging to proteins involved in signal transduction, cytoskeleton assembly, or endocytosis. The binding of synapsin I was specific for a subset of SH3 domains. The highest binding was observed with SH3 domains of c-Src, phospholipase C-gamma, p85 subunit of phosphatidylinositol 3-kinase, full-length and NH(2)-terminal Grb2, whereas binding was moderate with the SH3 domains of amphiphysins I/II, Crk, alpha-spectrin, and NADPH oxidase factor p47(phox) and negligible with the SH3 domains of p21(ras) GTPase-activating protein and COOH-terminal Grb2. Distinct sites in the proline-rich COOH-terminal region of synapsin I were found to be involved in binding to the various SH3 domains. Synapsin II also interacted with SH3 domains with a partly distinct binding pattern. Phosphorylation of synapsin I in the COOH-terminal region by Ca(2+)/calmodulin-dependent protein kinase II or mitogen-activated protein kinase modulated the binding to the SH3 domains of amphiphysins I/II, Crk, and alpha-spectrin without affecting the high affinity interactions. The SH3-mediated interaction of synapsin I with amphiphysins affected the ability of synapsin I to interact with actin and synaptic vesicles, and pools of synapsin I and amphiphysin I were shown to associate in isolated nerve terminals. The ability to bind multiple SH3 domains further implicates the synapsins in signal transduction and protein-protein interactions at the nerve terminal level.     

Network evolution: rewiring and signatures of conservation in signaling

The analysis of network evolution has been hampered by limited availability of protein interaction data for different organisms. In this study, we investigate evolutionary mechanisms in Src Homology 3 (SH3) domain and kinase interaction networks using high-resolution specificity profiles. We constructed and examined networks for 23 fungal species ranging from Saccharomyces cerevisiae to Schizosaccharomyces pombe. We quantify rates of different rewiring mechanisms and show that interaction change through binding site evolution is faster than through gene gain or loss. We found that SH3 interactions evolve swiftly, at rates similar to those found in phosphoregulation evolution. Importantly, we show that interaction changes are sufficiently rapid to exhibit saturation phenomena at the observed timescales. Finally, focusing on the SH3 interaction network, we observe extensive clustering of binding sites on target proteins by SH3 domains and a strong correlation between the number of domains that bind a target protein (target in-degree) and interaction conservation. The relationship between in-degree and interaction conservation is driven by two different effects, namely the number of clusters that correspond to interaction interfaces and the number of domains that bind to each cluster leads to sequence specific conservation, which in turn results in interaction conservation. In summary, we uncover several network evolution mechanisms likely to generalize across peptide recognition modules.     

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.     

Identification of an intramolecular interaction between the SH3 and guanylate kinase domains of PSD-95.

Postsynaptic density-95 (PSD-95/SAP-90) is a member of the membrane-associated guanylate kinase (MAGUK) family of proteins that assemble protein complexes at synapses and other cell junctions. MAGUKs comprise multiple protein-protein interaction motifs including PDZ, SH3 and guanylate kinase (GK) domains, and these binding sites mediate the scaffolding function of MAGUK proteins. Synaptic binding partners for the PDZ and GK domains of PSD-95 have been identified, but the role of the SH3 domain remains elusive. We now report that the SH3 domain of PSD-95 mediates a specific interaction with the GK domain. The GK domain lacks a poly-proline motif that typically binds to SH3 domains; instead, SH3/GK binding is a bi-domain interaction that requires both intact motifs. Although isolated SH3 and GK domains can bind in trans, experiments with intact PSD-95 molecules indicate that intramolecular SH3/GK binding dominates and prevents intermolecular associations. SH3/GK binding is conserved in the related Drosophila MAGUK protein DLG but is not detectable for Caenorhabditis elegans LIN-2. Many previously identified genetic mutations of MAGUKs in invertebrates occur in the SH3 or GK domains, and all of these mutations disrupt intramolecular SH3/GK binding.