Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models

Determination of the binding specificity of SH3 domain, a peptide recognition module (PRM), is important to understand their biological functions and reconstruct the SH3-mediated protein-protein interaction network. In the present study, the SH3-peptide interactions for both class I and II SH3 domains were characterized by the intermolecular residue-residue interaction network. We developed generic MIEC-SVM models to infer SH3 domain-peptide recognition specificity that achieved satisfactory prediction accuracy. By investigating the domain-peptide recognition mechanisms at the residue level, we found that the class-I and class-II binding peptides have different binding modes even though they occupy the same binding site of SH3. Furthermore, we predicted the potential binding partners of SH3 domains in the yeast proteome and constructed the SH3-mediated protein-protein interaction network. Comparison with the experimentally determined interactions confirmed the effectiveness of our approach. This study showed that our sophisticated computational approach not only provides a powerful platform to decipher protein recognition code at the molecular level but also allows identification of peptide-mediated protein interactions at a proteomic scale. We believe that such an approach is general to be applicable to other domain-peptide interactions.     

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.     

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.     

A novel pro-Arg motif recognized by WW domains

WW domains mediate protein-protein interactions through binding to short proline-rich sequences. Two distinct sequence motifs, PPXY and PPLP, are recognized by different classes of WW domains, and another class binds to phospho-Ser-Pro sequences. We now describe a novel Pro-Arg sequence motif recognized by a different class of WW domains using data from oriented peptide library screening, expression cloning, and in vitro binding experiments. The prototype member of this group is the WW domain of formin-binding protein 30 (FBP30), a p53-regulated molecule whose WW domains bind to Pro-Arg-rich cellular proteins. This new Pro-Arg sequence motif re-classifies the organization of WW domains based on ligand specificity, and the Pro-Arg class now includes the WW domains of FBP21 and FE65. A structural model is presented which rationalizes the distinct motifs selected by the WW domains of YAP, Pin1, and FBP30. The Pro-Arg motif identified for WW domains often overlaps with SH3 domain motifs within protein sequences, suggesting that the same extended proline-rich sequence could form discrete SH3 or WW domain complexes to transduce distinct cellular signals.     

Using genome-wide measurements for computational prediction of SH2–peptide interactions

Peptide-recognition modules (PRMs) are used throughout biology to mediate protein–protein interactions, and many PRMs are members of large protein domain families. Recent genome-wide measurements describe networks of peptide–PRM interactions. In these networks, very similar PRMs recognize distinct sets of peptides, raising the question of how peptide-recognition specificity is achieved using similar protein domains. The analysis of individual protein complex structures often gives answers that are not easily applicable to other members of the same PRM family. Bioinformatics-based approaches, one the other hand, may be difficult to interpret physically. Here we integrate structural information with a large, quantitative data set of SH2 domain–peptide interactions to study the physical origin of domain–peptide specificity. We develop an energy model, inspired by protein folding, based on interactions between the amino-acid positions in the domain and peptide. We use this model to successfully predict which SH2 domains and peptides interact and uncover the positions in each that are important for specificity. The energy model is general enough that it can be applied to other members of the SH2 family or to new peptides, and the cross-validation results suggest that these energy calculations will be useful for predicting binding interactions. It can also be adapted to study other PRM families, predict optimal peptides for a given SH2 domain, or study other biological interactions, e.g. protein–DNA interactions.     

Identification of Src, Fyn, Lyn, PI3K and Abl SH3 domain ligands using phage display libraries.

Many proteins involved in intracellular signal transduction contain a small, 50-60 amino acid domain, termed the Src homology 3 (SH3) domain. This domain appears to mediate critical protein-protein interactions that are involved in responses to extracellular signals. Previous studies have shown that the SH3 domains from several proteins recognize short, contiguous amino acid sequences that are rich in proline residues. While all SH3 recognition sequences identified to date share a conserved P-X-X-P motif, the sequence recognition specificity of individual SH3 domains is poorly understood. We have employed a novel modification of phage display involving biased libraries to identify peptide ligands of the Src, Fyn, Lyn, PI3K and Abl SH3 domains. With biased libraries, we probed SH3 recognition over a 12 amino acid window. The Src SH3 domain prefers the sequence XXXRPLPPLPXP, Fyn prefers XXXRPLPP(I/L)PXX, Lyn prefers RXXRPLPPLPXP, PI3K prefers RXXRPLPPLPP while the Abl SH3 domain selects phage containing the sequence PPPYPPPP(I/V)PXX. We have also analysed the binding properties of Abl and Src SH3 ligands. We find that although the phage-displayed Abl and Src SH3 ligands are proline rich, they are distinct. In surface plasmon resonance binding assays, these SH3 domains displayed highly selective binding to their cognate ligands when the sequences were displayed on the surface of the phage or as synthetic peptides. The selection of these high affinity SH3 peptide ligands provides valuable information on the recognition motifs of SH3 domains, serve as new tools to interfere with the cellular functions of SH3 domain-mediated processes and form the basis for the design of SH3-specific inhibitors of disease pathways.     

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.     

A Conserved residue in the yeast Bem1p SH3 domain maintains the high level of binding specificity required for function

The yeast Bem1p SH3b and Nbp2p SH3 domains are unusual because they bind to peptides containing the same consensus sequence, yet they perform different functions and display low sequence similarity. In this work, by analyzing the interactions of these domains with six biologically relevant peptides containing the consensus sequence, they are shown to possess finely tuned and distinct binding specificities. We also identify a residue in the Bem1p SH3b domain that inhibits binding, yet is highly conserved for the purpose of preventing nonspecific interactions. Substitution of this residue results in a marked reduction of in vivo function that is caused by titration of the domain away from its proper targets through nonspecific interactions with other proteins. This work provides a clear illustration of the importance of intrinsic binding specificity for the function of protein-protein interaction modules, and the key role of "negative" interactions in determining the specificity of a domain.     

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.     

Binding specificity of SH2 domains: insight from free energy simulations

Cellular signal transduction pathways are controlled by specific protein-protein interactions mediated by the binding of short peptides to small modular interaction domains. To gain insights into the specificity of these interactions, the association of phosphotyrosine-containing peptides to Src Homology 2 (SH2) domains is characterized using computations. Molecular dynamics simulations based on high-resolution crystal structures complemented by homology models are used to calculate the absolute binding free energies for 25 SH2-peptides pairs. The calculations are carried out using a potential of mean force free energy simulations method with restraining potentials that was developed previously (Woo and Roux, Proc Natl Acad Sci USA 2005;102:6825-6830). The method is utilized in conjunction with an implicit solvent representation to reduce the computational cost to characterize the association of five SH2 domains and five peptides. Specificity is ascertained by directly comparing the affinities of a given SH2 domain binding for any of the different peptides. For three of the five SH2 domains, the computational results rank the native peptides, as the most preferred binding motif. For the remaining two SH2 domains, high affinity binding motifs other than the native peptides are identified. This study illustrates how free energy computations can complement experiments in trying to elucidate complex protein-protein interactions networks.     

Defining SH2 domain and PTP specificity by screening combinatorial peptide libraries

Src homology 2 (SH2) domains mediate protein-protein interactions by recognizing short phosphotyrosyl (pY) peptide motifs in their partner proteins. Protein tyrosine phosphatases (PTPs) catalyze the dephosphorylation of pY proteins, counteracting the protein tyrosine kinases. Both types of proteins exhibit primary sequence specificity, which plays at least a partial role in dictating their physiological interacting partners or substrates. A combinatorial peptide library method has been developed to systematically assess the sequence specificity of SH2 domains and PTPs. A "one-bead-one-compound" pY peptide library is synthesized on 90-microm TentaGel beads and screened against an SH2 domain or PTP of interest for binding or catalysis. The beads that carry the tightest binding sequences against the SH2 domain or the most efficient substrates of the PTP are selected by an enzyme-linked assay and individually sequenced by a partial Edman degradation/mass spectrometry technique. The combinatorial method has been applied to determine the sequence specificity of 8 SH2 domains from Src and Csk kinases, adaptor protein Grb2, and phosphatases SHP-1, SHP-2, and SHIP1 and a prototypical PTP, PTP1B.     

Promiscuous binding nature of SH3 domains to their target proteins

SH3 domains are small but important domains in cell-signaling and function through protein-protein interactions. Their promiscuous nature in binding to polyproline peptides makes them much more important because many SH3 domains from different proteins bind to different proteins having polyproline template on their surface. Very subtle changes in the sequence of SH3 domains and the binding peptides determine the specificity of the peptide binding. Recent observation that SH3 domains bind to non- proline peptides makes the scenario of peptide binding involving SH3 domains complicated. If domain swapped dimerization as observed in Eps8-SH3 domain also binds different peptides, it proves the versatility of the SH3 domains in binding to peptides in various ways. An overview of the promiscuity of SH3 domains has been discussed.     

Regulatory role of SH3 domain-mediated protein-protein interactions in synaptic vesicle endocytosis.

Src homology (SH) 3 domains are small modules found in a diverse array of proteins. The presence of an SH3 domain confers upon its resident protein the ability to interact with specific proline-rich sequences in protein binding partners. A major focus of research has highlighted a role for SH3 domain-mediated interactions in the regulation of signal transduction events. However, more recent data has suggested an important function for SH3 domains in vesicular trafficking. This review will focus on this newly emerging role with a particular emphasis on the molecular components involved in synaptic vesicle endocytosis and the regulatory role of SH3 domain-mediated protein-protein interactions in this process.     

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.     

Boundary sequences of the NADPH oxidase p67(phox) C-terminal SH3 domain play on its specificity.

SH3 domains are found in many signal transduction proteins where they mediate protein-protein binding by recognizing specific peptides rich in proline. Based on the analysis of sequence alignment data, the NADPH oxidase p67(phox) C-terminal SH3 domain possesses a typical compact beta-barrel consisting of five beta-strands arranged in two antiparallel beta-sheets of three and two beta-strands. Multiple amino acid substitutions were made at beta e and its flanking residues to determine the role of the boundary sequences in binding activity and conformational specificity of the domain. Analysis of amino acid P55 indicated that all mutants were completely abolished in their binding activities. The substitution of F58 with Y58 showed no effect of the binding, whereas substitution with stop codon abolished activity. Furthermore, when amino acid V59 was substituted with stop codon, activity was also completely abolished. Substitution of E60 with stop codon showed no effect of binding. Moreover, our data show that V59 particularly could not be replaced by Leu. Taken together, these data suggest that V59 may not only contribute the exact boundary site but also play on the specificity for protein-protein interactions in phagocyte NADPH oxidase.     

A dynamic view of domain-motif interactions

Many protein-protein interactions are mediated by domain-motif interaction, where a domain in one protein binds a short linear motif in its interacting partner. Such interactions are often involved in key cellular processes, necessitating their tight regulation. A common strategy of the cell to control protein function and interaction is by post-translational modifications of specific residues, especially phosphorylation. Indeed, there are motifs, such as SH2-binding motifs, in which motif phosphorylation is required for the domain-motif interaction. On the contrary, there are other examples where motif phosphorylation prevents the domain-motif interaction. Here we present a large-scale integrative analysis of experimental human data of domain-motif interactions and phosphorylation events, demonstrating an intriguing coupling between the two. We report such coupling for SH3, PDZ, SH2 and WW domains, where residue phosphorylation within or next to the motif is implied to be associated with switching on or off domain binding. For domains that require motif phosphorylation for binding, such as SH2 domains, we found coupled phosphorylation events other than the ones required for domain binding. Furthermore, we show that phosphorylation might function as a double switch, concurrently enabling interaction of the motif with one domain and disabling interaction with another domain. Evolutionary analysis shows that co-evolution of the motif and the proximal residues capable of phosphorylation predominates over other evolutionary scenarios, in which the motif appeared before the potentially phosphorylated residue, or vice versa. Our findings provide strengthening evidence for coupled interaction-regulation units, defined by a domain-binding motif and a phosphorylated residue.     

Intramolecular interactions regulate SAP97 binding to GKAP

Membrane-associated guanylate kinase homologs (MAGUKs) are multidomain proteins found to be central organizers of cellular junctions. In this study, we examined the molecular mechanisms that regulate the interaction of the MAGUK SAP97 with its GUK domain binding partner GKAP (GUK-associated protein). The GKAP-GUK interaction is regulated by a series of intramolecular interactions. Specifically, the association of the Src homology 3 (SH3) domain and sequences situated between the SH3 and GUK domains with the GUK domain was found to interfere with GKAP binding. In contrast, N-terminal sequences that precede the first PDZ domain in SAP97, facilitated GKAP binding via its association with the SH3 domain. Utilizing crystal structure data available for PDZ, SH3 and GUK domains, molecular models of SAP97 were generated. These models revealed that SAP97 can exist in a compact U-shaped conformation in which the N-terminal domain folds back and interacts with the SH3 and GUK domains. These models support the biochemical data and provide new insights into how intramolecular interactions may regulate the association of SAP97 with its binding partners.     

4-Fluoroproline derivative peptides: effect on PPII conformation and SH3 affinity.

Eukaryotic signal transduction involves the assembly of transient protein-protein complexes mediated by modular interaction domains. Specific Pro-rich sequences with the consensus core motif PxxP adopt the PPII helix conformation upon binding to SH3 domains. For short Pro-rich peptides, little or no ordered secondary structure is usually observed before binding interactions. The association of a Pro-rich peptide with the SH3 domain involves unfavorable binding entropy due to the loss of rotational freedom on forming the PPII helix. With the aim of stabilizing the PPII helix conformation in the Pro-rich HPK1 decapeptide PPPLPPKPKF (P2), a series of P2 analogues was prepared, in which specific Pro positions were alternatively occupied by 4(S)- or 4(R)-4-fluoro-L-proline. The interactions of these peptides with the SH3 domain of the HPK1-binding partner HS1 were quantitatively analyzed by the NILIA-CD approach. A CD thermal analysis of the P2 analogues was performed to assess their propensity to adopt the PPII helix conformation. Contrary to our expectations, the K(d) values of the analogues were lower than that of the parent peptide P2. These results clearly show that the induction of a stable PPII helix conformation in short Pro-rich peptides is not sufficient to increase their affinity toward the SH3 domain and that the effect of 4-fluoroproline strongly depends on the position of this residue in the sequence and the chirality of the substituent in the pyrrolidine ring.     

Structure-function analysis of SH3 domains: SH3 binding specificity altered by single amino acid substitutions.

SH3 domains mediate intracellular protein-protein interactions through the recognition of proline-rich sequence motifs on cellular proteins. Structural analysis of the Src SH3 domain (Src SH3) complexed with proline-rich peptide ligands revealed three binding sites involved in this interaction: two hydrophobic interactions (between aliphatic proline dipeptides in the SH3 ligand and highly conserved aromatic residues on the surface of the SH3 domain), and one salt bridge (between Asp-99 of Src and an Arg three residues upstream of the conserved Pro-X-X-Pro motif in the ligand). We examined the importance of the arginine binding site of SH3 domains by comparing the binding properties of wild-type Src SH3 and Abl SH3 with those of a Src SH3 mutant containing a mutated arginine binding site (D99N) and Abl SH3 mutant constructs engineered to contain an arginine binding site (T98D and T98D/F91Y). We found that the D99N mutation diminished binding to most Src SH3-binding proteins in whole cell extracts; however, there was only a moderate reduction in binding to a small subset of Src SH3-binding proteins (including the Src substrate p68). p68 was shown to contain two Arg-containing Asp-99-dependent binding sites and one Asp-99-independent binding site which lacks an Arg. Moreover, substitution of Asp for Thr-98 in Abl SH3 changed the binding specificity of this domain and conferred the ability to recognize Arg-containing ligands. These results indicate that Asp-99 is important for Src SH3 binding specificity and that Asp-99-dependent binding interactions play a dominant role in Src SH3 recognition of cellular binding proteins, and they suggest the existence of two Src SH3 binding mechanisms, one requiring Asp-99 and the other independent of this residue.