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.     

Rosetta FlexPepDock ab-initio: simultaneous folding, docking and refinement of peptides onto their receptors

Flexible peptides that fold upon binding to another protein molecule mediate a large number of regulatory interactions in the living cell and may provide highly specific recognition modules. We present Rosetta FlexPepDock ab-initio, a protocol for simultaneous docking and de-novo folding of peptides, starting from an approximate specification of the peptide binding site. Using the Rosetta fragments library and a coarse-grained structural representation of the peptide and the receptor, FlexPepDock ab-initio samples efficiently and simultaneously the space of possible peptide backbone conformations and rigid-body orientations over the receptor surface of a given binding site. The subsequent all-atom refinement of the coarse-grained models includes full side-chain modeling of both the receptor and the peptide, resulting in high-resolution models in which key side-chain interactions are recapitulated. The protocol was applied to a benchmark in which peptides were modeled over receptors in either their bound backbone conformations or in their free, unbound form. Near-native peptide conformations were identified in 18/26 of the bound cases and 7/14 of the unbound cases. The protocol performs well on peptides from various classes of secondary structures, including coiled peptides with unusual turns and kinks. The results presented here significantly extend the scope of state-of-the-art methods for high-resolution peptide modeling, which can now be applied to a wide variety of peptide-protein interactions where no prior information about the peptide backbone conformation is available, enabling detailed structure-based studies and manipulation of those interactions.     

Binding free energy landscape of domain-peptide interactions

Peptide recognition domains (PRDs) are ubiquitous protein domains which mediate large numbers of protein interactions in the cell. How these PRDs are able to recognize peptide sequences in a rapid and specific manner is incompletely understood. We explore the peptide binding process of PDZ domains, a large PRD family, from an equilibrium perspective using an all-atom Monte Carlo (MC) approach. Our focus is two different PDZ domains representing two major PDZ classes, I and II. For both domains, a binding free energy surface with a strong bias toward the native bound state is found. Moreover, both domains exhibit a binding process in which the peptides are mostly either bound at the PDZ binding pocket or else interact little with the domain surface. Consistent with this, various binding observables show a temperature dependence well described by a simple two-state model. We also find important differences in the details between the two domains. While both domains exhibit well-defined binding free energy barriers, the class I barrier is significantly weaker than the one for class II. To probe this issue further, we apply our method to a PDZ domain with dual specificity for class I and II peptides, and find an analogous difference in their binding free energy barriers. Lastly, we perform a large number of fixed-temperature MC kinetics trajectories under binding conditions. These trajectories reveal significantly slower binding dynamics for the class II domain relative to class I. Our combined results are consistent with a binding mechanism in which the peptide C terminal residue binds in an initial, rate-limiting step.     

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.     

Structural determinants of the Na+/H+ exchanger regulatory factor interaction with the beta 2 adrenergic and platelet-derived growth factor receptors

The Na(+)/H(+) exchanger regulatory factor (NHERF) binds through its PDZ1 domain to the carboxyl-terminal sequences NDSLL and EDSFL of the beta(2) adrenergic receptor (beta(2)AR) and platelet-derived growth factor receptor, respectively, and plays a critical role in the membrane localization and physiological regulation of these receptors. The crystal structures of the human NHERF PDZ1 domain bound to the sequences NDSLL and EDSFL have been determined at 1.9- and 2.2-A resolution, respectively. The beta(2)AR and platelet-derived growth factor receptor ligands insert into the PDZ1 binding pocket by a beta-sheet augmentation process and are stabilized by largely similar networks of hydrogen bonds and hydrophobic contacts. In the PDZ1-beta(2)AR complex, the side chain of asparagine at position -4 in the beta(2)AR peptide forms two additional hydrogen bonds with Gly(30) of PDZ1, which contribute to the higher affinity of this interaction. Remarkably, both complexes are further stabilized by hydrophobic interactions involving the side chains of the penultimate amino acids of the peptide ligands, whereas the PDZ1 residues Asn(22) and Glu(43) undergo conformational changes to accommodate these side chains. These results provide structural insights into the mechanisms by which different side chains at the position -1 of peptide ligands interact with PDZ domains and contribute to the affinity of the PDZ-ligand interaction.     

Exploring Protein-Peptide Binding Specificity through Computational Peptide Screening

The binding of short disordered peptide stretches to globular protein domains is important for a wide range of cellular processes, including signal transduction, protein transport, and immune response. The often promiscuous nature of these interactions and the conformational flexibility of the peptide chain, sometimes even when bound, make the binding specificity of this type of protein interaction a challenge to understand. Here we develop and test a Monte Carlo-based procedure for calculating protein-peptide binding thermodynamics for many sequences in a single run. The method explores both peptide sequence and conformational space simultaneously by simulating a joint probability distribution which, in particular, makes searching through peptide sequence space computationally efficient. To test our method, we apply it to 3 different peptide-binding protein domains and test its ability to capture the experimentally determined specificity profiles. Insight into the molecular underpinnings of the observed specificities is obtained by analyzing the peptide conformational ensembles of a large number of binding-competent sequences. We also explore the possibility of using our method to discover new peptide-binding pockets on protein structures.     

Unusual binding interactions in PDZ domain crystal structures help explain binding mechanisms

PDZ domains most commonly bind the C‐terminus of their protein targets. Typically the C‐terminal four residues of the protein target are considered as the binding motif, particularly the C‐terminal residue (P0) and third‐last residue (P‐2) that form the major contacts with the PDZ domain's “binding groove”. We solved crystal structures of seven human PDZ domains, including five of the seven PDLIM family members. The structures of GRASP, PDLIM2, PDLIM5, and PDLIM7 show a binding mode with only the C‐terminal P0 residue bound in the binding groove. Importantly, in some cases, the P‐2 residue formed interactions outside of the binding groove, providing insight into the influence of residues remote from the binding groove on selectivity. In the GRASP structure, we observed both canonical and noncanonical binding in the two molecules present in the asymmetric unit making a direct comparison of these binding modes possible. In addition, structures of the PDZ domains from PDLIM1 and PDLIM4 also presented here allow comparison with canonical binding for the PDLIM PDZ domain family. Although influenced by crystal packing arrangements, the structures nevertheless show that changes in the positions of PDZ domain side‐chains and the αB helix allow noncanonical binding interactions. These interactions may be indicative of intermediate states between unbound and fully bound PDZ domain and target protein. The noncanonical “perpendicular” binding observed potentially represents the general form of a kinetic intermediate. Comparison with canonical binding suggests that the rearrangement during binding involves both the PDZ domain and its ligand.     

Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.     

Advantages of fine-grained side chain conformer libraries

We compare the modelling accuracy of two common rotamer libraries, the Dunbrack-Cohen and the 'Penultimate' rotamer libraries, with that of a novel library of discrete side chain conformations extracted from the Protein Data Bank. These side chain conformer libraries are extracted automatically from high-quality protein structures using stringent filters and maintain crystallographic bond lengths and angles. This contrasts with traditional rotamer libraries defined in terms of chi angles under the assumption of idealized covalent geometry. We demonstrate that side chain modelling onto native and near-native main chain conformations is significantly more successful with the conformer libraries than with the rotamer libraries when solely considering excluded-volume interactions. The rotamer libraries are inadequate to model side chains without atomic clashes on over 20% of targets if the backbone is held fixed in the native conformation. An algorithm is described for simultaneously modelling both main chain and side chain atoms during discrete ab initio sampling. The resulting models have equivalent root mean square deviations from the experimentally determined protein loops as models from backbone-only ensembles, indicating that all-atom modelling does not detract from the accuracy of conformational sampling.     

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.     

Ensemble models of proteins and protein domains based on distance distribution restraints

Conformational ensembles of intrinsically disordered peptide chains are not fully determined by experimental observations. Uncertainty due to lack of experimental restraints and due to intrinsic disorder can be distinguished if distance distributions restraints are available. Such restraints can be obtained from pulsed dipolar electron paramagnetic resonance (EPR) spectroscopy applied to pairs of spin labels. Here, we introduce a Monte Carlo approach for generating conformational ensembles that are consistent with a set of distance distribution restraints, backbone dihedral angle statistics in known protein structures, and optionally, secondary structure propensities or membrane immersion depths. The approach is tested with simulated restraints for a terminal and an internal loop and for a protein with 69 residues by using sets of sparse restraints for underlying well‐defined conformations and for published ensembles of a premolten globule‐like and a coil‐like intrinsically disordered protein. Proteins 2016; 84:544–560. © 2016 Wiley Periodicals, Inc.     

Structure, dynamics and binding characteristics of the second PDZ domain of PTP-BL

The PDZ domains of the protein tyrosine phosphatase PTP-BL mediate interactions by binding to specific amino acid sequences in target proteins. The solution structure of the second PDZ domain of PTP-BL, PDZ2, displays a compact fold with six β strands and two α-helices. A unique feature of this domain compared to the canonical PDZ fold is an extended flexible loop at the base of the binding pocket, termed L1, that folds back onto the protein backbone, a feature that is shared by both the murine and human orthologues. The structure of PDZ2 differs significantly from the orthologous human structure. A comparison of structural quality indicators clearly demonstrates that the PDZ2 ensemble is statistically more reasonable than that of the human orthologue. The analysis of ¹⁵N relaxation data for PDZ2 shows a normal pattern, with more rigid secondary structures and more flexible loop structures. Close to the binding pocket, Leu85 and Thr88 display greater mobility when compared to surrounding residues. Peptide binding studies demonstrated a lack of interaction between murine PDZ2 and the C terminus of the murine Fas/CD95 receptor, suggesting that the Fas/CD95 receptor is not an in vivo target for PDZ2. In addition, PDZ2 specifically binds the C termini of both human Fas/CD95 receptor and the RIL protein, despite RIL containing a non-canonical PDZ-interacting sequence of E-x-V. A model of PDZ2 with the RIL peptide reveals that the PDZ2 binding pocket is able to accommodate the bulkier side-chain of glutamic acid while maintaining crucial protein to peptide hydrogen bond interactions.     

Selective binding of synapse-associated protein 97 to GluR-A alpha-amino-5-hydroxy-3-methyl-4-isoxazole propionate receptor subunit is determined by a novel sequence motif

A family of four closely related PDZ domain-containing membrane-associated guanylate kinase homologues (MAGUKs) is involved in the regulation of the amount and functional state of ionotropic glutamate receptors in excitatory synapses. To understand the mechanisms that determine the specificity of these interactions, we examined the structural basis of the highly selective association between the ionotropic GluR subunit GluR-A and synapse-associated protein 97 (SAP97). The C terminus of GluR-A bound to the PDZ domains of SAP97, but not to those of three related MAGUKs, PSD-93, PSD-95, and SAP102. Experiments with single PDZ domains indicated that the strongest contribution was by the second PDZ domain. Unexpectedly, mutation analysis of the GluR-A C terminus revealed that a tripeptide sequence SSG at position -9 to -11 plays an essential role in this binding, in addition to a C-terminal type I PDZ binding motif (leucine at C terminus and threonine at the -2 position). Analysis of the in vitro MAGUK-binding properties of a GluR-D mutant with a one-residue deletion at the C terminus provides further support for the view that an SSG sequence located N-terminally from a type I PDZ binding motif can mediate selective binding to SAP97 and suggest the existence of a novel variation of the PDZ domain-peptide interaction.     

Sub‐angstrom modeling of complexes between flexible peptides and globular proteins

A wide range of regulatory processes in the cell are mediated by flexible peptides that fold upon binding to globular proteins. Computational efforts to model these interactions are hindered by the large number of rotatable bonds in flexible peptides relative to typical ligand molecules, and the fact that different peptides assume different backbone conformations within the same binding site. In this study, we present Rosetta FlexPepDock, a novel tool for refining coarse peptide–protein models that allows significant changes in both peptide backbone and side chains. We obtain high resolution models, often of sub‐angstrom backbone quality, over an extensive and general benchmark that is based on a large nonredundant dataset of 89 peptide–protein interactions. Importantly, side chains of known binding motifs are modeled particularly well, typically with atomic accuracy. In addition, our protocol has improved modeling quality for the important application of cross docking to PDZ domains. We anticipate that the ability to create high resolution models for a wide range of peptide–protein complexes will have significant impact on structure‐based functional characterization, controlled manipulation of peptide interactions, and on peptide‐based drug design. Proteins 2010. © 2010 Wiley‐Liss, Inc.     

Dead‐end elimination with perturbations (DEEPer): A provable protein design algorithm with continuous sidechain and backbone flexibility

Computational protein and drug design generally require accurate modeling of protein conformations. This modeling typically starts with an experimentally determined protein structure and considers possible conformational changes due to mutations or new ligands. The DEE/A* algorithm provably finds the global minimum‐energy conformation (GMEC) of a protein assuming that the backbone does not move and the sidechains take on conformations from a set of discrete, experimentally observed conformations called rotamers. DEE/A* can efficiently find the overall GMEC for exponentially many mutant sequences. Previous improvements to DEE/A* include modeling ensembles of sidechain conformations and either continuous sidechain or backbone flexibility. We present a new algorithm, DEEPer (D ead‐E nd E limination with Per turbations), that combines these advantages and can also handle much more extensive backbone flexibility and backbone ensembles. DEEPer provably finds the GMEC or, if desired by the user, all conformations and sequences within a specified energy window of the GMEC. It includes the new abilities to handle arbitrarily large backbone perturbations and to generate ensembles of backbone conformations. It also incorporates the shear, an experimentally observed local backbone motion never before used in design. Additionally, we derive a new method to accelerate DEE/A*‐based calculations, indirect pruning, that is particularly useful for DEEPer. In 67 benchmark tests on 64 proteins, DEEPer consistently identified lower‐energy conformations than previous methods did, indicating more accurate modeling. Additional tests demonstrated its ability to incorporate larger, experimentally observed backbone conformational changes and to model realistic conformational ensembles. These capabilities provide significant advantages for modeling protein mutations and protein–ligand interactions. Proteins 2013. © 2012 Wiley Periodicals, Inc.     

Detailed regulatory mechanism of the interaction between ZO-1 PDZ2 and connexin43 revealed by MD simulations

The gap junction protein connexin43 (Cx43) binds to the second PDZ domain of Zonula occludens-1 (ZO-1) through its C-terminal tail, mediating the regulation of gap junction plaque size and dynamics. Biochemical study demonstrated that the very C-terminal 12 residues of Cx43 are necessary and sufficient for ZO-1 PDZ2 binding and phosphorylation at residues Ser (-9) and Ser (-10) of the peptide can disrupt the association. However, only a crystal structure of ZO-1 PDZ2 in complex with a shorter 9 aa peptide of connexin43 was solved experimentally. Here, the interactions between ZO-1 PDZ2 and the short, long and phosphorylated Cx43 peptides were studied using molecular dynamics (MD) simulations and free energy calculation. The short peptide bound to PDZ2 exhibits large structural variations, while the extension of three upstream residues stabilizes the peptide conformation and enhanced the interaction. Phosphorylation at Ser(-9) significantly weakens the binding and results in conformational flexibility of the peptide. Glu210 of ZO-1 PDZ2 was found to be a key regulatory point in Cx43 binding and phosphorylation induced dissociation.     

A physical model for PDZ-domain/peptide interactions

The PDZ domain is an interaction motif that recognizes and binds the C-terminal peptides of target proteins. PDZ domains are ubiquitous in nature and help assemble multiprotein complexes that control cellular organization and signaling cascades. We present an optimized energy function to predict the binding free energy (ΔΔG) of PDZ domain/peptide interactions computationally. Geometry-optimized models of PDZ domain/peptide interfaces were built using Rosetta, and protein and peptide side chain and backbone degrees of freedom are minimized simultaneously. Using leave-one-out cross-validation, Rosetta’s energy function is adjusted to reproduce experimentally determined ΔΔG values with a correlation coefficient of 0.66 and a standard deviation of 0.79 kcal mol⁻¹. The energy function places an increased weight on hydrogen bonding interactions when compared to a previously developed method to analyze protein/protein interactions. Binding free enthalpies (ΔΔH) and entropies (ΔS) are predicted with reduced accuracies of R = 0.60 and R = 0.17, respectively. The computational method improves prediction of PDZ domain specificity from sequence and allows design of novel PDZ domain/peptide interactions.     

Solution structure of the PDZ2 domain from human phosphatase hPTP1E and its interactions with C-terminal peptides from the Fas receptor

The solution structure of the second PDZ domain (PDZ2) from human phosphatase hPTP1E has been determined using 2D and 3D heteronuclear NMR experiments. The binding of peptides derived from the C-terminus of the Fas receptor to PDZ2 was studied via changes in backbone peptide and protein resonances. The structure is based on a total of 1387 nonredundant experimental NMR restraints including 1261 interproton distance restraints, 45 backbone hydrogen bonds, and 81 torsion angle restraints. Analysis of 30 lowest-energy structures resulted in rmsd values of 0.41 +/- 0.09 A for backbone atoms (N, Calpha, C') and 1.08 +/- 0.10 A for all heavy atoms, excluding the disordered N- and C-termini. The hPTP1E PDZ2 structure is similar to known PDZ domain structures but contains two unique structural features. In the peptide binding domain, the first glycine of the GLGF motif is replaced by a serine. This serine appears to replace a bound water observed in PDZ crystal structures that hydrogen bonds to the bound peptide's C-terminus. The hPTP1E PDZ2 structure also contains an unusually large loop following strand beta2 and proximal to the peptide binding site. This well-ordered loop folds back against the PDZ domain and contains several residues that undergo large amide chemical shift changes upon peptide binding. Direct observation of peptide resonances demonstrates that as many as six Fas peptide residues interact with the PDZ2 domain.     

Conformational features responsible for the binding of cyclic analogues of enkephalin to opioid receptors. III. Probable binding conformations of mu-agonists with phenylalanine in position 3.

Theoretical conformational analysis was carried out for several tetrapeptide analogues of beta-casomorphin and dermorphin containing a Phe residue in position 3. Sets of low-energy backbone structures of the mu-selective peptides [N-Me-Phe3, D-Pro4]-morphiceptin and Tyr-D-Orn-Phe-Asp-NH2 were obtained. These sets of structures were compared for geometrical similarity between themselves and with the low-energy conformations found for the delta-selective peptide Tyr-D-Cys-Phe-D-Pen-OH and nonactive peptide Tyr-Orn-Phe-Asp-NH2. Two pairs of geometrically similar conformations of mu-selective peptides, sharing no similarity with the conformations of peptides showing low affinity to the mu-receptor, were selected as two alternative models of probable mu-receptor-bound backbone conformations. Both models share geometrical similarity with the low-energy structures of the linear mu-selective peptide Tyr-D-Ala-Phe-Phe-NH2. Putative binding conformations of Tyr1 and Phe3 side chains are also discussed.