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.     

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.     

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 comparative study of the single crystal X-ray determination and molecular modelling of the binding of oligomycin to ATP Synthase

Recently published X-ray structures of three common forms, A, B and C, of oligomycin, including absolute configurations, are investigated to examine their binding to ATP Synthase. The X-ray studies reveal regions with differences in three-dimensional structure and hydrogen bonding propensity between the oligomycins, which may be associated with their potential to bind to sites on ATP Synthase. Computational docking studies carried out using MOE with the X-ray structures and an homology model of the F_O domain of ATP Synthase from Escherichia coli, are used to derive an induced fit pocket. Docking of all oligomycins to this pocket indicate that the B and C forms bind more tightly than the A form. Consideration of the single crystal X-ray data alone indicate the B form may be the best inhibitor and that O(24) is the most important ligating group for binding, this is supported by the docking data. The latter reveals Asn214 and other key proton translocating residues to be the main residues contacted by the inhibitor. These data allow the binding modes of different forms of oligomycin to be deduced from X-ray single crystal data supported by molecular modelling and computational docking studies.     

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.     

Computational simulation of ligand docking to L-type pyruvate kinase subunit

Computational blind docking approach was used for mapping of possible binding sites in L-type pyruvate kinase subunit for peptides, RRASVA and the phosphorylated derivative RRAS(P_i)VA, which model the phosphorylatable N-terminal regulatory domain of the enzyme. In parallel, the same docking analysis was done for both substrates of this enzyme, phosphoenolpyruvate (PEP) and adenosine diphosphate (ADP), and for docking of fructose 1,6-bisphosphate (FBP), which is the allosteric activator of the enzyme. The binding properties of the entire surface of the protein were scanned and several possible binding sites were identified in domains A and C of the protein, while domain B revealed no docking sites for peptides or for substrates or the allosteric regulator. It was found that the docking sites of different ligands were partially overlapping, pointing to the possibility that some regulatory effects, observed in the case of L-type pyruvate kinase, may be caused by the competition of different ligands for the same binding sites.     

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.     

DOX: A new computational protocol for accurate prediction of the protein–ligand binding structures

Molecular docking techniques have now been widely used to predict the protein–ligand binding modes, especially when the structures of crystal complexes are not available. Most docking algorithms are able to effectively generate and rank a large number of probable binding poses. However, it is hard for them to accurately evaluate these poses and identify the most accurate binding structure. In this study, we first examined the performance of some docking programs, based on a testing set made of 15 crystal complexes with drug statins for the human 3‐hydroxy‐3‐methylglutaryl coenzyme A reductase (HMGR). We found that most of the top ranking HMGR–statin binding poses, predicted by the docking programs, were energetically unstable as revealed by the high theoretical‐level calculations, which were usually accompanied by the large deviations from the geometric parameters of the corresponding crystal binding structures. Subsequently, we proposed a new computational protocol, DOX, based on the joint use of molecular Docking, ONIOM, and eXtended ONIOM (XO) methods to predict the accurate binding structures for the protein–ligand complexes of interest. Our testing results demonstrate that the DOX protocol can efficiently predict accurate geometries for all 15 HMGR‐statin crystal complexes without exception. This study suggests a promising computational route, as an effective alternative to the experimental one, toward predicting the accurate binding structures, which is the prerequisite for all the deep understandings of the properties, functions, and mechanisms of the protein–ligand complexes. © 2015 Wiley Periodicals, Inc.     

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.     

Protein-ligand docking against non-native protein conformers

In the validation of protein-ligand docking protocols, performance is mostly measured against native protein conformers, i.e. each ligand is docked into the protein conformation from the structure that contained that ligand. In real-life applications, however, ligands are docked against non-native conformations of the protein, i.e. the apo structure or a structure of a different protein-ligand complex. Here, we have constructed an extensive test set for assessing docking performance against non-native protein conformations. This new test set is built on the Astex Diverse Set (which we recently constructed for assessing native docking performance) and contains 1112 non-native structures for 65 drug targets. Using the protein-ligand docking program GOLD, the Astex Diverse Set and the new Astex Non-native Set, we established that, whereas docking performance (top-ranked solution within 2 A rmsd of the experimental binding mode) is approximately 80% for native docking, this drops to 61% for non-native docking. A similar drop-off is observed for sampling performance (any solution within 2 A): 91% for native docking vs 72% for non-native docking. No significant differences were observed between docking performance against apo and nonapo structures. We found that, whereas small variations in protein conformation are generally tolerated by our rigid docking protocol, larger protein movements result in a catastrophic drop-off in performance. Some docking performance and nearly all sampling performance can be recovered by considering dockings produced against a small number of non-native structures simultaneously. Docking against non-native structures of complexes containing ligands that are similar to the docked ligand also significantly improves both docking performance and sampling performance.     

New approach to molecular docking and its application to virtual screening of chemical databases

This paper describes the validation of a molecular docking method and its application to virtual database screening. The code flexibly docks ligand molecules into rigid receptor structures using a tabu search methodology driven by an empirically derived function for estimating the binding affinity of a protein-ligand complex. The docking method has been tested on 70 ligand-receptor complexes for which the experimental binding affinity and binding geometry are known. The lowest energy geometry produced by the docking protocol is within 2.0 A root mean square of the experimental binding mode for 79% of the complexes. The method has been applied to the problem of virtual database screening to identify known ligands for thrombin, factor Xa, and the estrogen receptor. A database of 10,000 randomly chosen "druglike" molecules has been docked into the three receptor structures. In each case known receptor ligands were included in the study. The results showed good separation between the predicted binding affinities of the known ligand set and the database subset.     

Deciphering common failures in molecular docking of ligand-protein complexes

Common failures in predicting crystal structures of ligand-protein complexes are investigated for three ligand-protein systems by a combined thermodynamic and kinetic analysis of the binding energy landscapes. Misdocked predictions in ligand-protein docking are classified as `soft' and `hard' failures. While a soft failure arises when the search algorithm is unable to find the global energy minimum corresponding to the crystal structure, a hard failure results from a flaw of the energy function to qualify the crystal structure as the predicted lowest energy conformation in docking simulations. We find that neither the determination of a single structure with the lowest energy nor finding the most common binding mode is sufficient to predict crystal structures of the complexes, which belong to the category of hard failures. In a proposed hierarchical approach, structural similarity clustering of the conformations, generated from equilibrium simulations with the simplified energy function, is followed by energy refinement with the AMBER force field. This protocol, that involves a hierarchy of energy functions, resolves some common failures in ligand-protein docking and detects crystallographic binding modes that were not found during docking simulations.     

Predicting protein-protein interactions using graph invariants and a neural network

The PDZ domain of proteins mediates a protein-protein interaction by recognizing the hydrophobic C-terminal tail of the target protein. One of the challenges put forth by the DREAM (Discussions on Reverse Engineering Assessment and Methods) 2009 Challenge consists of predicting a position weight matrix (PWM) that describes the specificity profile of five PDZ domains to their target peptides. We consider the primary structures of each of the five PDZ domains as a numerical sequence derived from graph-theoretic models of each of the individual amino acids in the protein sequence. Using available PDZ domain databases to obtain known targets, the graph-theoretic based numerical sequences are then used to train a neural network to recognize their targets. Given the challenge sequences, the target probabilities are computed and a corresponding position weight matrix is derived. In this work we present our method. The results of our method placed second in the DREAM 2009 challenge.     

Biochemical characterization of peptidyl carrier protein (PCP), the thiolation domain of multifunctional peptide synthetases

Background: A structurally diverse group of bioactive peptides is synthesized by peptide synthetases which act as templates for a growing peptide chain, attached to the enzyme via a thloester bond. The protein templates are composed of distinctive substrate-activating modules, whose order dictates the primary structure of the corresponding peptide product. Each module contains defined domains that catalyze adenylation, thioester and peptide bond formation, as well as substrate modifications. To show that a putative thiolation domain (PCP) is involved in covalent binding and transfer of amino aryl residues during non-ribosomal peptide synthesis, we have cloned and biochemically characterized that region of tyrocidine synthetase 1, TycA.Results: The 327-bp gene fragment encoding PCP was cloned using its homology to the genes for the acyl carrier proteins of fatty acid and polyketide biosynthesis. The protein was expressed as a His₆, fusion protein, and purified in a single step by affinity chromatography. Incorporation of β-[³H]alanine, a precursor of coenzyme A, demonstrated the modification of PCP with the cofactor 4′-phosphopantetheine. When an adenylation domain is present to supply the amino adenylate moiety, PCP can be acylated in vitro.Conclusions: PCP can bind covalently to the cofactor phosphopantetheine and can subsequently be acylated, strongly supporting the multiple carrier model of non-ribosomal peptide synthesis. The adenylation and thiolation domains can each act as independent multifunctional enzymes, further confirming the modular structure of peptide synthees, and can also perform sequential steps in trans, as do multienzyme complexes.     

Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism

CYP2D6 is an important enzyme that is involved in first pass metabolism and is responsible for metabolizing ~25% of currently marketed drugs. A homology model of CYP2D6 was built using X-ray structures of ligand-bound CYP2C5 complexes as templates. This homology model was used in docking studies to rationalize and predict the site of metabolism of known CYP2D6 substrates. While the homology model was generally found to be in good agreement with the recently solved apo (ligand-free) X-ray structure of CYP2D6, significant differences between the structures were observed in the B′ and F–G helical region. These structural differences are similar to those observed between ligand-free and ligand-bound structures of other CYPs and suggest that these conformational changes result from induced-fit adaptations upon ligand binding. By docking to the homology model using Glide, it was possible to identify the correct site of metabolism for a set of 16 CYP2D6 substrates 85% of the time when the 5 top scoring poses were examined. On the other hand, docking to the apo CYP2D6 X-ray structure led to a loss in accuracy in predicting the sites of metabolism for many of the CYP2D6 substrates considered in this study. These results demonstrate the importance of describing substrate-induced conformational changes that occur upon binding. The best results were obtained using Glide SP with van der Waals scaling set to 0.8 for both the receptor and ligand atoms. A discussion of putative binding modes that explain the distribution of metabolic sites for substrates, as well as a relationship between the number of metabolic sites and substrate size, are also presented. In addition, analysis of these binding modes enabled us to rationalize the typical hydroxylation and O-demethylation reactions catalyzed by CYP2D6 as well as the less common N-dealkylation.     

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.     

Gaussian mapping of chemical fragments in ligand binding sites

We present a new approach to automatically define a quasi-optimal minimal set of pharmacophoric points mapping the interaction properties of a user-defined ligand binding site. The method is based on a fitting algorithm where a grid of sampled interaction energies of the target protein with small chemical fragments in the binding site is approximated by a linear expansion of Gaussian functions. A heuristic approximation selects from this expansion the smallest possible set of Gaussians required to describe the interaction properties of the binding site within a prespecified accuracy. We have evaluated the performance of the approach by comparing the computed Gaussians with the positions of aromatic sites found in experimental protein-ligand complexes. For a set of 53 complexes, good correspondence is found in general. At a 95% significance level, approximately 65% of the predicted interaction points have an aromatic binding site within 1.5 A. We then studied the utility of these points in docking using the program DOCK. Short docking times, with an average of approximately 0.18 s per conformer, are obtained, while retaining, both for rigid and flexible docking, the ability to sample native-like binding modes for the ligand. An average 4-5-fold speed-up in docking times and a similar success rate is estimated with respect to the standard DOCK protocol.     

Characterization of PDZ domain–peptide interactions using an integrated protocol of QM/MM,PB/SA,and CFEA analyses

Protein–protein interactions, particularly weak and transient ones, are often mediated by peptide recognition domains. Characterizing the interaction interface of domain–peptide complexes and analyzing binding specificity for modular domains are critical for deciphering protein–protein interaction networks. In this article, we report the successful use of an integrated computational protocol to dissect the energetic profile and structural basis of peptide binding to third PDZ domain (PDZ3) from the PSD-95 protein. This protocol employs rigorous quantum mechanics/molecular mechanics (QM/MM), semi-empirical Poisson–Boltzmann/surface area (PB/SA), and empirical conformational free energy analysis (CFEA) to quantitatively describe and decompose systematic energy changes arising from, respectively, noncovalent interaction, desolvation effect, and conformational entropy loss associated with the formation of 30 affinity-known PDZ3–peptide complexes. We show that the QM/MM-, PB/SA-, and CFEA-derived energy components can work together fairly well in reproducing experimentally measured affinity after a linearly weighting treatment, albeit they are not compatible with each other directly. We also demonstrate that: (1) noncovalent interaction and desolvation effect donate, respectively, stability and specificity to complex architecture, while entropy loss contributes modestly to binding; (2) P₀ and P₋₂ of peptide ligand are the most important positions for determining both the stability and specificity of the PDZ3–peptide complex, P₋₁ and P₋₃ can confer substantial stability (but not specificity) for the complex, and N-terminal P₋₄ and P₋₅ have only a very limited effect on binding.     

Protein-protein Interaction Between Domains of PDZ and BAR from PICK1

IntroductionProtein kinase Cα(PKCα) widely existed in thecells of human being and animals is one kind of serine/threonine kinase that can be rapidly activated by a vari-ety of factors such as Ca2 , cAMP and hormone, re-sulting in the signal transmissio…