Reaching for high-hanging fruit in drug discovery at protein-protein interfaces

A highly potent and cellularly active beta-peptidic inhibitor of the p53/hDM2 interaction

New and improved: The incorporation of a 6-chlorotryptophan (6-Cl-Trp) into a beta-peptide (M)-3(14) helix leads to a high-affinity hDM2 inhibitor, as demonstrated by fluorescence fluctuation analysis at single molecule resolution. When conjugated to penetratin, the newly derived hDM2 binder specifically inhibits tumour cell growth in vitro.

The road less traveled: modulating signal transduction enzymes by inhibiting their protein-protein interactions

The biological functions of intracellular signaling enzymes typically depend on multiple protein-protein interactions (PPI) with substrates, scaffolding proteins, and other cytoplasmic molecules. Blocking these interactions provides an alternative means to modulate signaling activity without fully ablating the catalytic activity of the target. Several recent reports describe small-molecule antagonists that target PPI sites on signaling enzymes. These findings suggest that such sites may often be druggable. However, the hypothesis that targeting such sites might confer on the resulting inhibitors improved properties of efficacy and/or tolerability, while appealing, remains largely untested.

Peptide inhibitors of hepatitis C virus core oligomerization and virus production

Hepatitis C virus (HCV) nucleocapsid assembly requires dimerization of the core protein, an essential step in the formation of the virus particle. We developed a novel quantitative assay for monitoring this protein–protein interaction, with the goal of identifying inhibitors of core dimerization that might block HCV production in infected Huh-7.5 hepatoma cells. Two core-derived, 18-residue peptides were found that inhibited the dimerization of a fragment of core comprising residues 1–106 (core106) by 68 and 63 %, respectively. A third, related 15-residue peptide displayed 50 % inhibition, with an IC50 of 21.9 μM. This peptide was shown, by fluorescence polarization, to bind directly to core106 with a K d of 1.9 μM and was displaced by the unlabelled peptide with an IC50 of 18.7 μM. When measured by surface plasmon resonance, the same peptide bound core169 with a K d of 7.2 μM. When added to HCV-infected cells, each of the three peptides blocked release, but not replication, of infectious virus. When measured by real-time RT-PCR, the RNA levels were reduced by 7-fold. The 15-residue peptide had no effect on HIV propagation. Such inhibitors may constitute useful tools to investigate the role of core dimerization in the virus cycle.

Increasingly accurate dynamic molecular models of G-protein coupled receptor oligomers: Panacea or Pandora's box for novel drug discovery?

For years, conventional drug design at G-protein coupled receptors (GPCRs) has mainly focused on the inhibition of a single receptor at a usually well-defined ligand-binding site. The recent discovery of more and more physiologically relevant GPCR dimers/oligomers suggests that selectively targeting these complexes or designing small molecules that inhibit receptor-receptor interactions might provide new opportunities for novel drug discovery. To uncover the fundamental mechanisms and dynamics governing GPCR dimerization/oligomerization, it is crucial to understand the dynamic process of receptor-receptor association, and to identify regions that are suitable for selective drug binding. This minireview highlights current progress in the development of increasingly accurate dynamic molecular models of GPCR oligomers based on structural, biochemical, and biophysical information that has recently appeared in the literature. In view of this new information, there has never been a more exciting time for computational research into GPCRs than at present. Information-driven modern molecular models of GPCR complexes are expected to efficiently guide the rational design of GPCR oligomer-specific drugs, possibly allowing researchers to reach for the high-hanging fruits in GPCR drug discovery, i.e. more potent and selective drugs for efficient therapeutic interventions.

Trapping moving targets with small molecules

Structure-based drug design traditionally uses static protein models as inspirations for focusing on "active" site targets. Allosteric regulation of biological macromolecules, however, is affected by both conformational and dynamic properties of the protein or protein complex and can potentially lead to more avenues for therapeutic development. We discuss the advantages of searching for molecules that conformationally trap a macromolecule in its inactive state. Although multiple methodologies exist to probe protein dynamics and ligand binding, our current discussion highlights the use of nuclear magnetic resonance spectroscopy in the drug discovery and design process.

Rational redesign of neutral endopeptidase binding to merlin and moesin proteins

Neutral endopeptidase (NEP) is a 90- to 110-kDa cell-surface peptidase that is normally expressed by numerous tissues but whose expression is lost or reduced in a variety of malignancies. The anti-tumorigenic function of NEP is mediated not only by its catalytic activity but also through direct protein-protein interactions of its cytosolic region with several binding partners, including Lyn kinase, PTEN, and ezrin/radixin/moesin (ERM) proteins. We have previously shown that mutation of the K(19)K(20)K(21) basic cluster in NEPs' cytosolic region to residues QNI disrupts binding to the ERM proteins. Here we show that the ERM-related protein merlin (NF2) does not bind NEP or its cytosolic region. Using experimental data, threading, and sequence analysis, we predicted the involvement of moesin residues E(159)Q(160) in binding to the NEP cytosolic domain. Mutation of these residues to NL (to mimic the corresponding N(159)L(160) residues in the nonbinder merlin) disrupted moesin binding to NEP. Mutation of residues N(159)L(160)Y(161)K(162)M(163) in merlin to the corresponding moesin residues resulted in NEP binding to merlin. This engineered NEP peptide-merlin interaction was diminished by the QNI mutation in NEP, supporting the role of the NEP basic cluster in binding. We thus identified the region of interaction between NEP and moesin, and engineered merlin into a NEP-binding protein. These data form the basis for further exploration of the details of NEP-ERM binding and function.

Natural product inhibitors of protein-protein interactions mediated by Src-family SH2 domains

In this Letter, we report the natural products salvianolic acid A, salvianolic acid B, and caftaric acid as inhibitors of the protein-protein interactions mediated by the SH2 domains of the Src-family kinases Src and Lck, two established disease targets. Moreover, we propose a binding mode for the inhibitors based on molecular modeling, which will facilitate chemical optimization efforts of these important lead structures for drug discovery.

Identification of Shc Src homology 2 domain-binding peptoid-peptide hybrids

A fluorescence anisotropy (FA) competition-based Shc Src homology 2 (SH2) domain-binding was established using the high affinity fluorescein isothiocyanate (FITC) containing peptide, FITC-NH-(CH2)4-CO-pY-Q-G-L-S-amide (8; Kd = 0.35 microM). Examination of a series of open-chain bis-alkenylamide containing peptides, prepared as ring-closing metathesis precursors, showed that the highest affinities were obtained by replacement of the original Gly residue with N alpha-substituted Gly (NSG) "peptoid" residues. This provided peptoid-peptide hybrids of the form "Ac-pY-Q-[NSG]-L-amide." Depending on the NSG substituent, certain of these hybrids exhibited up to 40-fold higher Shc SH2 domain-binding affinity than the parent Gly-containing peptide (IC50 = 248 microM) (for example, for N-homoallyl analogue 50, IC50 = 6 microM). To our knowledge, this work represents the first successful example of the application of peptoid-peptide hybrids in the design of SH2 domain-binding antagonists. These results could provide a foundation for further structural optimization of Shc SH2 domain-binding peptide mimetics.

Assessment of helical interfaces in protein-protein interactions

Herein we identify and analyze helical protein interfaces as potential targets for synthetic modulators of protein-protein interactions.

Targeting protein–protein interactions for therapeutic intervention: a challenge for the future

Background: Over the last two decades, an increasing research effort in academia and industry has focused on the modulation (both inhibition and stabilization) of protein–protein interactions (PPIs) in order to develop novel therapeutic approaches and target-selective agents in drug discovery. Discussion: The diversity and complexity of highly dynamic systems such as PPIs present many challenges for the identification of drug-like molecules with the ability to modulate the PPI with the necessary selectivity and potency. In this review, a number of these strategies will be presented along with a critical overview of the challenges and potential solutions relating to the exploitation of PPIs as molecular targets. Conclusions: Both traditional drug discovery approaches and some more recently developed innovative strategies have already provided valuable tools for the discovery of PPI modulators, and a number of successful examples have highlighted the potential of targeting PPIs for therapeutic intervention, especially in the oncology area.

Structure-based design of molecular cancer therapeutics

Structure-based approaches now impact across the whole continuum of drug discovery, from new target selection through the identification of hits to the optimization of lead compounds. Optimal application of structure-based design involves close integration with other discovery technologies, including fragment-based and virtual screening. Here, we illustrate the use of structural information and of structure-based drug design approaches in the discovery of small-molecule inhibitors for cancer drug targets and provide an outlook on the exploitation of structural information in future cancer drug discovery. Examples include high profile protein kinase targets and structurally related PI3 kinases, histone deacetylases, poly(ADP-ribose)polymerase and the molecular chaperone HSP90. Structure-based design approaches have also been successfully applied to the protein-protein interaction targets p53-MDM2 and the Bcl-2 family.

Principles of cancer therapy: oncogene and non-oncogene addiction

Cancer is a complex collection of distinct genetic diseases united by common hallmarks. Here, we expand upon the classic hallmarks to include the stress phenotypes of tumorigenesis. We describe a conceptual framework of how oncogene and non-oncogene addictions contribute to these hallmarks and how they can be exploited through stress sensitization and stress overload to selectively kill cancer cells. In particular, we present evidence for a large class of non-oncogenes that are essential for cancer cell survival and present attractive drug targets. Finally, we discuss the path ahead to therapeutic discovery and provide theoretical considerations for combining orthogonal cancer therapies.

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.

LEDGF/p75 and transportin-SR2 are cellular cofactors of HIV integrase and novel targets for antiviral therapy

The HIV replication cycle is an elaborate interplay between the viral machinery and cellular proteins. In this review we propose that protein–protein interactions between cellular proteins and HIV integrase are new targets for future antiviral therapy. We focus on the early steps of HIV replication, namely viral entry, uncoating, reverse transcription, trafficking, nuclear import and integration, and the host cell proteins involved herein. We then discuss the feasibility of developing small-molecule protein–protein interaction inhibitors as antiviral agents. Next, we review the HIV integrase cofactors described in the literature highlighting two validated cofactors, lens epithelium-derived growth factor/p75 and transportin-SR2, which are discussed in detail. Finally, a speculative viewpoint is given on small-molecule protein–protein interaction inhibitors as future HIV inhibitors.

Fast predictions of thermodynamics and kinetics of protein-protein recognition from structures: from molecular design to systems biology

The increasing call for an overall picture of the interactions between the components of a biological system that give rise to the observed function is often summarized by the expression systems biology. Both the interpretative and predictive capabilities of holistic models of biochemical systems, however, depend to a large extent on the level of physico-chemical knowledge of the individual molecular interactions making up the network. This review is focused on the structure-based quantitative characterization of protein-protein interactions, ubiquitous in any biochemical pathway. Recently developed, fast and effective computational methods are reviewed, which allow the assessment of kinetic and thermodynamic features of the association-dissociation processes of protein complexes, both in water soluble and membrane environments. The performance and the accuracy of fast and semi-empirical structure-based methods have reached comparable levels with respect to the classical and more elegant molecular simulations. Nevertheless, the broad accessibility and lower computational cost provide the former methods with the advantageous possibility to perform systems-level analyses including extensive in silico mutagenesis screenings and large-scale structural predictions of multiprotein complexes.

Exploring the molecular design of protein interaction sites with molecular dynamics simulations and free energy calculations

The significant work that has been invested toward understanding protein-protein interaction has not translated into significant advances in structure-based predictions. In particular redesigning protein surfaces to bind to unrelated receptors remains a challenge, partly due to receptor flexibility, which is often neglected in these efforts. In this work, we computationally graft the binding epitope of various small proteins obtained from the RCSB database to bind to barnase, lysozyme, and trypsin using a previously derived and validated algorithm. In an effort to probe the protein complexes in a realistic environment, all native and designer complexes were subjected to a total of nearly 400 ns of explicit-solvent molecular dynamics (MD) simulation. The MD data led to an unexpected observation: some of the designer complexes were highly unstable and decomposed during the trajectories. In contrast, the native and a number of designer complexes remained consistently stable. The unstable conformers provided us with a unique opportunity to define the structural and energetic factors that lead to unproductive protein-protein complexes. To that end we used free energy calculations following the MM-PBSA approach to determine the role of nonpolar effects, electrostatics and entropy in binding. Remarkably, we found that a majority of unstable complexes exhibited more favorable electrostatics than native or stable designer complexes, suggesting that favorable electrostatic interactions are not prerequisite for complex formation between proteins. However, nonpolar effects remained consistently more favorable in native and stable designer complexes reinforcing the importance of hydrophobic effects in protein-protein binding. While entropy systematically opposed binding in all cases, there was no observed trend in the entropy difference between native and designer complexes. A series of alanine scanning mutations of hot-spot residues at the interface of native and designer complexes showed less than optimal contacts of hot-spot residues with their surroundings in the unstable conformers, resulting in more favorable entropy for these complexes. Finally, disorder predictions revealed that secondary structures at the interface of unstable complexes exhibited greater disorder than the stable complexes.

alpha-Helix mimetics as inhibitors of protein-protein interactions

The inhibition of protein-protein interactions using small molecules is a viable approach for the treatment of a range of pathological conditions that result from a malfunctioning of these interactions. Our strategy for the design of such agents involves the mimicry of side-chain residues on one face of the alpha-helix; these residues frequently play a key role in mediating protein-protein interactions. The first-generation terphenyl scaffold, with a 3,2',2''-substitution pattern, is able to successfully mimic key helix residues and disrupt therapeutically relevant interactions, including the Bcl-X(L)-Bak and the p53-hDM2 (human double minute 2) interactions that are implicated in cancer. The second- and third-generation scaffolds have resulted in greater synthetic accessibility and more drug-like character in these molecules.

Compstatin: a complement inhibitor on its way to clinical application

Therapeutic modulation of the human complement system is considered a promising approach for treating a number of pathological conditions. Owing to its central position in the cascade, component C3 is a particularly attractive target for complement-specific drugs. Compstatin, a cyclic tridecapeptide, which was originally discovered from phage-display libraries, is a highly potent and selective C3 inhibitor that demonstrated clinical potential in a series of experimental models. A combination of chemical, biophysical, and computational approaches allowed a remarkable optimization of its binding affinity towards C3 and its inhibitory potency. With the recent announcement of clinical trials with a compstatin analog for the treatment of age-related macular degeneration, another important milestone has been reached on its way to a drug. Furthermore, the release of a co-crystal structure of compstatin with C3c allows a detailed insight into the binding mode and paves the way to the rational design of peptides and mimetics with improved activity. Considering the new incentives and the promising pre-clinical results, compstatin seems to be well equipped for the challenges on its way to a clinical therapeutic.

Identification of direct residue contacts in protein-protein interaction by message passing

Understanding the molecular determinants of specificity in protein-protein interaction is an outstanding challenge of postgenome biology. The availability of large protein databases generated from sequences of hundreds of bacterial genomes enables various statistical approaches to this problem. In this context covariance-based methods have been used to identify correlation between amino acid positions in interacting proteins. However, these methods have an important shortcoming, in that they cannot distinguish between directly and indirectly correlated residues. We developed a method that combines covariance analysis with global inference analysis, adopted from use in statistical physics. Applied to a set of >2,500 representatives of the bacterial two-component signal transduction system, the combination of covariance with global inference successfully and robustly identified residue pairs that are proximal in space without resorting to ad hoc tuning parameters, both for heterointeractions between sensor kinase (SK) and response regulator (RR) proteins and for homointeractions between RR proteins. The spectacular success of this approach illustrates the effectiveness of the global inference approach in identifying direct interaction based on sequence information alone. We expect this method to be applicable soon to interaction surfaces between proteins present in only 1 copy per genome as the number of sequenced genomes continues to expand. Use of this method could significantly increase the potential targets for therapeutic intervention, shed light on the mechanism of protein-protein interaction, and establish the foundation for the accurate prediction of interacting protein partners.

Toward a molecular understanding of the interaction of dual specificity phosphatases with substrates: insights from structure-based modeling and high throughput screening

Dual-specificity phosphatases (DSPs) are important, but poorly understood, cell signaling enzymes that remove phosphate groups from tyrosine and serine/threonine residues on their substrate. Deregulation of DSPs has been implicated in cancer, obesity, diabetes, inflammation, and Alzheimer's disease. Due to their biological and biomedical significance, DSPs have increasingly become the subject of drug discovery high-throughput screening (HTS) and focused compound library development efforts. Progress in identifying selective and potent DSP inhibitors has, however, been restricted by the lack of sufficient structural data on inhibitor-bound DSPs. The shallow, almost flat, substrate binding sites in DSPs have been a major factor in hampering the rational design and the experimental development of active site inhibitors. Recent experimental and virtual HTS studies, as well as advances in molecular modeling, provide new insights into the potential mechanisms for substrate recognition and binding by this important class of enzymes. We present herein an overview of the progress, along with a brief description of applications to two types of DSPs: Cdc25 and MAP kinase phosphatase (MKP) family members. In particular, we focus on combined computational and experimental efforts for designing Cdc25B and MKP-1 inhibitors and understanding their mechanisms of interactions with their target proteins. These studies emphasize the utility of developing computational models and methods that meet the two major challenges currently faced in structure-based in silico design of lead compounds: the conformational flexibility of the target protein and the entropic contribution to the selection and stabilization of particular bound conformers.

Predicting druggable binding sites at the protein-protein interface

Protein-protein interfaces are highly attractive targets for drug discovery because they are involved in a large number of disease pathways where therapeutic intervention would bring widespread benefit. Recent successes have challenged the widely held belief that these targets are 'undruggable'. The pocket finding algorithms described here show marked differences between the binding pockets that define protein-protein interactions (PPIs) and those that define protein-ligand interactions (PLIs) of currently marketed drugs. In the case of PPIs, drug discovery methods that simultaneously target several small pockets at the protein-protein interface are likely to increase the chances of success in this new and important field of therapeutics.

Targeting and tinkering with interaction networks

Biological interaction networks have been in the scientific limelight for nearly a decade. Increasingly, the concept of network biology and its various applications are becoming more commonplace in the community. Recent years have seen networks move from pretty pictures with limited application to solid concepts that are increasingly used to understand the fundamentals of biology. They are no longer merely results of postgenome analysis projects, but are now the starting point of many of the most exciting new scientific developments. We discuss here recent progress in identifying and understanding interaction networks, new tools that use them in predictive ways in exciting areas of biology, and how they have become the focus of many efforts to study, design and tinker with biological systems, with applications in biomedicine, bioengineering, ecology and beyond.

Design and Applications of Bifunctional Small Molecules: Why Two Heads Are Better Than One

Induction of protein--protein interactions is a daunting challenge, but recent studies show promise for small molecules that specifically bring two or more protein molecules together for enhanced or novel biological effect. The first such bifunctional molecules were the rapamycin- and FK506-based "chemical inducers of dimerization", but the field has since expanded with new molecules and new applications in chemical genetics and cell biology. Examples include coumermycin-mediated gyrase B dimerization, proteolysis targeting chimeric molecules (PROTACs), drug hybrids, and strategies for exploiting multivalency in toxin binding and antibody recruitment. This Review discusses these and other advances in the design and use of bifunctional small molecules and potential strategies for future systems.

Design of peptide inhibitors for the importin alpha/beta nuclear import pathway by activity-based profiling

Despite the current availability of selective inhibitors for the classical nuclear export pathway, no inhibitor for the classical nuclear import pathway has been developed. Here we describe the development of specific inhibitors for the importin alpha/beta pathway using a novel method of peptide inhibitor design. An activity-based profile was created via systematic mutational analysis of a peptide template of a nuclear localization signal. An additivity-based design using the activity-based profile generated two peptides with affinities for importin alpha that were approximately 5 million times higher than that of the starting template sequence. The high affinity of these peptides resulted in specific inhibition of the importin alpha/beta pathway. These peptide inhibitors provide a useful tool for studying nuclear import events. Moreover, our inhibitor design method should enable the development of potent inhibitors from a peptide seed.

Regulated expression of an essential allosteric activator of polyamine biosynthesis in African trypanosomes

Trypanosoma brucei is the causative agent of African sleeping sickness. The polyamine biosynthetic pathway has the distinction of being the target of the only clinically proven anti-trypanosomal drug with a known mechanism of action. Polyamines are essential for cell growth, and their metabolism is extensively regulated. However, trypanosomatids appear to lack the regulatory control mechanisms described in other eukaryotic cells. In T. brucei, S-adenosylmethionine decarboxylase (AdoMetDC) and ornithine decarboxylase (ODC) are required for the synthesis of polyamines and also for the unique redox-cofactor trypanothione. Further, trypanosomatid AdoMetDC is activated by heterodimer formation with a catalytically dead homolog termed prozyme, found only in these species. To study polyamine regulation in T. brucei, we generated inducible AdoMetDC RNAi and prozyme conditional knockouts in the mammalian blood form stage. Depletion of either protein led to a reduction in spermidine and trypanothione and to parasite death, demonstrating that prozyme activation of AdoMetDC is essential. Under typical growth conditions, prozyme concentration is limiting in comparison to AdoMetDC. However, both prozyme and ODC protein levels were significantly increased relative to stable transcript levels by knockdown of AdoMetDC or its chemical inhibition. Changes in protein stability do not appear to account for the increased steady-state protein levels, as both enzymes are stable in the presence of cycloheximide. These observations suggest that prozyme and ODC are translationally regulated in response to perturbations in the pathway. In conclusion, we describe the first evidence for regulation of polyamine biosynthesis in T. brucei and we demonstrate that the unique regulatory subunit of AdoMetDC is a key component of this regulation. The data support ODC and AdoMetDC as the key control points in the pathway and the likely rate-limiting steps in polyamine biosynthesis.

Design of multi-binding-site inhibitors, ligand efficiency, and consensus screening of avian influenza H5N1 wild-type neuraminidase and of the oseltamivir-resistant H274Y variant

The binding sites of wild-type avian influenza A H5N1 neuraminidase, as well as those of the Tamiflu (oseltamivir)-resistant H274Y variant, were explored computationally to design inhibitors that target simultaneously several adjacent binding sites of the open conformation of the virus protein. The compounds with the best computed free energies of binding, in agreement by two docking methods, consensus scoring, and ligand efficiency values, suggest that mimicking a polysaccharide, beta-lactam, and other structures, including known drugs, could be routes for multibinding site inhibitor design. This new virtual screening method based on consensus scoring and ligand efficiency indices is introduced, which allows the combination of pharmacodynamic and pharmacokinetic properties into unique measures.

Combinatorial optimization of a CD4-mimetic miniprotein and cocrystal structures with HIV-1 gp120 envelope glycoprotein

Miniproteins provide a bridge between proteins and small molecules. Here we adapt methods from combinatorial chemistry to optimize CD4M33, a synthetic miniprotein into which we had previously transplanted the HIV-1 gp120 binding surface of the CD4 receptor. Iterative deconvolution of generated libraries produced CD4M47, a derivative of CD4M33 that had been optimized at four positions. Surface plasmon resonance demonstrated fourfold to sixfold improvement in CD4M47 affinity for gp120 to a level about threefold tighter than that of CD4 itself. Assessment of the neutralization properties of CD4M47 against a diverse range of isolates spanning from HIV-1 to SIVcpz showed that CD4M47 retained the extraordinary breadth of the parent CD4M33, but yielded only limited improvements in neutralization potencies. Crystal structures of CD4M47 and a phenylalanine variant ([Phe23]M47) were determined at resolutions of 2.4 and 2.6 A, in ternary complexes with HIV-1 gp120 and the 17b antibody. Analysis of these structures revealed a correlation between mimetic affinity for gp120 and overall mimetic-gp120 interactive surface. A correlation was also observed between CD4- and mimetic-induced gp120 structural similarity and CD4- and mimetic-induced gp120 affinity for the CCR5 coreceptor. Despite mimetic substitutions, including a glycine-to-(d)-proline change, the gp120 conformation induced by CD4M47 was as close or closer to the conformation induced by CD4 as the one induced by the parent CD4M33. Our results demonstrate the ability of combinatorial chemistry to optimize a disulfide-containing miniprotein, and of structural biology to decipher the resultant interplay between binding affinity, neutralization breadth, molecular mimicry, and induced affinity for CCR5.

Beta-hairpin peptidomimetics: design, structures and biological activities

The folded 3D structures of peptides and proteins provide excellent starting points for the design of synthetic molecules that mimic key epitopes (or surface patches) involved in protein-protein and protein-nucleic acid interactions. Protein epitope mimetics (PEMs) may recapitulate not only the structural and conformational properties of the target epitope but also their biological activities. By transferring the epitope from a recombinant to a synthetic scaffold that can be produced by parallel combinatorial methods, it is possible to optimize properties through iterative cycles of library synthesis and screening, and even to evolve new biological activities. One very interesting scaffold is found in beta-hairpin motifs, which are used by many proteins to mediate molecular recognition events. This motif is readily amenable to PEM design, for example, by transplanting hairpin loop sequences from folded proteins onto hairpin-stabilizing templates, such as the dipeptide D-Pro-L-Pro. In addition, beta-hairpin peptidomimetics can also be exploited to mimic other types of epitopes, such as those based on alpha-helical secondary structures. The size and shape of beta-hairpin PEMs appear well suited for the design of inhibitors of both protein-protein and protein-nucleic acid interactions, endeavors that have so far proven difficult using small "drug-like" molecules. In recent work, it was shown that beta-hairpin PEMs can be designed that mimic the canonical conformations of antibody hypervariable loops, suggesting that novel small-molecule antibody mimics may be feasible. Using naturally occurring peptides as starting points, beta-hairpin mimetics have been discovered that possess antimicrobial activity, while others are potent inhibitors of the chemokine receptor CXCR4. Beta-hairpin PEMs have also been designed and optimized that mimic an alpha-helical epitope in p53 and so block its interaction with HDM2. A crystal structure of one HDM2-mimetic complex revealed how the surface of the protein had adapted to the shape of the hairpin, thereby enhancing inhibitor affinity. Small folded RNA motifs also make interesting targets for inhibitor design. For example, beta-hairpin mimetics have been designed and optimized that bind with high affinity and good selectivity to the TAR and RRE RNA motifs from HIV-1. Solution structures of the mimetics both free and bound to the RNA target provided some surprises, as well as an improved understanding of the mechanisms of binding. These mimetics represent still a relatively new family of RNA-binding molecules, but clearly one with potential for development into novel antiviral agents.

Differences between high- and low-affinity complexes of enzymes and nonenzymes

Physical differences in small molecule binding between enzymes and nonenzymes were found through mining the protein-ligand database, Binding MOAD (Mother of All Databases). The data suggest that divergent approaches may be more productive for improving the affinity of ligands for the two classes of proteins. High-affinity ligands of enzymes are much larger than those with low affinity, indicating that the addition of complementary functional groups is likely to improve the affinity of an enzyme inhibitor. However, this process may not be as fruitful for ligands of nonenzymes. High- and low-affinity ligands of nonenzymes are nearly the same size, so modest modifications and isosteric replacement might be most productive. The inherent differences between enzymes and nonenzymes have significant ramifications for scoring functions and structure-based drug design. In particular, nonenzymes were found to have greater ligand efficiencies than enzymes. Ligand efficiencies are often used to indicate druggability of a target, and this finding supports the feasibility of nonenzymes as drug targets. The differences in ligand efficiencies do not appear to come from the ligands; instead, the pockets yield different amino acid compositions despite very similar distributions of amino acids in the overall protein sequences.

Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design

Use of solvent mapping, based on multiple-copy minimization (MCM) techniques, is common in structure-based drug discovery. The minima of small-molecule probes define locations for complementary interactions within a binding pocket. Here, we present improved methods for MCM. In particular, a Jarvis-Patrick (JP) method is outlined for grouping the final locations of minimized probes into physical clusters. This algorithm has been tested through a study of protein-protein interfaces, showing the process to be robust, deterministic, and fast in the mapping of protein "hot spots." Improvements in the initial placement of probe molecules are also described. A final application to HIV-1 protease shows how our automated technique can be used to partition data too complicated to analyze by hand. These new automated methods may be easily and quickly extended to other protein systems, and our clustering methodology may be readily incorporated into other clustering packages.

The interaction of CK2alpha and CK2beta, the subunits of protein kinase CK2, requires CK2beta in a preformed conformation and is enthalpically driven

The protein kinase CK2 (former name: "casein kinase 2") predominantly occurs as a heterotetrameric holoenzyme composed of two catalytic chains (CK2alpha) and two noncatalytic subunits (CK2beta). The CK2beta subunits form a stable dimer to which the CK2alpha monomers are attached independently. In contrast to the cyclins in the case of the cyclin-dependent kinases CK2beta is no on-switch of CK2alpha; rather the formation of the CK2 holoenzyme is accompanied with an overall change of the enzyme's profile including a modulation of the substrate specificity, an increase of the thermostability, and an allocation of docking sites for membranes and other proteins. In this study we used C-terminal deletion variants of human CK2alpha and CK2beta that were enzymologically fully competent and in particular able to form a heterotetrameric holoenzyme. With differential scanning calorimetry (DSC) we confirmed the strong thermostabilization effect of CK2alpha on CK2beta with an upshift of the CK2alpha melting temperature of more than 9 degrees . Using isothermal titration calorimetry (ITC) we measured a dissociation constant of 12.6 nM. This high affinity between CK2alpha and CK2beta is mainly caused by enthalpic rather than entropic contributions. Finally, we determined a crystal structure of the CK2beta construct to 2.8 A resolution and revealed by structural comparisons with the CK2 holoenzyme structure that the CK2beta conformation is largely conserved upon association with CK2alpha, whereas the latter undergoes significant structural adaptations of its backbone.

Crystal structure and carbohydrate analysis of Nipah virus attachment glycoprotein: a template for antiviral and vaccine design

Two members of the paramyxovirus family, Nipah virus (NiV) and Hendra virus (HeV), are recent additions to a growing number of agents of emergent diseases which use bats as a natural host. Identification of ephrin-B2 and ephrin-B3 as cellular receptors for these viruses has enabled the development of immunotherapeutic reagents which prevent virus attachment and subsequent fusion. Here we present the structural analysis of the protein and carbohydrate components of the unbound viral attachment glycoprotein of NiV glycoprotein (NiV-G) at a 2.2-A resolution. Comparison with its ephrin-B2-bound form reveals that conformational changes within the envelope glycoprotein are required to achieve viral attachment. Structural differences are particularly pronounced in the 579-590 loop, a major component of the ephrin binding surface. In addition, the 236-245 loop is rather disordered in the unbound structure. We extend our structural characterization of NiV-G with mass spectrometric analysis of the carbohydrate moieties. We demonstrate that NiV-G is largely devoid of the oligomannose-type glycans that in viruses such as human immunodeficiency virus type 1 and Ebola virus influence viral tropism and the host immune response. Nevertheless, we find putative ligands for the endothelial cell lectin, LSECtin. Finally, by mapping structural conservation and glycosylation site positions from other members of the paramyxovirus family, we suggest the molecular surface involved in oligomerization. These results suggest possible pathways of virus-host interaction and strategies for the optimization of recombinant vaccines.

Protein–protein interaction and quaternary structure

Protein–protein recognition plays an essential role in structure and function. Specific non-covalent interactions stabilize the structure of macromolecular assemblies, exemplified in this review by oligomeric proteins and the capsids of icosahedral viruses. They also allow proteins to form complexes that have a very wide range of stability and lifetimes and are involved in all cellular processes. We present some of the structure-based computational methods that have been developed to characterize the quaternary structure of oligomeric proteins and other molecular assemblies and analyze the properties of the interfaces between the subunits. We compare the size, the chemical and amino acid compositions and the atomic packing of the subunit interfaces of protein–protein complexes, oligomeric proteins, viral capsids and protein–nucleic acid complexes. These biologically significant interfaces are generally close-packed, whereas the non-specific interfaces between molecules in protein crystals are loosely packed, an observation that gives a structural basis to specific recognition. A distinction is made within each interface between a core that contains buried atoms and a solvent accessible rim. The core and the rim differ in their amino acid composition and their conservation in evolution, and the distinction helps correlating the structural data with the results of site-directed mutagenesis and in vitro studies of self-assembly.

Cortisone dissociates the Shaker family K+ channels from their beta subunits

The Shaker family voltage-dependent potassium channels (Kv1) are expressed in a wide variety of cells and are essential for cellular excitability. In humans, loss-of-function mutations of Kv1 channels lead to hyperexcitability and are directly linked to episodic ataxia and atrial fibrillation. All Kv1 channels assemble with beta subunits (Kv betas), and certain Kv betas, for example Kv beta 1, have an N-terminal segment that closes the channel by the N-type inactivation mechanism. In principle, dissociation of Kv beta 1, although never reported, should eliminate inactivation and thus potentiate Kv1 current. We found that cortisone increases rat Kv1 channel activity by binding to Kv beta 1. A crystal structure of the Kv beta-cortisone complex was solved to 1.82-A resolution and revealed novel cortisone binding sites. Further studies demonstrated that cortisone promotes dissociation of Kv beta. The new mode of channel modulation may be explored by native or synthetic ligands to fine-tune cellular excitability.

The unfoldomics decade: an update on intrinsically disordered proteins

BACKGROUND

Our first predictor of protein disorder was published just over a decade ago in the Proceedings of the IEEE International Conference on Neural Networks (Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK (1997) Identifying disordered regions in proteins from amino acid sequence. Proceedings of the IEEE International Conference on Neural Networks, 1: 90-95). By now more than twenty other laboratory groups have joined the efforts to improve the prediction of protein disorder. While the various prediction methodologies used for protein intrinsic disorder resemble those methodologies used for secondary structure prediction, the two types of structures are entirely different. For example, the two structural classes have very different dynamic properties, with the irregular secondary structure class being much less mobile than the disorder class. The prediction of secondary structure has been useful. On the other hand, the prediction of intrinsic disorder has been revolutionary, leading to major modifications of the more than 100 year-old views relating protein structure and function. Experimentalists have been providing evidence over many decades that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. In addition, experimentalists are also showing that, for many proteins, their functions depend on the unstructured rather than structured state; such results are in marked contrast to the greater than hundred year old views such as the lock and key hypothesis. Despite extensive data on many important examples, including disease-associated proteins, the importance of disorder for protein function has been largely ignored. Indeed, to our knowledge, current biochemistry books don't present even one acknowledged example of a disorder-dependent function, even though some reports of disorder-dependent functions are more than 50 years old. The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins.

RESULTS

Disorder prediction has been important for showing that the relatively few experimentally characterized examples are members of a very large collection of related disordered proteins that are wide-spread over all three domains of life. Many significant biological functions are now known to depend directly on, or are importantly associated with, the unfolded or partially folded state. Here our goal is to review the key discoveries and to weave these discoveries together to support novel approaches for understanding sequence-function relationships.

CONCLUSION

Intrinsically disordered protein is common across the three domains of life, but especially common among the eukaryotic proteomes. Signaling sequences and sites of posttranslational modifications are frequently, or very likely most often, located within regions of intrinsic disorder. Disorder-to-order transitions are coupled with the adoption of different structures with different partners. Also, the flexibility of intrinsic disorder helps different disordered regions to bind to a common binding site on a common partner. Such capacity for binding diversity plays important roles in both protein-protein interaction networks and likely also in gene regulation networks. Such disorder-based signaling is further modulated in multicellular eukaryotes by alternative splicing, for which such splicing events map to regions of disorder much more often than to regions of structure. Associating alternative splicing with disorder rather than structure alleviates theoretical and experimentally observed problems associated with the folding of different length, isomeric amino acid sequences. The combination of disorder and alternative splicing is proposed to provide a mechanism for easily "trying out" different signaling pathways, thereby providing the mechanism for generating signaling diversity and enabling the evolution of cell differentiation and multicellularity. Finally, several recent small molecules of interest as potential drugs have been shown to act by blocking protein-protein interactions based on intrinsic disorder of one of the partners. Study of these examples has led to a new approach for drug discovery, and bioinformatics analysis of the human proteome suggests that various disease-associated proteins are very rich in such disorder-based drug discovery targets.