Principles of Protein−Protein Interactions: What are the Preferred Ways For Proteins To Interact?

Lipophilic Isosteres of a π-π Stacking Interaction: New Inhibitors of the Bcl-2-Bak Protein-Protein Interaction

The discovery of new Bcl-2 protein-protein interaction antagonists is described. We replaced the northern fragment of ABT737 (π-π stacking interactions) with structurally simplified hydrophobic cage structures with much reduced conformational flexibility and rotational freedom. The binding mode of the compounds was elucidated by X-ray crystallography, and the compounds showed excellent oral bioavailability and clearance in rat PK studies.

2-O-alkylated para-benzamide α-helix mimetics: the role of scaffold curvature

The design and synthesis of a new 2-O-alklyated benzamide α-helix mimetic is described. Comparison with regioisomeric 3-O-alkylated benzamides permits a preliminary evaluation of the role that mimetic curvature has in determining molecular recognition properties.

Protein-protein interaction site predictions with three-dimensional probability distributions of interacting atoms on protein surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors.

Constructing structural networks of signaling pathways on the proteome scale

Proteins function through their interactions, and the availability of protein interaction networks could help in understanding cellular processes. However, the known structural data are limited and the classical network node-and-edge representation, where proteins are nodes and interactions are edges, shows only which proteins interact; not how they interact. Structural networks provide this information. Protein-protein interface structures can also indicate which binding partners can interact simultaneously and which are competitive, and can help forecasting potentially harmful drug side effects. Here, we use a powerful protein-protein interactions prediction tool which is able to carry out accurate predictions on the proteome scale to construct the structural network of the extracellular signal-regulated kinases (ERK) in the mitogen-activated protein kinase (MAPK) signaling pathway. This knowledge-based method, PRISM, is motif-based, and is combined with flexible refinement and energy scoring. PRISM predicts protein interactions based on structural and evolutionary similarity to known protein interfaces.

Effect of interprotein polarization on protein-protein binding energy

Molecular dynamics simulation in explicit water for the binding of the benchmark barnase-barstar complex was carried out to investigate the effect polarization of interprotein hydrogen bonds on its binding free energy. Our study is based on the AMBER force field but with polarized atomic charges derived from fragment quantum mechanical calculation for the protein complex. The quantum-derived atomic charges include the effect of polarization of interprotein hydrogen bonds, which was absent in the standard force fields that were used in previous theoretical calculations of barnase-barstar binding energy. This study shows that this polarization effect impacts both the static (electronic) and dynamic interprotein electrostatic interactions and significantly lowers the free energy of the barnase-barstar complex.

Fast and accurate modeling of protein-protein interactions by combining template-interface-based docking with flexible refinement

The similarity between folding and binding led us to posit the concept that the number of protein-protein interface motifs in nature is limited, and interacting protein pairs can use similar interface architectures repeatedly, even if their global folds completely vary. Thus, known protein-protein interface architectures can be used to model the complexes between two target proteins on the proteome scale, even if their global structures differ. This powerful concept is combined with a flexible refinement and global energy assessment tool. The accuracy of the method is highly dependent on the structural diversity of the interface architectures in the template dataset. Here, we validate this knowledge-based combinatorial method on the Docking Benchmark and show that it efficiently finds high-quality models for benchmark complexes and their binding regions even in the absence of template interfaces having sequence similarity to the targets. Compared to "classical" docking, it is computationally faster; as the number of target proteins increases, the difference becomes more dramatic. Further, it is able to distinguish binders from nonbinders. These features allow performing large-scale network modeling. The results on an independent target set (proteins in the p53 molecular interaction map) show that current method can be used to predict whether a given protein pair interacts. Overall, while constrained by the diversity of the template set, this approach efficiently produces high-quality models of protein-protein complexes. We expect that with the growing number of known interface architectures, this type of knowledge-based methods will be increasingly used by the broad proteomics community.

Roles of residues in the interface of transient protein-protein complexes before complexation

Transient protein-protein interactions play crucial roles in all facets of cellular physiology. Here, using an analysis on known 3-D structures of transient protein-protein complexes, their corresponding uncomplexed forms and energy calculations we seek to understand the roles of protein-protein interfacial residues in the unbound forms. We show that there are conformationally near invariant and evolutionarily conserved interfacial residues which are rigid and they account for ∼65% of the core interface. Interestingly, some of these residues contribute significantly to the stabilization of the interface structure in the uncomplexed form. Such residues have strong energetic basis to perform dual roles of stabilizing the structure of the uncomplexed form as well as the complex once formed while they maintain their rigid nature throughout. This feature is evolutionarily well conserved at both the structural and sequence levels. We believe this analysis has general bearing in the prediction of interfaces and understanding molecular recognition.

Corresponding functional dynamics across the Hsp90 Chaperone family: insights from a multiscale analysis of MD simulations

Understanding how local protein modifications, such as binding small-molecule ligands, can trigger and regulate large-scale motions of large protein domains is a major open issue in molecular biology. We address various aspects of this problem by analyzing and comparing atomistic simulations of Hsp90 family representatives for which crystal structures of the full length protein are available: mammalian Grp94, yeast Hsp90 and E.coli HtpG. These chaperones are studied in complex with the natural ligands ATP, ADP and in the Apo state. Common key aspects of their functional dynamics are elucidated with a novel multi-scale comparison of their internal dynamics. Starting from the atomic resolution investigation of internal fluctuations and geometric strain patterns, a novel analysis of domain dynamics is developed. The results reveal that the ligand-dependent structural modulations mostly consist of relative rigid-like movements of a limited number of quasi-rigid domains, shared by the three proteins. Two common primary hinges for such movements are identified. The first hinge, whose functional role has been demonstrated by several experimental approaches, is located at the boundary between the N-terminal and Middle-domains. The second hinge is located at the end of a three-helix bundle in the Middle-domain and unfolds/unpacks going from the ATP- to the ADP-state. This latter site could represent a promising novel druggable allosteric site common to all chaperones.

Understanding the connections between structure, binding, dynamics and function in proteins is one of the most fascinating problems in biology and is actively investigated experimentally and computationally. In the latter context, significant advancements are possible by exposing the causal link between the fine atomic-scale protein-ligand interactions and the large-scale protein motions. One ideal avenue to explore this relationship is given by proteins of the Hsp90 chaperones family. Their dynamics is regulated by ATP binding and hydrolysis, which activates the onset of large-scale, functional conformational changes. Herein, we concentrated on three homologs with markedly different structural organization—mammalian Grp94, yeast Hsp90 and prokaryotic HtpG—and developed a novel computational multiscale approach to detect and characterize the salient traits of the functionally-oriented internal dynamics of the three chaperones. The comparative analysis, which exploits a novel highly simplified, yet viable, description of the protein internal dynamics, highlights fundamental mechanical aspects that preside the ligand-dependent conformational arrangements in all chaperones. For the three molecules, two corresponding regions are singled out as ligand-susceptible hinges for the large-scale internal motion. On the basis of this and other evidence it is suggested that these regions represent functionally relevant druggable substructures in the discovery of novel allosteric modulators.

Visualisation of variable binding pockets on protein surfaces by probabilistic analysis of related structure sets

Background

Protein structures provide a valuable resource for rational drug design. For a protein with no known ligand, computational tools can predict surface pockets that are of suitable size and shape to accommodate a complementary small-molecule drug. However, pocket prediction against single static structures may miss features of pockets that arise from proteins' dynamic behaviour. In particular, ligand-binding conformations can be observed as transiently populated states of the apo protein, so it is possible to gain insight into ligand-bound forms by considering conformational variation in apo proteins. This variation can be explored by considering sets of related structures: computationally generated conformers, solution NMR ensembles, multiple crystal structures, homologues or homology models. It is non-trivial to compare pockets, either from different programs or across sets of structures. For a single structure, difficulties arise in defining particular pocket's boundaries. For a set of conformationally distinct structures the challenge is how to make reasonable comparisons between them given that a perfect structural alignment is not possible.

Results

We have developed a computational method, Provar, that provides a consistent representation of predicted binding pockets across sets of related protein structures. The outputs are probabilities that each atom or residue of the protein borders a predicted pocket. These probabilities can be readily visualised on a protein using existing molecular graphics software. We show how Provar simplifies comparison of the outputs of different pocket prediction algorithms, of pockets across multiple simulated conformations and between homologous structures. We demonstrate the benefits of use of multiple structures for protein-ligand and protein-protein interface analysis on a set of complexes and consider three case studies in detail: i) analysis of a kinase superfamily highlights the conserved occurrence of surface pockets at the active and regulatory sites; ii) a simulated ensemble of unliganded Bcl2 structures reveals extensions of a known ligand-binding pocket not apparent in the apo crystal structure; iii) visualisations of interleukin-2 and its homologues highlight conserved pockets at the known receptor interfaces and regions whose conformation is known to change on inhibitor binding.

Conclusions

Through post-processing of the output of a variety of pocket prediction software, Provar provides a flexible approach to the analysis and visualization of the persistence or variability of pockets in sets of related protein structures.

Druggability assessment of protein-protein interfaces

Recent success stories concerning the targeting of protein-protein interactions (PPIs) have led to an increased focus on this challenging target class for drug discovery. This article explores various avenues to assess the druggability of PPIs and describes a druggability decision flow chart, which can be applied to any PPI target. This flow chart not only covers small molecules but also peptidomimetics, peptides and conformationally restricted peptides as potential modalities for targeting PPIs. Additionally, a retrospective analysis of PPI druggability using various computational tools is summarized. The application of a systematic approach as presented in this paper will increase confidence that modulators (e.g., small organic molecules or peptides) can ultimately be identified for a particular target before a decision is made to commit significant discovery resources.

The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation

Protein-protein and protein-ligand interactions are ubiquitous in a biological cell. Here, we report a comprehensive study of the distribution of protein-ligand interaction sites, namely ligand-binding pockets, around protein-protein interfaces where protein-protein interactions occur. We inspected a representative set of 1,611 representative protein-protein complexes and identified pockets with a potential for binding small molecule ligands. The majority of these pockets are within a 6 Å distance from protein interfaces. Accordingly, in about half of ligand-bound protein-protein complexes, amino acids from both sides of a protein interface are involved in direct contacts with at least one ligand. Statistically, ligands are closer to a protein-protein interface than a random surface patch of the same solvent accessible surface area. Similar results are obtained in an analysis of the ligand distribution around domain-domain interfaces of 1,416 nonredundant, two-domain protein structures. Furthermore, comparable sized pockets as observed in experimental structures are present in artificially generated protein complexes, suggesting that the prominent appearance of pockets around protein interfaces is mainly a structural consequence of protein packing and thus, is an intrinsic geometric feature of protein structure. Nature may take advantage of such a structural feature by selecting and further optimizing for biological function. We propose that packing nearby protein-protein or domain-domain interfaces is a major route to the formation of ligand-binding pockets.

Novel insights through the integration of structural and functional genomics data with protein networks

In recent years, major advances in genomics, proteomics, macromolecular structure determination, and the computational resources capable of processing and disseminating the large volumes of data generated by each have played major roles in advancing a more systems-oriented appreciation of biological organization. One product of systems biology has been the delineation of graph models for describing genome-wide protein-protein interaction networks. The network organization and topology which emerges in such models may be used to address fundamental questions in an array of cellular processes, as well as biological features intrinsic to the constituent proteins (or "nodes") themselves. However, graph models alone constitute an abstraction which neglects the underlying biological and physical reality that the network's nodes and edges are highly heterogeneous entities. Here, we explore some of the advantages of introducing a protein structural dimension to such models, as the marriage of conventional network representations with macromolecular structural data helps to place static node and edge constructs in a biologically more meaningful context. We emphasize that 3D protein structures constitute a valuable conceptual and predictive framework by discussing examples of the insights provided, such as enabling in silico predictions of protein-protein interactions, providing rational and compelling classification schemes for network elements, as well as revealing interesting intrinsic differences between distinct node types, such as disorder and evolutionary features, which may then be rationalized in light of their respective functions within networks.

Composite structural motifs of binding sites for delineating biological functions of proteins

Most biological processes are described as a series of interactions between proteins and other molecules, and interactions are in turn described in terms of atomic structures. To annotate protein functions as sets of interaction states at atomic resolution, and thereby to better understand the relation between protein interactions and biological functions, we conducted exhaustive all-against-all atomic structure comparisons of all known binding sites for ligands including small molecules, proteins and nucleic acids, and identified recurring elementary motifs. By integrating the elementary motifs associated with each subunit, we defined composite motifs that represent context-dependent combinations of elementary motifs. It is demonstrated that function similarity can be better inferred from composite motif similarity compared to the similarity of protein sequences or of individual binding sites. By integrating the composite motifs associated with each protein function, we define meta-composite motifs each of which is regarded as a time-independent diagrammatic representation of a biological process. It is shown that meta-composite motifs provide richer annotations of biological processes than sequence clusters. The present results serve as a basis for bridging atomic structures to higher-order biological phenomena by classification and integration of binding site structures.

Towards structure-based protein drug design

Structure-based drug design for chemical molecules has been widely used in drug discovery in the last 30 years. Many successful applications have been reported, especially in the field of virtual screening based on molecular docking. Recently, there has been much progress in fragment-based as well as de novo drug discovery. As many protein-protein interactions can be used as key targets for drug design, one of the solutions is to design protein drugs based directly on the protein complexes or the target structure. Compared with protein-ligand interactions, protein-protein interactions are more complicated and present more challenges for design. Over the last decade, both sampling efficiency and scoring accuracy of protein-protein docking have increased significantly. We have developed several strategies for structure-based protein drug design. A grafting strategy for key interaction residues has been developed and successfully applied in designing erythropoietin receptor-binding proteins. Similarly to small-molecule design, we also tested de novo protein-binder design and a virtual screen of protein binders using protein-protein docking calculations. In comparison with the development of structure-based small-molecule drug design, we believe that structure-based protein drug design has come of age.

The use of small molecule probes to study spatially separated stimulus-induced signaling pathways

Simultaneous activation of signaling pathways requires dynamic assembly of higher-order protein complexes at the cytoplasmic domains of membrane-associated receptors in a stimulus-specific manner. Here, using the paradigm of cellular activation through cytokine and innate immune receptors, we demonstrate the proof-of-principle application of small molecule probes for the dissection of receptor-proximal signaling processes, such as activation of the transcription factor NF-κB and the protein kinase p38.

Expanding the conformational selection paradigm in protein-ligand docking

Conformational selection emerges as a theme in macromolecular interactions. Data validate it as a prevailing mechanism in protein-protein, protein-DNA, protein-RNA, and protein-small molecule drug recognition. This raises the question of whether this fundamental biomolecular binding mechanism can be used to improve drug docking and discovery. Actually, in practice this has already been taking place for some years in increasing numbers. Essentially, it argues for using not a single conformer, but an ensemble. The paradigm of conformational selection holds that because the ensemble is heterogeneous, within it there will be states whose conformation matches that of the ligand. Even if the population of this state is low, since it is favorable for binding the ligand, it will bind to it with a subsequent population shift toward this conformer. Here we suggest expanding it by first modeling all protein interactions in the cell by using Prism, an efficient motif-based protein-protein interaction modeling strategy, followed by ensemble generation. Such a strategy could be particularly useful for signaling proteins, which are major targets in drug discovery and bind multiple partners through a shared binding site, each with some-minor or major-conformational change.

Hot spots and transient pockets: predicting the determinants of small-molecule binding to a protein-protein interface

Protein-protein interfaces are considered difficult targets for small-molecule protein-protein interaction modulators (PPIMs ). Here, we present for the first time a computational strategy that simultaneously considers aspects of energetics and plasticity in the context of PPIM binding to a protein interface. The strategy aims at identifying the determinants of small-molecule binding, hot spots, and transient pockets, in a protein-protein interface in order to make use of this knowledge for predicting binding modes of and ranking PPIMs with respect to their affinity. When applied to interleukin-2 (IL-2), the computationally inexpensive constrained geometric simulation method FRODA outperforms molecular dynamics simulations in sampling hydrophobic transient pockets. We introduce the PPIAnalyzer approach for identifying transient pockets on the basis of geometrical criteria only. A sequence of docking to identified transient pockets, starting structure selection based on hot spot information, RMSD clustering and intermolecular docking energies, and MM-PBSA calculations allows one to enrich IL-2 PPIMs from a set of decoys and to discriminate between subgroups of IL-2 PPIMs with low and high affinity. Our strategy will be applicable in a prospective manner where nothing else than a protein-protein complex structure is known; hence, it can well be the first step in a structure-based endeavor to identify PPIMs.

Modulating protein-protein interactions with small molecules: the importance of binding hotspots

The modulation of protein-protein interactions (PPIs) by small drug-like molecules is a relatively new area of research and has opened up new opportunities in drug discovery. However, the progress made in this area is limited to a handful of known cases of small molecules that target specific diseases. With the increasing availability of protein structure complexes, it is highly important to devise strategies exploiting homologous structure space on a large scale for discovering putative PPIs that could be attractive drug targets. Here, we propose a scheme that allows performing large-scale screening of all protein complexes and finding putative small-molecule and/or peptide binding sites overlapping with protein-protein binding sites (so-called "multibinding sites"). We find more than 600 nonredundant proteins from 60 protein families with multibinding sites. Moreover, we show that the multibinding sites are mostly observed in transient complexes, largely overlap with the binding hotspots and are more evolutionarily conserved than other interface sites. We investigate possible mechanisms of how small molecules may modulate protein-protein binding and discuss examples of new candidates for drug design.

Electron microscopy and x-ray diffraction evidence for two Z-band structural states

In vertebrate muscles, Z-bands connect adjacent sarcomeres, incorporate several cell signaling proteins, and may act as strain sensors. Previous electron microscopy (EM) showed Z-bands reversibly switch between a relaxed, "small-square" structure, and an active, "basketweave" structure, but the mechanism of this transition is unknown. Here, we found the ratio of small-square to basketweave in relaxed rabbit psoas muscle varied with temperature, osmotic pressure, or ionic strength, independent of activation. By EM, the A-band and both Z-band lattice spacings varied with temperature and pressure, not ionic strength; however, the basketweave spacing was consistently 10% larger than small-square. We next sought evidence for the two Z-band structures in unfixed muscles using x-ray diffraction, which indicated two Z-reflections whose intensity ratios and spacings correspond closely to the EM measurements for small-square and basketweave if the EM spacings are adjusted for 20% shrinkage due to EM processing. We conclude that the two Z-reflections arise from the small-square and basketweave forms of the Z-band as seen by EM. Regarding the mechanism of transition during activation, the effects of Ca(2+) in the presence of force inhibitors suggested that the interconversion of Z-band forms was correlated with tropomyosin movement on actin.

Sledge G, Khanna R, Meroueh SO. Targeting multiple conformations leads to small molecule inhibitors of the uPAR·uPA protein-protein interaction that block cancer cell invasion

Interaction of the urokinase receptor (uPAR) with its binding partners such as the urokinase-type plasminogen activator (uPA) at the cell surface triggers a series of proteolytic and signaling events that promote invasion and metastasis. Here, we report the discovery of a small molecule (IPR-456) and its derivatives that inhibit the tight uPAR·uPA protein-protein interaction. IPR-456 was discovered by virtual screening against multiple conformations of uPAR sampled from explicit-solvent molecular dynamics simulations. Biochemical characterization reveal that the compound binds to uPAR with submicromolar affinity (K(d) = 310 nM) and inhibits the tight protein-protein interaction with an IC(50) of 10 μM. Free energy calculations based on explicit-solvent molecular dynamics simulations suggested the importance of a carboxylate moiety on IPR-456, which was confirmed by the activity of several derivatives including IPR-803. Immunofluorescence imaging showed that IPR-456 inhibited uPA binding to uPAR of breast MDA-MB-231 tumor cells with an IC(50) of 8 μM. The compounds blocked MDA-MB-231 cell invasion, but IPR-456 showed little effect on MDA-MB-231 migration and no effect on adhesion, suggesting that uPAR mediates these processes through its other binding partners.

Sequence and structural features of binding site residues in protein-protein complexes: comparison with protein-nucleic acid complexes

Background

Protein-protein interactions are important for several cellular processes. Understanding the mechanism of protein-protein recognition and predicting the binding sites in protein-protein complexes are long standing goals in molecular and computational biology.

Methods

We have developed an energy based approach for identifying the binding site residues in protein–protein complexes. The binding site residues have been analyzed with sequence and structure based parameters such as binding propensity, neighboring residues in the vicinity of binding sites, conservation score and conformational switching.

Results

We observed that the binding propensities of amino acid residues are specific for protein-protein complexes. Further, typical dipeptides and tripeptides showed high preference for binding, which is unique to protein-protein complexes. Most of the binding site residues are highly conserved among homologous sequences. Our analysis showed that 7% of residues changed their conformations upon protein-protein complex formation and it is 9.2% and 6.6% in the binding and non-binding sites, respectively. Specifically, the residues Glu, Lys, Leu and Ser changed their conformation from coil to helix/strand and from helix to coil/strand. Leu, Ser, Thr and Val prefer to change their conformation from strand to coil/helix.

Conclusions

The results obtained in this study will be helpful for understanding and predicting the binding sites in protein-protein complexes.

Identification of hub proteins from sequence

Identification of hub proteins from sequence is a challenge in molecular biology. Therefore, it is of interest to predict protein hubs in networks. We describe the prediction of protein "hub" using physiochemical, thermodynamic and conformational properties of amino acid residues in sequence. We have used twenty sequence based features to identify hub behaviour. Linear discriminant analysis and normalised Bayesian approach were utilized for identifying hub proteins solely using these sequence features in E. coli/H. sapiens datasets with accuracies of 99.5/98.6, 87.8/89.6 and 90.1/92.6, respectively.

Clustered hydrophobic amino acids in amphipathic helices mediate erlin1/2 complex assembly

Erlin1 and erlin2 are highly homologous, ∼40kDa, endoplasmic reticulum membrane proteins that assemble into a ring-shaped complex with a mass of ∼2 MDa. How this complex is formed is not understood, but appears to involve multiple interactions, including a coiled-coil region that mediates lower-order erlin assembly, and a short hydrophobic region, termed the "assembly domain", that mediates higher-order assembly into ∼2 MDa complexes. Here we have used molecular modeling, mutagenesis and cross-linking to examine the role of the assembly domain in higher-order assembly. We find (i) that the assembly domains of erlin1 and erlin2 are amphipathic helices, (ii) that erlin1 alone and erlin2 alone can assemble into ∼2 MDa complexes, (iii) that higher-order assembly is strongly inhibited by point mutations to the assembly domain, (iv) that three interacting hydrophobic residues in the assembly domain and aromaticity are essential for higher-order assembly, and (iv) that while erlins1 and 2 are equally capable of forming lower-order homo- and hetero-oligomers, hetero-oligomers are the most prevalent form when erlin1 and erlin2 are co-expressed. Overall, we conclude that the ∼2 MDa erlin1/2 complex is composed of an assemblage of lower-order hetero-oligomers, probably heterotrimers, linked together by assembly domain hydrophobic residues.

Critical residue that promotes protein dimerization: a story of partially exposed Phe25 in 14-3-3σ

Many proteins exist and function as oligomers. While hydrophobic interactions have been recognized as the major driving force for oligomerization, detailed molecular mechanisms for the assembly are unknown. Here, we used 14-3-3σ as a model protein and investigated the role of hydrophobic residues at the dimeric interface using MD simulations and coimmunoprecipitations. We found that a half-exposed and half-buried residue in the interface, Phe(25), plays a more important role in promoting homodimerization than the hydrophobic core residues by organizing both favorable hydrophobic and hydrophilic interactions. Phe(25) is critical in packing and stabilizing hydrophobic core residues. We conclude that the structural stability of hydrophobic cores is critical for a stable homodimer complex and this stable property can be bestowed by residues outside of hydrophobic core. The important organizing activity of Phe(25) for homodimerization of 14-3-3σ originates from its unique physical location, rigidity, size, and hydrophobicity. Thus, hydrophobic residues that are not deeply buried at the oligomeric interface may play important but different roles from the buried core residues and they may promote oligomerization by organizing co-operativity of core and other residues for favorable hydrophobic and electrostatic interactions.

Assessing helical protein interfaces for inhibitor design

Structure-based design of synthetic inhibitors of protein-protein interactions (PPIs) requires adept molecular design and synthesis strategies as well as knowledge of targetable complexes. To address the significant gap between the elegant design of helix mimetics and their sporadic use in biology, we analyzed the full set of helical protein interfaces in the Protein Data Bank to obtain a snapshot of how helices that are critical for complex formation interact with the partner proteins. The results of this study are expected to guide the systematic design of synthetic inhibitors of PPIs. We have experimentally evaluated new classes of protein complexes that emerged from this data set, highlighting the significance of the results described herein.

Beauty is skin deep: a surface monolayer perspective on nanoparticle interactions with cells and bio-macromolecules

Surface recognition of biosystems is a critical component in the development of novel biosensors and delivery vehicles, and for the therapeutic regulation of biological processes. Monolayer-protected nanoparticles present a highly versatile scaffold for selective interaction with bio-macromolecules and cells. Through the engineering of the monolayer surface, nanoparticles can be tailored for surface recognition of biomolecules and cells. This review highlights recent progress in nanoparticle-bio-macromolecule/cellular interactions, emphasizing the effect of the surface monolayer structure on the interactions with proteins, DNA, and cell surfaces. The extension of these tailored interactions to hybrid nanomaterials, biosensing platforms, and delivery vehicles is also discussed.

DBAC: a simple prediction method for protein binding hot spots based on burial levels and deeply buried atomic contacts

BACKGROUND

A protein binding hot spot is a cluster of residues in the interface that are energetically important for the binding of the protein with its interaction partner. Identifying protein binding hot spots can give useful information to protein engineering and drug design, and can also deepen our understanding of protein-protein interaction. These residues are usually buried inside the interface with very low solvent accessible surface area (SASA). Thus SASA is widely used as an outstanding feature in hot spot prediction by many computational methods. However, SASA is not capable of distinguishing slightly buried residues, of which most are non hot spots, and deeply buried ones that are usually inside a hot spot.

RESULTS

We propose a new descriptor called "burial level" for characterizing residues, atoms and atomic contacts. Specifically, burial level captures the depth the residues are buried. We identify different kinds of deeply buried atomic contacts (DBAC) at different burial levels that are directly broken in alanine substitution. We use their numbers as input for SVM to classify between hot spot or non hot spot residues. We achieve F measure of 0.6237 under the leave-one-out cross-validation on a data set containing 258 mutations. This performance is better than other computational methods.

CONCLUSIONS

Our results show that hot spot residues tend to be deeply buried in the interface, not just having a low SASA value. This indicates that a high burial level is not only a necessary but also a more sufficient condition than a low SASA for a residue to be a hot spot residue. We find that those deeply buried atoms become increasingly more important when their burial levels rise up. This work also confirms the contribution of deeply buried interfacial atomic contacts to the energy of protein binding hot spot.

Structure collisions between interacting proteins

Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions.

PredUs: a web server for predicting protein interfaces using structural neighbors

We describe PredUs, an interactive web server for the prediction of protein-protein interfaces. Potential interfacial residues for a query protein are identified by 'mapping' contacts from known interfaces of the query protein's structural neighbors to surface residues of the query. We calculate a score for each residue to be interfacial with a support vector machine. Results can be visualized in a molecular viewer and a number of interactive features allow users to tailor a prediction to a particular hypothesis. The PredUs server is available at: http://wiki.c2b2.columbia.edu/honiglab_public/index.php/Software:PredUs.

Mapping the protein-protein interface between a toxin and its cognate antitoxin from the bacterial pathogen Streptococcus pyogenes

Protein--protein interactions are ubiquitous and essential for most biological processes. Although new proteomic technologies have generated large catalogs of interacting proteins, considerably less is known about these interactions at the molecular level, information that would aid in predicting protein interactions, designing therapeutics to alter these interactions, and understanding the effects of disease-producing mutations. Here we describe mapping the interacting surfaces of the bacterial toxin SPN (Streptococcus pyogenes NAD(+) hydrolase) in complex with its antitoxin IFS (immunity factor for SPN) by using hydrogen-deuterium amide exchange and electrospray ionization mass spectrometry. This approach affords data in a relatively short time for small amounts of protein, typically 5-7 pmol per analysis. The results show a good correspondence with a recently determined crystal structure of the IFS--SPN complex but additionally provide strong evidence for a folding transition of the IFS protein that accompanies its binding to SPN. The outcome shows that mass-based chemical footprinting of protein interaction surfaces can provide information about protein dynamics that is not easily obtained by other methods and can potentially be applied to large, multiprotein complexes that are out of range for most solution-based methods of biophysical analysis.

A hamiltonian replica exchange method for building protein-protein interfaces applied to a leucine zipper

Leucine zippers consist of alpha helical monomers dimerized (or oligomerized) into alpha superhelical structures known as coiled coils. Forming the correct interface of a dimer from its monomers requires an exploration of configuration space focused on the side chains of one monomer that must interdigitate with sites on the other monomer. The aim of this work is to generate good interfaces in short simulations starting from separated monomers. Methods are developed to accomplish this goal based on an extension of a previously introduced [Su and Cukier, J. Phys. Chem. B 113, 9595, (2009)] hamiltonian temperature replica exchange method (HTREM), which scales the hamiltonian in both potential and kinetic energies that was used for the simulation of dimer melting curves. The new method, HTREM_MS (MS designates mean square), focused on interface formation, adds restraints to the hamiltonians for all but the physical system, which is characterized by the normal molecular dynamics force field at the desired temperature. The restraints in the nonphysical systems serve to prevent the monomers from separating too far, and have the dual aims of enhancing the sampling of close in configurations and breaking unwanted correlations in the restrained systems. The method is applied to a 31-residue truncation of the 33-residue leucine zipper (GCN4-p1) of the yeast transcriptional activator GCN4. The monomers are initially separated by a distance that is beyond their capture length. HTREM simulations show that the monomers oscillate between dimerlike and monomerlike configurations, but do not form a stable interface. HTREM_MS simulations result in the dimer interface being faithfully reconstructed on a 2 ns time scale. A small number of systems (one physical and two restrained with modified potentials and higher effective temperatures) are sufficient. An in silico mutant that should not dimerize because it lacks charged residues that provide electrostatic stabilization of the dimer does not with HTREM_MS, giving confidence in the method. The interface formation time scale is sufficiently short that using HTREM_MS as a screening tool to validate leucine zipper design methods may be feasible.

Prediction of protein-protein interactions: unifying evolution and structure at protein interfaces

The vast majority of the chores in the living cell involve protein-protein interactions. Providing details of protein interactions at the residue level and incorporating them into protein interaction networks are crucial toward the elucidation of a dynamic picture of cells. Despite the rapid increase in the number of structurally known protein complexes, we are still far away from a complete network. Given experimental limitations, computational modeling of protein interactions is a prerequisite to proceed on the way to complete structural networks. In this work, we focus on the question 'how do proteins interact?' rather than 'which proteins interact?' and we review structure-based protein-protein interaction prediction approaches. As a sample approach for modeling protein interactions, PRISM is detailed which combines structural similarity and evolutionary conservation in protein interfaces to infer structures of complexes in the protein interaction network. This will ultimately help us to understand the role of protein interfaces in predicting bound conformations.

Chemical genetics

Chemical genetics can be defined as the study of biological systems using small molecule tools. Cell permeable and selective small molecules modulate gene product function rapidly, reversibly and can be administered conditionally in either a cellular or organismal context. The small molecule approach provides exacting temporal and quantitative control and is therefore an extremely powerful tool for dissecting biological processes. This tutorial review has been written to introduce the subject to a broad audience and highlights recent developments within the field in four key areas of biology: modulating protein-protein interactions, malaria research, hepatitis C virus research, and disrupting RNA interference pathways.

Small molecule inhibitors of amyloid β peptide aggregation as a potential therapeutic strategy for Alzheimer's disease

Amyloid β (Aβ) peptides have long been viewed as a potential target for Alzheimer's disease (AD). Aggregation of Aβ peptides in the brain tissue is believed to be an exclusively pathological process. Therefore, blocking the initial stages of Aβ peptide aggregation with small molecules could hold considerable promise as the starting point for the development of new therapies for AD. Recent rapid progresses in our understanding of toxic amyloid assembly provide a fresh impetus for this interesting approach. Here, we discuss the problems, challenges and new concepts in targeting Aβ peptides.

Helix-mediated protein--protein interactions as targets for intervention using foldamers

Protein--protein interactions (PPIs) play a central role in virtually all biological processes and have been the focus of intense investigation from structural molecular biology to cell biology for the majority of the last two decades and, more recently, are emerging as important targets for pharmaceutical intervention. A common motif found at the interface of PPIs is the α-helix, suggesting that, in the same way as the "lock and key" model has evolved for competitive inhibition of enzymes, it should be possible to elaborate "rule-based" approaches for inhibition of helix-mediated PPIs. This review will describe the biological function and structural features of a series of representative helix-mediated PPIs and discuss approaches that are being developed to target these interactions with small molecules that employ non-natural amino acids.

Topology of protein interaction network shapes protein abundances and strengths of their functional and nonspecific interactions

How do living cells achieve sufficient abundances of functional protein complexes while minimizing promiscuous nonfunctional interactions? Here we study this problem using a first-principle model of the cell whose phenotypic traits are directly determined from its genome through biophysical properties of protein structures and binding interactions in a crowded cellular environment. The model cell includes three independent prototypical pathways, whose topologies of protein-protein interaction (PPI) subnetworks are different, but whose contributions to the cell fitness are equal. Model cells evolve through genotypic mutations and phenotypic protein copy number variations. We found a strong relationship between evolved physical-chemical properties of protein interactions and their abundances due to a "frustration" effect: Strengthening of functional interactions brings about hydrophobic interfaces, which make proteins prone to promiscuous binding. The balancing act is achieved by lowering concentrations of hub proteins while raising solubilities and abundances of functional monomers. On the basis of these principles we generated and analyzed a possible realization of the proteome-wide PPI network in yeast. In this simulation we found that high-throughput affinity capture-mass spectroscopy experiments can detect functional interactions with high fidelity only for high-abundance proteins while missing most interactions for low-abundance proteins.