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

Solvent properties governing protein partitioning in polymer/polymer aqueous two-phase systems

Distribution coefficients of various proteins were measured in aqueous Dextran-Ficoll, Dextran-PES, and Ficoll-PES two-phase systems, containing 0.15M NaCl in 0.01 M phosphate buffer, pH 7.4. The acquired data were combined with data for the same proteins in different systems reported previously and known solvatochromic solvent properties of the systems to characterize the protein-solvent interactions. The relative susceptibilities of proteins to solvent dipolarity/polarizability, solvent hydrogen bond acidity, solvent hydrogen bond basicity, and solvent ability to participate in ion-ion and ion-dipole interactions were characterized. These parameters, which are representative of solute-solvent interactions, adequately described the partitioning of the proteins in each system. It was found that the relative susceptibilities of proteins to solvent dipolarity/polarizability are interrelated with their relative susceptibilities to solvent hydrogen bond acidity and solvent hydrogen bond basicity similarly to those established previously for small nonionic organic compounds.

Protein homomers in point-group assembly: symmetry making and breaking are specific and distinctive in their codes of chemical alphabet in side chains

Oligomerizing to point-group symmetry, protein oligomers need to have the symmetry broken for biologically crucial functions, such as, allosteric regulation, enzyme catalysis, and so forth. In the making of symmetry, based on self assembly, and the breaking of symmetry, based on intermolecular interactions, proteins may manifest, like their other functions, specific scripts over the coding alphabet in side chains. To address the possibility, we analyzed 82 protein homodimers in their C(2)-symmetry-related side chains across noncrystallographic interfaces, to know if they may be identical or distinct in conformation, and thus conserved or broken in symmetry. We find the propensity to conformational mismatch across interfaces correlated with side-chain chemical structure, low to very low in aromatic Trp, Tyr, His, Phe, and Arg, and high to very high in aliphatic Val, Pro, Met, Glu, Ser, Lys, Gln, Asn, and Asp, related not to polarity but, interestingly, to aromaticity of the structure. The organizational plan having aromatics embedded in a hub of aliphatic-nonpolar groups and a surrounding rim of aliphatic-polar groups, called "hotspot," has been known to direct protein-protein interaction. Finding conformational-mismatch propensities of side chains congruous with their specific chemical roles in protein-protein interaction, we propose that aromatic side chains will drive protein homomers to high symmetry, while polar- and nonpolar aliphatic side chains will drive them to the functionally-necessitated breaks of symmetry. Side chains are in their roles as protein-coding alphabet illuminated in the physics, which is discussed.

Intrinsically disordered proteins in a physics-based world

Intrinsically disordered proteins (IDPs) are a newly recognized class of functional proteins that rely on a lack of stable structure for function. They are highly prevalent in biology, play fundamental roles, and are extensively involved in human diseases. For signaling and regulation, IDPs often fold into stable structures upon binding to specific targets. The mechanisms of these coupled binding and folding processes are of significant importance because they underlie the organization of regulatory networks that dictate various aspects of cellular decision-making. This review first discusses the challenge in detailed experimental characterization of these heterogeneous and dynamics proteins and the unique and exciting opportunity for physics-based modeling to make crucial contributions, and then summarizes key lessons from recent de novo simulations of the structure and interactions of several regulatory IDPs.

Systematic classification and analysis of themes in protein-DNA recognition

Protein-DNA recognition plays a central role in the regulation of gene expression. With the rapidly increasing number of protein-DNA complex structures available at atomic resolution in recent years, a systematic, complete, and intuitive framework to clarify the intrinsic relationship between the global binding modes of these complexes is needed. In this work, we modified, extended, and applied previously defined RNA-recognition themes to describe protein-DNA recognition and used a protocol that incorporates automatic methods into manual inspection to plant a comprehensive classification tree for currently available high-quality protein-DNA structures. Further, a nonredundant (representative) data set consisting of 200 thematically diverse complexes was extracted from the leaves of the classification tree by using a locally sensitive interface comparison algorithm. On the basis of the representative data set, various physical and chemical properties associated with protein-DNA interactions were analyzed using empirical or semiempirical methods. We also examined the individual energetic components involved in protein-DNA interactions and highlighted the importance of conformational entropy, which has been almost completely ignored in previous studies of protein-DNA binding energy.

Structural space of protein-protein interfaces is degenerate, close to complete, and highly connected

At the heart of protein-protein interactions are protein-protein interfaces where the direct physical interactions occur. By developing and applying an efficient structural alignment method, we study the structural similarity of representative protein-protein interfaces involving interactions between dimers. Even without structural similarity between individual monomers that form dimeric complexes, ∼90% of native interfaces have a close structural neighbor with similar backbone C(α) geometry and interfacial contact pattern. About 80% of the interfaces form a dense network, where any two interfaces are structurally related using a transitive set of at most seven intermediate interfaces. The degeneracy of interface space is largely due to the packing of compact, hydrogen-bonded secondary structure elements. This packing generates relatively flat interacting surfaces whose geometries are highly degenerate. Comparative study of artificial and native interfaces argues that the library of protein interfaces is close to complete and comprised of roughly 1,000 distinct interface types. In contrast, the number of possible quaternary structures of dimers is estimated to be about 10(4) times larger; thus, an experimentally determined database of all representative quaternary structures is not likely in the near future. Nevertheless, one could in principle exploit the completeness of protein interfaces to predict most dimeric quaternary structures. Finally, our results provide a structural explanation for the prevalence of promiscuous protein interactions. By side-chain packing adjustments, we illustrate how multiprotein specificity can be attained at a promiscuous interface.

Domain distribution and intrinsic disorder in hubs in the human protein-protein interaction network

Intrinsic disorder and distributed surface charge have been previously identified as some of the characteristics that differentiate hubs (proteins with a large number of interactions) from non-hubs in protein-protein interaction networks. In this study, we investigated the differences in the quantity, diversity, and functional nature of Pfam domains, and their relationship with intrinsic disorder, in hubs and non-hubs. We found that proteins with a more diverse domain composition were over-represented in hubs when compared with non-hubs, with the number of interactions in hubs increasing with domain diversity. Conversely, the fraction of intrinsic disorder in hubs decreased with increasing number of ordered domains. The difference in the levels of disorder was more prominent in hubs and non-hubs with fewer domains. Functional analysis showed that hubs were enriched in kinase and adaptor domains acting primarily in signal transduction and transcription regulation, whereas non-hubs had more DNA-binding domains and were involved in catalytic activity. Consistent with the differences in the functional nature of their domains, hubs with two or more domains were more likely to connect distinct functional modules in the interaction network when compared with single domain hubs. We conclude that the availability of greater number and diversity of ordered domains, in addition to the tendency to have promiscuous domains, differentiates hubs from non-hubs and provides an additional means of achieving interaction promiscuity. Further, hubs with fewer domains use greater levels of intrinsic disorder to facilitate interaction promiscuity with the prevalence of disorder decreasing with increasing number of ordered domains.

A holistic molecular docking approach for predicting protein-protein complex structure

A holistic protein-protein molecular docking approach, HoDock, was established, composed of such steps as binding site prediction, initial complex structure sampling, refined complex structure sampling, structure clustering, scoring and final structure selection. This article explains the detailed steps and applications for CAPRI Target 39. The CAPRI result showed that three predicted binding site residues, A191HIS, B512ARG and B531ARG, were correct, and there were five submitted structures with a high fraction of correct receptor-ligand interface residues, indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.

Backbone flexibility controls the activity and specificity of a protein-protein interface: specificity in snake venom metalloproteases

Protein-protein interfaces have crucial functions in many biological processes. The large interaction areas of such interfaces show complex interaction motifs. Even more challenging is the understanding of (multi)specificity in protein-protein binding. Many proteins can bind several partners to mediate their function. A perfect paradigm to study such multispecific protein-protein interfaces are snake venom metalloproteases (SVMPs). Inherently, they bind to a variety of basement membrane proteins of capillaries, hydrolyze them, and induce profuse bleeding. However, despite having a high sequence homology, some SVMPs show a strong hemorrhagic activity, while others are (almost) inactive. We present computer simulations indicating that the activity to induce hemorrhage, and thus the capability to bind the potential reaction partners, is related to the backbone flexibility in a certain surface region. A subtle interplay between flexibility and rigidity of two loops seems to be the prerequisite for the proteins to carry out their damaging function. Presumably, a significant alteration in the backbone dynamics makes the difference between SVMPs that induce hemorrhage and the inactive ones.

Predicting interaction sites from the energetics of isolated proteins: a new approach to epitope mapping

An increasing number of functional studies of proteins have shown that sequence and structural similarities alone may not be sufficient for reliable prediction of their interaction properties. This is particularly true for proteins recognizing specific antibodies, where the prediction of antibody-binding sites, called epitopes, has proven challenging. The antibody-binding properties of an antigen depend on its structure and related dynamics. Aiming to predict the antibody-binding regions of a protein, we investigate a new approach based on the integrated analysis of the dynamical and energetic properties of antigens, to identify nonoptimized, low-intensity energetic interaction networks in the protein structure isolated in solution. The method is based on the idea that recognition sites may correspond to localized regions with low-intensity energetic couplings with the rest of the protein, which allows them to undergo conformational changes, to be recognized by a binding partner, and to tolerate mutations with minimal energetic expense. Upon analyzing the results on isolated proteins and benchmarking against antibody complexes, it is found that the method successfully identifies binding sites located on the protein surface that are accessible to putative binding partners. The combination of dynamics and energetics can thus discriminate between epitopes and other substructures based only on physical properties. We discuss implications for vaccine design.

iAlign: a method for the structural comparison of protein-protein interfaces

MOTIVATION

Protein-protein interactions play an essential role in many cellular processes. The rapid accumulation of protein-protein complex structures provides an unprecedented opportunity for comparative studies of protein-protein interactions. To facilitate such studies, it is necessary to develop an accurate and efficient computational algorithm for the comparison of protein-protein interaction modes. While there are many structural comparison approaches developed for individual proteins, very few methods are available for protein-protein complexes.

RESULTS

We present a novel interface alignment method, iAlign, for the structural alignment of protein-protein interfaces. New scoring schemes for measuring interface similarity are introduced, and an iterative dynamic programming algorithm is implemented. We find that the similarity scores follow extreme value distributions. Using statistical models, we empirically estimate their statistical significance, which is in good agreement with manual classifications by human experts. Large-scale tests of iAlign were conducted on both artificial docking models and experimental structures. In a benchmark test on 1517 dimers, iAlign successfully detects biologically related, structurally similar protein-protein interfaces at a coverage percentage of 90% and an error per query of 0.05. When compared against previously published methods, iAlign is substantially more accurate and efficient.

AVAILABILITY

The iAlign software package is freely available at http://cssb.biology.gatech.edu/iAlign.

Interaction prediction and classification of PDZ domains

Background

PDZ domain is a well-conserved, structural protein domain found in hundreds of signaling proteins that are otherwise unrelated. PDZ domains can bind to the C-terminal peptides of different proteins and act as glue, clustering different protein complexes together, targeting specific proteins and routing these proteins in signaling pathways. These domains are classified into classes I, II and III, depending on their binding partners and the nature of bonds formed. Binding specificities of PDZ domains are very crucial in order to understand the complexity of signaling pathways. It is still an open question how these domains recognize and bind their partners.

Results

The focus of the current study is two folds: 1) predicting to which peptides a PDZ domain will bind and 2) classification of PDZ domains, as Class I, II or I-II, given the primary sequences of the PDZ domains. Trigram and bigram amino acid frequencies are used as features in machine learning methods. Using 85 PDZ domains and 181 peptides, our model reaches high prediction accuracy (91.4%) for binary interaction prediction which outperforms previously investigated similar methods. Also, we can predict classes of PDZ domains with an accuracy of 90.7%. We propose three critical amino acid sequence motifs that could have important roles on specificity pattern of PDZ domains.

Conclusions

Our model on PDZ interaction dataset shows that our approach produces encouraging results. The method can be further used as a virtual screening technique to reduce the search space for putative candidate target proteins and drug-like molecules of PDZ domains.

Energy based approach for understanding the recognition mechanism in protein-protein complexes

Protein-protein interactions play an essential role in the regulation of various cellular processes. Understanding the recognition mechanism of protein-protein complexes is a challenging task in molecular and computational biology. In this work, we have developed an energy based approach for identifying the binding sites and important residues for binding in protein-protein complexes. The new approach is different from the traditional distance based contacts in which the repulsive interactions are treated as binding sites as well as the contacts within a specific cutoff have been treated in the same way. We found that the residues and residue-pairs with charged and aromatic side chains are important for binding. These residues influence to form cation-, electrostatic and aromatic interactions. Our observation has been verified with the experimental binding specificity of protein-protein complexes and found good agreement with experiments. Based on these results we have proposed a novel mechanism for the recognition of protein-protein complexes: the charged and aromatic residues in receptor and ligand initiate recognition by making suitable interactions between them; the neighboring hydrophobic residues assist the stability of complex along with other hydrogen bonding partners by the polar residues. Further, the propensity of residues in the binding sites of receptors and ligands, atomic contributions and the influence on secondary structure will be discussed.

HotPoint: hot spot prediction server for protein interfaces

The energy distribution along the protein–protein interface is not homogenous; certain residues contribute more to the binding free energy, called ‘hot spots’. Here, we present a web server, HotPoint, which predicts hot spots in protein interfaces using an empirical model. The empirical model incorporates a few simple rules consisting of occlusion from solvent and total knowledge-based pair potentials of residues. The prediction model is computationally efficient and achieves high accuracy of 70%. The input to the HotPoint server is a protein complex and two chain identifiers that form an interface. The server provides the hot spot prediction results, a table of residue properties and an interactive 3D visualization of the complex with hot spots highlighted. Results are also downloadable as text files. This web server can be used for analysis of any protein–protein interface which can be utilized by researchers working on binding sites characterization and rational design of small molecules for protein interactions. HotPoint is accessible at http://prism.ccbb.ku.edu.tr/hotpoint.

Geometric similarities of protein-protein interfaces at atomic resolution are only observed within homologous families: an exhaustive structural classification study

To elucidate the structural basis of the diversity and universality in protein-protein interactions, an exhaustive all-against-all structural comparison of all known protein interfaces in the Protein Data Bank was performed at atomic resolution. After similar interfaces were clustered, approximately 20,000 structural motifs with at least two members were identified, out of which 3678 motifs consisted of at least 10 interfaces. Except for some trivial interfaces involving single alpha helices, almost all motifs were found to be confined within single protein families. Furthermore, the interaction partners of each motif were found to be very limited, and, accordingly, the interaction networks of the motifs tend to be small and are much more restricted than the binding sites for small ligand molecules. These findings suggest that, at the level of atomic structures, protein-protein interactions are precisely designed; hence, protein interfaces with multiple interacting partners should involve incompletely overlapping multiple interfaces and/or accommodate structural changes upon binding to their targets.

Enhancements of screening collections to address areas of unmet medical need: an industry perspective

The past 20 years have witnessed an impressive expansion of the 'drug space'; defined as the intersection of the Medicinal Chemistry space and the Biologically Active space relevant in the quest for new treatments for disease. Despite the success of known lead discovery tactics, areas of unmet medical need are often linked to challenging or novel targets and are poorly served by current screening collections. A successful strategy to fill the gaps is to diversify the approaches taken in the enhancement of screening collections. Possible strategies include investments through proven methods, exploring areas of chemical space previously neglected (e.g. hydrophilic compounds, natural product mimics), and applying tactics to the lead discovery process that are complementary to HTS (e.g. fragment based screening or multidisciplinary team efforts to tackle new target classes).

The 19-amino acid insertion in the tumor-associated splice isoform Rac1b confers specific binding to p120 catenin

The Rac1b splice isoform contains a 19-amino acid insertion not found in Rac1; this insertion leads to decreased GTPase activity and reduced affinity for GDP, resulting in the intracellular predominance of GTP-bound Rac1b. Here, using co-precipitation and proteomic methods, we find that Rac1b does not bind to many common regulators of Rho family GTPases but that it does display enhanced binding to SmgGDS, RACK1, and p120 catenin (p120(ctn)), proteins involved in cell-cell adhesion, motility, and transcriptional regulation. We use molecular modeling and structure analysis approaches to determine that the interaction between Rac1b and p120(ctn) is dependent upon protein regions that are predicted to be unstructured in the absence of molecular complex formation, suggesting that the interaction between these two proteins involves coupled folding and binding. We also find that directed cell movement initiated by Rac1b is dependent upon p120. These results define a distinct binding functionality of Rac1b and provide insight into how the distinct phenotypic program activated by this protein may be implemented through molecular recognition of effectors distinct from those of Rac1.

Designing focused chemical libraries enriched in protein-protein interaction inhibitors using machine-learning methods

Protein-protein interactions (PPIs) may represent one of the next major classes of therapeutic targets. So far, only a minute fraction of the estimated 650,000 PPIs that comprise the human interactome are known with a tiny number of complexes being drugged. Such intricate biological systems cannot be cost-efficiently tackled using conventional high-throughput screening methods. Rather, time has come for designing new strategies that will maximize the chance for hit identification through a rationalization of the PPI inhibitor chemical space and the design of PPI-focused compound libraries (global or target-specific). Here, we train machine-learning-based models, mainly decision trees, using a dataset of known PPI inhibitors and of regular drugs in order to determine a global physico-chemical profile for putative PPI inhibitors. This statistical analysis unravels two important molecular descriptors for PPI inhibitors characterizing specific molecular shapes and the presence of a privileged number of aromatic bonds. The best model has been transposed into a computer program, PPI-HitProfiler, that can output from any drug-like compound collection a focused chemical library enriched in putative PPI inhibitors. Our PPI inhibitor profiler is challenged on the experimental screening results of 11 different PPIs among which the p53/MDM2 interaction screened within our own CDithem platform, that in addition to the validation of our concept led to the identification of 4 novel p53/MDM2 inhibitors. Collectively, our tool shows a robust behavior on the 11 experimental datasets by correctly profiling 70% of the experimentally identified hits while removing 52% of the inactive compounds from the initial compound collections. We strongly believe that this new tool can be used as a global PPI inhibitor profiler prior to screening assays to reduce the size of the compound collections to be experimentally screened while keeping most of the true PPI inhibitors. PPI-HitProfiler is freely available on request from our CDithem platform website, www.CDithem.com.

Geometric similarity between protein-RNA interfaces

A new method is described to measure the geometric similarity between protein-RNA interfaces quantitatively. The method is based on a procedure that dissects the interface geometry in terms of the spatial relationships between individual amino acid nucleotide pairs. Using this technique, we performed an all-on-all comparison of 586 protein-RNA interfaces deposited in the current Protein Data Bank, as the result, an interface-interface similarity score matrix was obtained. Based upon this matrix, hierarchical clustering was carried out which yielded a complete clustering tree for the 586 protein-RNA interfaces. By investigating the organizing behavior of the clustering tree and the SCOP classification of protein partners in complexes, a geometrically nonredundant, diverse data set (representative data set) consisting of 45 distinct protein-RNA interfaces was extracted for the purpose of studying protein-RNA interactions, RNA regulations, and drug design. We classified protein-RNA interfaces into three types. In type I, the families and interface structural classes of the protein partners, as well as the interface geometries are all similar. In type II, the interface geometries and the interface structural classes are similar, whereas the protein families are different. In type III, only the interface geometries are similar but the protein families and the interface structural classes are distinct. Furthermore, we also show two new RNA recognition themes derived from the representative data set.

Structure of the preamyloid dimer of beta-2-microglobulin from covalent labeling and mass spectrometry

Beta-2-microglobulin (beta2m) self-associates into fibrillar amyloid deposits in the musculoskeletal system of patients undergoing hemodialysis treatment. Previous studies have shown that stoichiometric amounts of Cu(II) at near physiological conditions can cause beta2m to organize into native-like dimers prior to forming amyloid fibrils. Here, we report the results from selective covalent labeling reactions combined with mass spectrometry that provide insight into the amino acid residues that mediate dimer formation in the wild-type protein. Using three complementary covalent labeling reagents, we find that the dimer interface is formed by the antiparallel stacking of ABED beta-sheets from two beta2m monomers. In addition, our data clearly indicate that a dimer interface involving the interactions of D-D strands from separate protein units as seen in the recent crystal structures of two mutant beta2m oligomers is unlikely.

Development of a Novel Bioinformatics Tool for In Silico Validation of Protein Interactions

Protein interactions are crucial in most biological processes. Several in silico methods have been recently developed to predict them. This paper describes a bioinformatics method that combines sequence similarity and structural information to support experimental studies on protein interactions. Given a target protein, the approach selects the most likely interactors among the candidates revealed by experimental techniques, but not yet in vivo validated. The sequence and the structural information of the in vivo confirmed proteins and complexes are exploited to evaluate the candidate interactors. Finally, a score is calculated to suggest the most likely interactors of the target protein. As an example, we searched for GRB2 interactors. We ranked a set of 46 candidate interactors by the presented method. These candidates were then reduced to 21, through a score threshold chosen by means of a cross-validation strategy. Among them, the isoform 1 of MAPK14 was in silico confirmed as a GRB2 interactor. Finally, given a set of already confirmed interactors of GRB2, the accuracy and the precision of the approach were 75% and 86%, respectively. In conclusion, the proposed method can be conveniently exploited to select the proteins to be experimentally investigated within a set of potential interactors.

Human cancer protein-protein interaction network: a structural perspective

Protein-protein interaction networks provide a global picture of cellular function and biological processes. Some proteins act as hub proteins, highly connected to others, whereas some others have few interactions. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. Similar or overlapping binding sites should be used repeatedly in single interface hub proteins, making them promiscuous. Alternatively, multi-interface hub proteins make use of several distinct binding sites to bind to different partners. We propose a methodology to integrate protein interfaces into cancer interaction networks (ciSPIN, cancer structural protein interface network). The interactions in the human protein interaction network are replaced by interfaces, coming from either known or predicted complexes. We provide a detailed analysis of cancer related human protein-protein interfaces and the topological properties of the cancer network. The results reveal that cancer-related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer proteins, which may indicate low affinity and high specificity of the cancer-related interactions. We also classified the genes in ciSPIN according to phenotypes. Within phenotypes, for breast cancer, colorectal cancer and leukemia, interface properties were found to be discriminating from non-cancer interfaces with an accuracy of 71%, 67%, 61%, respectively. In addition, cancer-related proteins tend to interact with their partners through distinct interfaces, corresponding mostly to multi-interface hubs, which comprise 56% of cancer-related proteins, and constituting the nodes with higher essentiality in the network (76%). We illustrate the interface related affinity properties of two cancer-related hub proteins: Erbb3, a multi interface, and Raf1, a single interface hub. The results reveal that affinity of interactions of the multi-interface hub tends to be higher than that of the single-interface hub. These findings might be important in obtaining new targets in cancer as well as finding the details of specific binding regions of putative cancer drug candidates.

Protein-protein interaction networks provide a global picture of cellular function and biological processes. The dysfunction of some interactions causes many diseases, including cancer. Proteins interact through their interfaces. Therefore, studying the interface properties of cancer-related proteins will help explain their role in the interaction networks. The structural details of interfaces are immensely useful in efforts to answer some fundamental questions such as: (i) what features of cancer-related protein interfaces make them act as hubs; (ii) how hub protein interfaces can interact with tens of other proteins with varying affinities; and (iii) which interactions can occur simultaneously and which are mutually exclusive. Addressing these questions, we propose a method to characterize interactions in a human protein-protein interaction network using three-dimensional protein structures and interfaces. Protein interface analysis shows that the strength and specificity of the interactions of hub proteins and cancer proteins are different than the interactions of non-hub and non-cancer proteins, respectively. In addition, distinguishing overlapping from non-overlapping interfaces, we illustrate how a fourth dimension, that of the sequence of processes, is integrated into the network with case studies. We believe that such an approach should be useful in structural systems biology.

Modulation of p53 binding to MDM2: computational studies reveal important roles of Tyr100

Background

The tumor suppressor protein p53 is regulated by the ubiquitin ligase MDM2 which down-regulates p53. In tumours with overexpressed MDM2, the p53-MDM2 interaction can be interrupted by a peptide or small molecule to stabilize p53 as a therapeutic strategy. Structural and biochemical/mutagenesis data show that p53 has 3 hydrophobic residues F19, W23 and L26 that embed into the ligand binding pocket of MDM2 which is highly plastic in nature and can modulate its size to accommodate a variety of ligands. This binding pocket is primarily dependent on the orientation of a particular residue, Y100. We have studied the role of the dynamics of Y100 in p53 recognition.

Results

Molecular dynamics simulations show that the Y100 side chain can be in "open" or "closed" states with only the former enabling complex formation. When both p53 and MDM2 are in near native conformations, complex formation is rapid and is driven by the formation of a hydrogen bond between W23 of p53 and L54 of MDM2 or by the embedding of F19 of p53 into MDM2. The transition of Y100 from "closed" to "open" can increase the size of the binding site. Interconversions between these two states can be induced by the N-terminal region of MDM2 or by the conformations of the p53 peptides.

Conclusion

Molecular dynamics simulations have revealed how the binding of p53 to MDM2 is modulated by the conformational mobility of Y100 which is the gatekeeper residue in MDM2. The mobility of this residue can be modulated by the conformations of p53 and the Nterminal lid region of MDM2.

Protein-protein interaction networks: how can a hub protein bind so many different partners?

How can a single hub protein bind so many different partners? Numerous studies have sought differences between hubs and non-hubs to explain what makes a protein a hub and how a shared hub-binding site can be promiscuous, yet at the same time be specific. Here, we suggest that the problem is largely non-existent and resides in the popular representation of protein interaction networks: protein products derived from a single gene, even if different, are clustered in maps into a single node. This leads to the impression that a single protein binds to a very large number of partners. In reality, it does not; rather, protein networks reflect the combination of multiple proteins, each with a distinct conformation.

Classical swine fever virus NS3 enhances RNA-dependent RNA polymerase activity by binding to NS5B

NS3 of pestiviruses contains a protease domain and a RNA helicase/NTPase domain. Contradictory results have been reported regarding NS3 in RNA synthesis. To investigate the effect of NS3 on classical swine fever virus (CSFV) NS5B RNA-dependent RNA polymerase activity (RdRp) activity and NS3-NS5B interaction, RdRp reactions, GST-pull-down assays and co-immunoprecipitation analyses containing NS5B and either of NS3 protein and the different truncated NS3 mutants were performed, respectively. We found that NS3 stimulated NS5B RdRp activity in a dose-dependent manner by binding to NS5 through a NS3 protease domain. Furthermore, mapping important regions of the NS3 protease domain was carried out by deletion mutagenesis, associated with RdRp reactions, GST-pull-down assays and co-immunoprecipitation analyses. Results showed that stimulation of CSFV NS5B RdRp activity was obtained by NS3 binding to NS5B through a 31-amino acid fragment at the N-terminal end of NS3 protease domain, which mediated a specific NS3-NS5B interaction.

Copper binding to beta-2-microglobulin and its pre-amyloid oligomers

Beta-2-microglobulin (beta2m) deposits as amyloid fibrils in the musculoskeletal system of patients undergoing long-term dialysis treatment as a result of kidney failure. Previous work has shown that Cu(II) binding causes beta2m to organize into nativelike dimers and tetramers that precede amyloid formation. Cu(II) is then released from higher-order oligomers before mature Cu(II)-free amyloid fibrils are formed. While some of the Cu(II)-induced structural changes that enable beta2m self-assembly are starting to be revealed, the details of how the Cu(II) binding site evolves from the monomer to the dimers and tetramers are not known. Here, we report results from three mass spectrometry (MS)-based methods that provide insight into the changing Cu-beta2m interactions. We find that monomeric beta2m binds Cu(II) via the N-terminal amine, the amide of Gln2, His31, and Asp59. In the dimer and tetramer, Asp59 is no longer bound to Cu(II), but the other residues still comprise a well-defined albeit weaker binding site that is better able to release Cu(II). Consistent with this is the observation that a fraction of the tetrameric species no longer binds Cu(II) at this weakened binding site, which agrees with a previous report that suggested the tetramer as the first Cu(II)-free oligomer. Our results also provide some insight into structural changes caused by Cu(II) binding that facilitate oligomer formation. Specifically, binding by Asp59 in the monomer requires significant movement of this residue, and we propose that this repositioning is important for establishing a pair of dimer-stabilizing salt bridges between this residue and Lys19. We also find evidence that Cu(II) binding in the N-terminal region of the monomer repels Arg3, which likely allows this residue to form a pair of dimer-stabilizing salt bridges with Glu16. Overall, our measurements suggest that the previously proposed conformational switch caused by Cu(II) binding includes not only a cis-trans isomerization at Pro32 but also the repositioning of residues that are critical for the formation of new electrostatic interactions.

From evidence to inference: probing the evolution of protein interaction networks

The evolutionary mechanisms by which protein interaction networks grow and change are beginning to be appreciated as a major factor shaping their present-day structures and properties. Starting with a consideration of the biases and errors inherent in our current views of these networks, we discuss the dangers of constructing evolutionary arguments from naïve analyses of network topology. We argue that progress in understanding the processes of network evolution is only possible when hypotheses are formulated as plausible evolutionary models and compared against the observed data within the framework of probabilistic modeling. The value of such models is expected to be greatly enhanced as they incorporate more of the details of the biophysical properties of interacting proteins, gene phylogeny, and measurement error and as more advanced methodologies emerge for model comparison and the inference of ancestral network states.

Structural adaptation of serine hydroxymethyltransferase to low temperatures

Structural adaptation of serine hydroxymethyltransferase (SHMT), a pyridoxal-5'-phosphate dependent enzyme that catalyzes the reversible conversion of l-serine and tetrahydropteroylglutamate to glycine and 5,10-methylene-tetrahydropteroylglutamate, synthesized by microorganisms adapted to low temperatures has been analyzed using a comparative approach. The variations of amino acid properties and frequencies among three temperature populations (psychrophilic, mesophilic, hyper- and thermophilic) of SHMT sequences have been tested. SHMTs display a general increase of polarity specially in the core, a more negatively charged surface, and enhanced flexibility. Subunit interface is more hydrophilic and less compact. Electrostatic potential of the tetrahydrofolate binding site has been compared. The enzyme from Psychromonas ingrahamii, the organism with the lowest adaptation temperatures, displayed the most positive potential. In general, the property variations show a coherent opposite trend in the hyperthermophilic population: in particular, increase of hydrophobicity, packing and decrease of flexibility was observed.

Regression applied to protein binding site prediction and comparison with classification

Background

The structural genomics centers provide hundreds of protein structures of unknown function. Therefore, developing methods enabling the determination of a protein function automatically is imperative. The determination of a protein function can be achieved by studying the network of its physical interactions. In this context, identifying a potential binding site between proteins is of primary interest. In the literature, methods for predicting a potential binding site location generally are based on classification tools. The aim of this paper is to show that regression tools are more efficient than classification tools for patches based binding site predictors. For this purpose, we developed a patches based binding site localization method usable with either regression or classification tools.

Results

We compared predictive performances of regression tools with performances of machine learning classifiers. Using leave-one-out cross-validation, we showed that regression tools provide better predictions than classification ones. Among regression tools, Multilayer Perceptron ranked highest in the quality of predictions. We compared also the predictive performance of our patches based method using Multilayer Perceptron with the performance of three other methods usable through a web server. Our method performed similarly to the other methods.

Conclusion

Regression is more efficient than classification when applied to our binding site localization method. When it is possible, using regression instead of classification for other existing binding site predictors will probably improve results. Furthermore, the method presented in this work is flexible because the size of the predicted binding site is adjustable. This adaptability is useful when either false positive or negative rates have to be limited.

Thermodynamic characterization of two homologous protein complexes: associations of the semaphorin receptor plexin-B1 RhoGTPase binding domain with Rnd1 and active Rac1

Plexin receptors function in response to semaphorin guidance cues in a variety of developmental processes involving cell motility. Interactions with Rho, as well as Ras family small GTPases are critical events in the cell signaling mechanism. We have recently determined the structure of a cytoplasmic domain (RBD) of plexin-B1 and mapped its binding interface with several Rho-GTPases, Rac1, Rnd1, and RhoD. All three GTPases associate with a similar region of this plexin domain, but show different functional behavior in cells. To understand whether thermodynamic properties of the GTPase-RBD interaction contribute to such different behavior, we have examined the interaction at different temperatures, buffer, and pH conditions. Although the binding affinity of both Rnd1 and Rac1 with the plexin-B1 RBD is similar, the detailed thermodynamic properties of the interactions are considerably different. These data suggest that on Rac1 binding to the plexin-B1 RBD, the proteins become more rigid in the complex. By contrast, Rnd1 binding is consistent with unchanged or slightly increased flexibility in one or both proteins. Both GTPases show an appreciable reduction in affinity for the dimeric plexin-B1 RBD indicating that GTPase binding is not cooperative with dimer formation, but that a partial steric hindrance destabilizes the dimer. However, a reduced affinity binding mode to a disulphide stabilized model for the dimeric RBD is also possible. Consistent with cellular studies, the interaction thermodynamics imply that further levels of regulation involving additional binding partners and/or regions outside of the RhoGTPase binding domain are required for receptor activation.

Knitting and untying the protein network: modulation of protein ensembles as a therapeutic strategy

Proteins constitute the working machinery and structural support of all organisms. In performing a given function, they must adopt highly specific structures that can change with their level of activity, often through the direct or indirect action of other proteins. Indeed, proteins typically function within an ensemble, rather than individually. Hence, they must be sufficiently flexible to interact with each other and execute diverse tasks. The discovery that errors within these groups can ultimately cause disease has led to a paradigm shift in drug discovery, from an emphasis on single protein targets to a holistic approach whereby entire ensembles are targeted.

Incorporating receptor flexibility in the molecular design of protein interfaces

The success of antibody-based pharmaceuticals has led to a resurgence in interest in computational structure-based design. Most efforts to date have been on the redesign of existing interfaces. These efforts have mostly neglected the inherent flexibility of the receptor that is critical for binding. In this work, we extend on a previous study to perform a series of designs of protein binding interfaces by incorporating receptor flexibility using an ensemble of conformers collected from explicit-solvent molecular dynamics (MD) simulations. All designer complexes are subjected to 30 ns of MD in explicit solvent to assess for stability for a total of 480 ns of dynamics. This is followed by end-point free energy calculations whereby intermolecular potential energy, polar and non-polar solvation energy and entropy of ligand and receptor are subtracted from that of the complex and averaged over 320 snapshots collected from each of the 30 ns MD simulations. Our initial effort consisted of redesigning the interface of three well-studied complexes, namely barnase-barstar, lysozyme-antibody D1.3 and trypsin-BPTI. The design was performed with flexible backbone approach. MD simulations revealed that all three complexes remained stable. Interestingly, the redesigned trypsin-BPTI complex was significantly more favorable than the native complex. This was attributed to the favorable electrostatics and entropy that complemented the already favorable non-polar component. Another aspect of this work consisted of grafting the surface of three proteins, namely tenascin, CheY and MBP1 to bind to barnase, trypsin and lysozyme. The process was initially performed using fixed backbone, and more than 300 ns of the explicit-solvent MD simulation revealed some of the complexes to dissociate over the course of the trajectories, whereas others remained stable. Free energy calculations confirmed that the non-polar component of the free energy as computed by summing the van der Waals energy and the non-polar solvation energy was a strong predictor of stability. Four complexes (two stable and two unstable) were selected, and redesigned using multiple conformers collected from the MD simulation. The resulting designer systems were then immersed in explicit solvent and 30 ns of MD was carried out on each. Interestingly, those complexes that were initially stable remained stable, whereas one of the unstable complexes became stable following redesign with flexible backbone. Free energy calculations showed significant improvements in the affinity for most complexes, revealing that the use of multiple conformers in protein design may significantly enhance such efforts.

Inhibition of protein-protein interactions using designed molecules

Although many cellular processes depend upon enzymatic reactions, protein-protein interactions (PPIs) mediate a large number of important regulatory pathways and thus play a central role in disease development. In order to understand and selectively inhibit cellular signalling pathways, there is a pressing need for small molecules that target PPIs, particularly in the context of pharmaceutical development. This tutorial review will introduce the relevance of PPIs to chemical biology and highlight the key challenges in designing inhibitors. Some of the successes using conventional approaches to the identification of small-molecule PPI inhibitors will be highlighted, and also the reasons why these approaches have not always proven successful. Several general approaches tailored to particular protein topologies are emerging for the design of scaffolds that inhibit PPIs-these will form the major content of this review. Finally a summary of the challenges to be faced in developing inhibitors of PPIs into drug leads and how these challenges may differ from those encountered with enzyme-like targets will be given.

Atomistic details of the disordered states of KID and pKID. Implications in coupled binding and folding

Intrinsically disordered proteins (IDPs) are a newly recognized class of functional proteins for which a lack of stable tertiary fold is required for function. Because of the heterogeneous and dynamical nature, molecular modeling is necessary to provide the missing details of disordered states of IDP that are crucial for understanding their functions. In particular, generalized Born (GB) implicit solvent, combined with replica exchange (REX), might offer an optimal balance between accuracy and efficiency for modeling IDPs. We carried out extensive REX simulations in an optimized GB force field to characterize the disordered states of a regulatory IDP, KID domain of transcription factor CREB, and its phosphorylated form, pKID. The results revealed that both KID and pKID, though highly disordered on the tertiary level, are compact and mainly occupy a small number of helical substates. Interestingly, although phosphorylation of KID Ser133 leads only to marginal changes in average helicities on the ensemble level, underlying conformational substates differ significantly. In particular, pSer133 appears to restrict the accessible conformational space of the loop region and thus reduces the entropic cost of KID folding upon binding to the KIX domain of CREB-binding protein. Such an expanded role of phosphorylation in the KID:KIX recognition was not previously recognized because of a lack of substantial conformational changes on the ensemble level and inaccessibility of the structural details from experiments. The results also suggest that an implicit solvent-based modeling framework, despite various existing limitations, might be feasible for accurate atomistic simulation of small IDPs in general.

Binding mechanisms in supramolecular complexes

Supramolecular chemistry has expanded dramatically in recent years both in terms of potential applications and in its relevance to analogous biological systems. The formation and function of supramolecular complexes occur through a multiplicity of often difficult to differentiate noncovalent forces. The aim of this Review is to describe the crucial interaction mechanisms in context, and thus classify the entire subject. In most cases, organic host-guest complexes have been selected as examples, but biologically relevant problems are also considered. An understanding and quantification of intermolecular interactions is of importance both for the rational planning of new supramolecular systems, including intelligent materials, as well as for developing new biologically active agents.

Evaluating docking methods for prediction of binding affinities of small molecules to the G protein betagamma subunits

Several studies have suggested that disrupting interactions of the G protein betagamma subunits with downstream binding partners might be a valuable study for pharmaceutical development. Recently, small molecules have been found which bind to Gbetagamma with high apparent affinity in an enzyme-linked immunosorbent assay (ELISA), have demonstrated selective inhibition of interactions of Gbetagamma with downstream signaling partners, and have been shown to increase antinociceptive effects of morphine and inhibit inflammation in vivo. In this paper we examine several docking and scoring protocols for estimating binding affinities for a set of 830 ligands from the NCI diversity set to the Gbeta1gamma2 subunit and compared these with IC50s measured in a competition ELISA with a high-affinity peptidic ligand. The best-performing docking protocol used a consensus score and ensemble docking and resulted in a 6-fold enrichment of high-affinity compounds in the top-ranked 5% of the ligand data set.