A novel endogenous inhibitor of the secreted streptococcal NAD-glycohydrolase

The Streptococcus pyogenes NAD-glycohydrolase (SPN) is a toxic enzyme that is introduced into infected host cells by the cytolysin-mediated translocation pathway. However, how S. pyogenes protects itself from the self-toxicity of SPN had been unknown. In this report, we describe immunity factor for SPN (IFS), a novel endogenous inhibitor that is essential for SPN expression. A small protein of 161 amino acids, IFS is localized in the bacterial cytoplasmic compartment. IFS forms a stable complex with SPN at a 1:1 molar ratio and inhibits SPN's NAD-glycohydrolase activity by acting as a competitive inhibitor of its beta-NAD+ substrate. Mutational studies revealed that the gene for IFS is essential for viability in those S. pyogenes strains that express an NAD-glycohydrolase activity. However, numerous strains contain a truncated allele of ifs that is linked to an NAD-glycohydrolase-deficient variant allele of spn. Of practical concern, IFS allowed the normally toxic SPN to be produced in the heterologous host Escherichia coli to facilitate its purification. To our knowledge, IFS is the first molecularly characterized endogenous inhibitor of a bacterial beta-NAD(+)-consuming toxin and may contribute protective functions in the streptococci to afford SPN-mediated pathogenesis.     

Structural basis of Streptococcus pyogenes immunity to its NAD+ glycohydrolase toxin

The virulence of Gram-positive bacteria is enhanced by toxins like the Streptococcus pyogenes β-NAD(+) glycohydrolase known as SPN. SPN-producing strains of S. pyogenes additionally express the protein immunity factor for SPN (IFS), which forms an inhibitory complex with SPN. We have determined crystal structures of the SPN-IFS complex and IFS alone, revealing that SPN is structurally related to ADP-ribosyl transferases but lacks the canonical binding site for protein substrates. SPN is instead a highly efficient glycohydrolase with the potential to deplete cellular levels of β-NAD(+). The protective effect of IFS involves an extensive interaction with the SPN active site that blocks access to β-NAD(+). The conformation of IFS changes upon binding to SPN, with repacking of an extended C-terminal α helix into a compact shape. IFS is an attractive target for the development of novel bacteriocidal compounds functioning by blocking the bacterium's self-immunity to the SPN toxin.     

Twin anchors of the soybean isoflavonoid metabolon: evidence for tethering of the complex to the endoplasmic reticulum by IFS and C4H

Isoflavonoids are specialized plant metabolites, almost exclusive to legumes, and their biosynthesis forms a branch of the diverse phenylpropanoid pathway. Plant metabolism may be coordinated at many levels, including formation of protein complexes, or ‘metabolons’, which represent the molecular level of organization. Here, we have confirmed the existence of the long‐postulated isoflavonoid metabolon by identifying elements of the complex, their subcellular localizations and their interactions. Isoflavone synthase (IFS) and cinnamate 4–hydroxylase (C4H) have been shown to be tandem P450 enzymes that are anchored in the ER, interacting with soluble enzymes of the phenylpropanoid and isoflavonoid pathways (chalcone synthase, chalcone reductase and chalcone isomerase). The soluble enzymes of these pathways, whether localized to the cytoplasm or nucleus, are tethered to the ER through interaction with these P450s. The complex is also held together by interactions between the soluble elements. We provide evidence for IFS interaction with upstream and non‐consecutive enzymes. The existence of such a protein complex suggests a possible mechanism for flux of metabolites into the isoflavonoid pathway. Further, through interaction studies, we identified several candidates that are associated with GmIFS2, an isoform of IFS, in soybean hairy roots. This list provides additional candidates for various biosynthetic and structural elements that are involved in isoflavonoid production. Our interaction studies provide valuable information about isoform specificity among isoflavonoid enzymes, which may guide future engineering of the pathway in legumes or help overcome bottlenecks in heterologous expression.     

Complementarity of stability patches at the interfaces of protein complexes: Implication for the structural organization of energetic hot spots

A rational design of protein complexes with defined functionalities and of drugs aimed at disrupting protein–protein interactions requires fundamental understanding of the mechanisms underlying the formation of specific protein complexes. Efforts to develop efficient small‐molecule or protein‐based binders often exploit energetic hot spots on protein surfaces, namely, the interfacial residues that provide most of the binding free energy in the complex. The molecular basis underlying the unusually high energy contribution of the hot spots remains obscure, and its elucidation would facilitate the design of interface‐targeted drugs. To study the nature of the energetic hot spots, we analyzed the backbone dynamic properties of contact surfaces in several protein complexes. We demonstrate that, in most complexes, the backbone dynamic landscapes of interacting surfaces form complementary “stability patches,” in which static areas from the opposing surfaces superimpose, and that these areas are predominantly located near the geometric center of the interface. We propose that a diminished enthalpy–entropy compensation effect augments the degree to which residues positioned within the complementary stability patches contribute to complex affinity, thereby giving rise to the energetic hot spots. These findings offer new insights into the nature of energetic hot spots and the role that backbone dynamics play in facilitating intermolecular recognition. Mapping the interfacial stability patches may provide guidance for protein engineering approaches aimed at improving the stability of protein complexes and could facilitate the design of ligands that target complex interfaces.     

Co-evolutionary analysis of domains in interacting proteins reveals insights into domain-domain interactions mediating protein-protein interactions

Recent advances in functional genomics have helped generate large-scale high-throughput protein interaction data. Such networks, though extremely valuable towards molecular level understanding of cells, do not provide any direct information about the regions (domains) in the proteins that mediate the interaction. Here, we performed co-evolutionary analysis of domains in interacting proteins in order to understand the degree of co-evolution of interacting and non-interacting domains. Using a combination of sequence and structural analysis, we analyzed protein-protein interactions in F1-ATPase, Sec23p/Sec24p, DNA-directed RNA polymerase and nuclear pore complexes, and found that interacting domain pair(s) for a given interaction exhibits higher level of co-evolution than the non-interacting domain pairs. Motivated by this finding, we developed a computational method to test the generality of the observed trend, and to predict large-scale domain-domain interactions. Given a protein-protein interaction, the proposed method predicts the domain pair(s) that is most likely to mediate the protein interaction. We applied this method on the yeast interactome to predict domain-domain interactions, and used known domain-domain interactions found in PDB crystal structures to validate our predictions. Our results show that the prediction accuracy of the proposed method is statistically significant. Comparison of our prediction results with those from two other methods reveals that only a fraction of predictions are shared by all the three methods, indicating that the proposed method can detect known interactions missed by other methods. We believe that the proposed method can be used with other methods to help identify previously unrecognized domain-domain interactions on a genome scale, and could potentially help reduce the search space for identifying interaction sites.     

Partner-aware prediction of interacting residues in protein-protein complexes from sequence data

Computational prediction of residues that participate in protein-protein interactions is a difficult task, and state of the art methods have shown only limited success in this arena. One possible problem with these methods is that they try to predict interacting residues without incorporating information about the partner protein, although it is unclear how much partner information could enhance prediction performance. To address this issue, the two following comparisons are of crucial significance: (a) comparison between the predictability of inter-protein residue pairs, i.e., predicting exactly which residue pairs interact with each other given two protein sequences; this can be achieved by either combining conventional single-protein predictions or making predictions using a new model trained directly on the residue pairs, and the performance of these two approaches may be compared: (b) comparison between the predictability of the interacting residues in a single protein (irrespective of the partner residue or protein) from conventional methods and predictions converted from the pair-wise trained model. Using these two streams of training and validation procedures and employing similar two-stage neural networks, we showed that the models trained on pair-wise contacts outperformed the partner-unaware models in predicting both interacting pairs and interacting single-protein residues. Prediction performance decreased with the size of the conformational change upon complex formation; this trend is similar to docking, even though no structural information was used in our prediction. An example application that predicts two partner-specific interfaces of a protein was shown to be effective, highlighting the potential of the proposed approach. Finally, a preliminary attempt was made to score docking decoy poses using prediction of interacting residue pairs; this analysis produced an encouraging result.     

Membrane targeting and stabilization of sarcospan is mediated by the sarcoglycan subcomplex

The dystrophin-glycoprotein complex (DGC) is a multisubunit complex that spans the muscle plasma membrane and forms a link between the F-actin cytoskeleton and the extracellular matrix. The proteins of the DGC are structurally organized into distinct subcomplexes, and genetic mutations in many individual components are manifested as muscular dystrophy. We recently identified a unique tetraspan-like dystrophin-associated protein, which we have named sarcospan (SPN) for its multiple sarcolemma spanning domains (Crosbie, R.H., J. Heighway, D.P. Venzke, J.C. Lee, and K.P. Campbell. 1997. J. Biol. Chem. 272:31221-31224). To probe molecular associations of SPN within the DGC, we investigated SPN expression in normal muscle as a baseline for comparison to SPN's expression in animal models of muscular dystrophy. We show that, in addition to its sarcolemma localization, SPN is enriched at the myotendinous junction (MTJ) and neuromuscular junction (NMJ), where it is a component of both the dystrophin- and utrophin-glycoprotein complexes. We demonstrate that SPN is preferentially associated with the sarcoglycan (SG) subcomplex, and this interaction is critical for stable localization of SPN to the sarcolemma, NMJ, and MTJ. Our experiments indicate that assembly of the SG subcomplex is a prerequisite for targeting SPN to the sarcolemma. In addition, the SG- SPN subcomplex functions to stabilize alpha-dystroglycan to the muscle plasma membrane. Taken together, our data provide important information about assembly and function of the SG-SPN subcomplex.     

Effect of pressure on helix‐coil transition of an alanine‐based peptide: An FTIR study

Protein–protein interactions are mediated by complementary amino acids defining complementary surfaces. Typically not all members of a family of related proteins interact equally well with all members of a partner family; thus analysis of the sequence record can reveal the complementary amino acid partners that confer interaction specificity. This article develops methods for learning and using probabilistic graphical models of such residue “cross‐coupling” constraints between interacting protein families, based on multiple sequence alignments and information about which pairs of proteins are known to interact. Our models generalize traditional consensus sequence binding motifs, and provide a probabilistic semantics enabling sound evaluation of the plausibility of new possible interactions. Furthermore, predictions made by the models can be explained in terms of the underlying residue interactions. Our approach supports different levels of prior knowledge regarding interactions, including both one‐to‐one (e.g., pairs of proteins from the same organism) and many‐to‐many (e.g., experimentally identified interactions), and we present a technique to account for possible bias in the represented interactions. We apply our approach in studies of PDZ domains and their ligands, fundamental building blocks in a number of protein assemblies. Our algorithms are able to identify biologically interesting cross‐coupling constraints, to successfully identify known interactions, and to make explainable predictions about novel interactions. Proteins 2009. © 2009 Wiley‐Liss, Inc.     

Investigation at residue level of the early steps during the assembly of two proteins into supramolecular objects

Understanding the driving forces governing protein assembly requires the characterization of interactions at molecular level. We focus on two homologous oppositely charged proteins, lysozyme and α-lactalbumin, which can assemble into microspheres. The assembly early steps were characterized through the identification of interacting surfaces monitored at residue level by NMR chemical shift perturbations by titrating one (15)N-labeled protein with its unlabeled partner. While α-lactalbumin has a narrow interacting site, lysozyme has interacting sites scattered on a broad surface. The further assembly of these rather unspecific heterodimers into tetramers leads to the establishment of well-defined interaction sites. Within the tetramers, most of the electrostatic charge patches on the protein surfaces are shielded. Then, hydrophobic interactions, which are possible because α-lactalbumin is in a partially folded state, become preponderant, leading to the formation of larger oligomers. This approach will be particularly useful for rationalizing the design of protein assemblies as nanoscale devices.     

Identification and mapping of protein-protein interactions by a combination of cross-linking, cleavage, and proteomics

Protein-protein interactions are vital for almost all cellular functions, and many require the formation of multiprotein complexes. Identification of the macroscopic and microscopic protein interactions within these complexes is essential in understanding their mechanisms, both under physiologic as well as pathologic conditions. This review describes the current technology available to investigate interactions between proteins utilizing chemical cross-linking and site-directed cleavage reagents, outlining the necessary steps involved in identifying interacting proteins both in vitro and in vivo. Once interacting proteins are identified, more information about the architecture of the assemblies is necessary. Unique separation techniques coupled with cross-linking and mass spectrometry can now identify specific interaction sites and lead to the development of quaternary structural protein models. Furthermore, combination of these methods with proteomic approaches enables the identification and analysis of complex interactions in vivo. Finally, future directions in cross-linking methodologies are discussed.     

Protein-protein interactions more conserved within species than across species

Experimental high-throughput studies of protein-protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein-protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein-protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein-protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/     

The folding of an enzyme. V. H/2H exchange-nuclear magnetic resonance studies on the folding pathway of barnase: complementarity to and agreement with protein engineering studies.

Two major methods are currently being used to characterize transient intermediates during protein folding at the level of individual residues. Nuclear magnetic resonance (n.m.r.) measurements on the protection of peptide NH hydrogens against exchange with solvent during refolding can provide information about secondary structure formation. Protein engineering and kinetics can provide direct information about intramolecular interactions of protein side-chains and indirect evidence on secondary structure. These procedures have provided the most complete pictures so far about protein folding intermediates. Both methods have been applied to the characterization of an intermediate in the refolding of barnase. Although the two methods give complementary information, there are some regions of the protein where the methods overlap well. We show that, with one possible exception that is obscure, n.m.r. and protein engineering give identical results for those interactions that can be analysed by both methods. This suggests that these are valid approaches for the study of protein folding intermediates in the case of barnase and that the combination of the methods is a powerful analytical procedure. Information provided by n.m.r. data that is complementary to the protein engineering experiments is: (1) early formation of the C terminus of helix2; (2) early formation of helix3; (3) early formation of several beta-turns (46-49, 101-104 in loop5); and (5) partial formation of loop5. Confirmatory evidence of protein engineering data on the intermediate is: (1) helix1 is complete from residues 10 to 18; (2) the interactions between all beta-strands are present; (3) part of loop2 is not formed; (4) part of loop3 is formed; and (5) some specific tertiary interactions are not made. For some interactions the protein engineering and H/2H exchange methods overlap directly. The information obtained for direct overlap is self consistent.     

Molecular and cellular approaches for the detection of protein-protein interactions: latest techniques and current limitations

Homotypic and heterotypic protein interactions are crucial for all levels of cellular function, including architecture, regulation, metabolism, and signaling. Therefore, protein interaction maps represent essential components of post-genomic toolkits needed for understanding biological processes at a systems level. Over the past decade, a wide variety of methods have been developed to detect, analyze, and quantify protein interactions, including surface plasmon resonance spectroscopy, NMR, yeast two-hybrid screens, peptide tagging combined with mass spectrometry and fluorescence-based technologies. Fluorescence techniques range from co-localization of tags, which may be limited by the optical resolution of the microscope, to fluorescence resonance energy transfer-based methods that have molecular resolution and can also report on the dynamics and localization of the interactions within a cell. Proteins interact via highly evolved complementary surfaces with affinities that can vary over many orders of magnitude. Some of the techniques described in this review, such as surface plasmon resonance, provide detailed information on physical properties of these interactions, while others, such as two-hybrid techniques and mass spectrometry, are amenable to high-throughput analysis using robotics. In addition to providing an overview of these methods, this review emphasizes techniques that can be applied to determine interactions involving membrane proteins, including the split ubiquitin system and fluorescence-based technologies for characterizing hits obtained with high-throughput approaches. Mass spectrometry-based methods are covered by a review by Miernyk and Thelen (2008; this issue, pp. 597–609 ). In addition, we discuss the use of interaction data to construct interaction networks and as the basis for the exciting possibility of using to predict interaction surfaces.     

SCOWLP classification: Structural comparison and analysis of protein binding regions

Background  

Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design.     

A quantitative analysis of interfacial amino acid conservation in protein-protein hetero complexes

A long-standing question in molecular biology is whether interfaces of protein-protein complexes are more conserved than the rest of the protein surfaces. Although it has been reported that conservation can be used as an indicator for predicting interaction sites on proteins, there are recent reports stating that the interface regions are only slightly more conserved than the rest of the protein surfaces, with conservation signals not being statistically significant enough for predicting protein-protein binding sites. In order to properly address these controversial reports we have studied a set of 28 well resolved hetero complex structures of proteins that consists of transient and non-transient complexes. The surface positions were classified into four conservation classes and the conservation index of the surface positions was quantitatively analyzed. The results indicate that the surface density of highly conserved positions is significantly higher in the protein-protein interface regions compared with the other regions of the protein surface. However, the average conservation index of the patches in the interface region is not significantly higher compared with other surface regions of the protein structures. This finding demonstrates that the number of conserved residue positions is a more appropriate indicator for predicting protein-protein binding sites than the average conservation index in the interacting region. We have further validated our findings on a set of 59 benchmark complex structures. Furthermore, an analysis of 19 complexes of antigen-antibody interactions shows that there is no conservation of amino acid positions in the interacting regions of these complexes, as expected, with the variable region of the immunoglobulins interacting mostly with the antigens. Interestingly, antigen interacting regions also have a higher number of non-conserved residue positions in the interacting region than the rest of the protein surface.     

Characterization of protein hubs by inferring interacting motifs from protein interactions

The characterization of protein interactions is essential for understanding biological systems. While genome-scale methods are available for identifying interacting proteins, they do not pinpoint the interacting motifs (e.g., a domain, sequence segments, a binding site, or a set of residues). Here, we develop and apply a method for delineating the interacting motifs of hub proteins (i.e., highly connected proteins). The method relies on the observation that proteins with common interaction partners tend to interact with these partners through a common interacting motif. The sole input for the method are binary protein interactions; neither sequence nor structure information is needed. The approach is evaluated by comparing the inferred interacting motifs with domain families defined for 368 proteins in the Structural Classification of Proteins (SCOP). The positive predictive value of the method for detecting proteins with common SCOP families is 75% at sensitivity of 10%. Most of the inferred interacting motifs were significantly associated with sequence patterns, which could be responsible for the common interactions. We find that yeast hubs with multiple interacting motifs are more likely to be essential than hubs with one or two interacting motifs, thus rationalizing the previously observed correlation between essentiality and the number of interacting partners of a protein. We also find that yeast hubs with multiple interacting motifs evolve slower than the average protein, contrary to the hubs with one or two interacting motifs. The proposed method will help us discover unknown interacting motifs and provide biological insights about protein hubs and their roles in interaction networks.     

Biochemical approaches for discovering protein-protein interactions

Protein–protein interactions or protein complexes are integral in nearly all cellular processes, ranging from metabolism to structure. Elucidating both individual protein associations and complex protein interaction networks, while challenging, is an essential goal of functional genomics. For example, discovering interacting partners for a 'protein of unknown function' can provide insight into actual function far beyond what is possible with sequence-based predictions, and provide a platform for future research. Synthetic genetic approaches such as two-hybrid screening often reveal a perplexing array of potential interacting partners for any given target protein. It is now known, however, that this type of anonymous screening approach can yield high levels of false-positive results, and therefore putative interactors must be confirmed by independent methods. In vitro biochemical strategies for identifying interacting proteins are varied and time-honored, some being as old as the field of protein chemistry itself. Herein we discuss five biochemical approaches for isolating and characterizing protein–protein interactions in vitro : co-immunoprecipitation, blue native gel electrophoresis, in vitro binding assays, protein cross-linking, and rate-zonal centrifugation. A perspective is provided for each method, and where appropriate specific, trial-tested methods are included.     

Identification of Semaphorin 5A Interacting Protein by Applying Apriori Knowledge and Peptide Complementarity Related to Protein Evolution and Structure

In the post-genomic era, various computational methods that predict proteinprotein interactions at the genome level are available; however, each method has its own advantages and disadvantages, resulting in false predictions. Here we developed a unique integrated approach to identify interacting partner（s） of Semaphorin 5A （SEMA5A）, beginning with seven proteins sharing similar ligand interacting residues as putative binding partners. The methods include Dwyer and Root- Bernstein/Dillon theories of protein evolution, hydropathic complementarity of protein structure, pattern of protein functions among molecules, information on domain-domain interactions, co-expression of genes and protein evolution. Among the set of seven proteins selected as putative SEMA5A interacting partners, we found the functions of Plexin B3 and Neuropilin-2 to be associated with SEMA5A. We modeled the semaphorin domain structure of Plexin B3 and found that it shares similarity with SEMA5A. Moreover, a virtual expression database search and RT-PCR analysis showed co-expression of SEMA5A and Plexin B3 and these proteins were found to have co-evolved. In addition, we confirmed the interaction of SEMA5A with Plexin B3 in co-immunoprecipitation studies. Overall, these studies demonstrate that an integrated method of prediction can be used at the genome level for discovering many unknown protein binding partners with known ligand binding domains.     

A systematic analysis of scoring functions in rigid‐body protein docking: The delicate balance between the predictive rate improvement and the risk of overtraining

Protein‐protein interactions play fundamental roles in biological processes including signaling, metabolism, and trafficking. While the structure of a protein complex reveals crucial details about the interaction, it is often difficult to acquire this information experimentally. As the number of interactions discovered increases faster than they can be characterized, protein‐protein docking calculations may be able to reduce this disparity by providing models of the interacting proteins. Rigid‐body docking is a widely used docking approach, and is often capable of generating a pool of models within which a near‐native structure can be found. These models need to be scored in order to select the acceptable ones from the set of poses. Recently, more than 100 scoring functions from the CCharPPI server were evaluated for this task using decoy structures generated with SwarmDock. Here, we extend this analysis to identify the predictive success rates of the scoring functions on decoys from three rigid‐body docking programs, ZDOCK, FTDock, and SDOCK, allowing us to assess the transferability of the functions. We also apply set‐theoretic measure to test whether the scoring functions are capable of identifying near‐native poses within different subsets of the benchmark. This information can provide guides for the use of the most efficient scoring function for each docking method, as well as instruct future scoring functions development efforts. Proteins 2017; 85:1287–1297. © 2017 Wiley Periodicals, Inc.