Evolvability of yeast protein-protein interaction interfaces

The functional importance of protein-protein interactions indicates that there should be strong evolutionary constraint on their interaction interfaces. However, binding interfaces are frequently affected by amino acid replacements. Change due to coevolution within interfaces can contribute to variability but is not ubiquitous. An alternative explanation for the ability of surfaces to accept replacements may be that many residues can be changed without affecting the interaction. Candidates for these types of residues are those that make interchain interaction only through the protein main chain, β-carbon, or associated hydrogen atoms. Since almost all residues have these atoms, we hypothesize that this subset of interface residues may be more easily substituted than those that make interactions through other atoms. We term such interactions "residue type independent." Investigating this hypothesis, we find that nearly a quarter of residues in protein interaction interfaces make exclusively interchain residue-type-independent contacts. These residues are less structurally constrained and less conserved than residues making residue-type-specific interactions. We propose that residue-type-independent interactions allow substitutions in binding interfaces while the specificity of binding is maintained.     

Computational design of second‐site suppressor mutations at protein–protein interfaces

The importance of a protein–protein interaction to a signaling pathway can be established by showing that amino acid mutations that weaken the interaction disrupt signaling, and that additional mutations that rescue the interaction recover signaling. Identifying rescue mutations, often referred to as second‐site suppressor mutations, controls against scenarios in which the initial deleterious mutation inactivates the protein or disrupts alternative protein–protein interactions. Here, we test a structure‐based protocol for identifying second‐site suppressor mutations that is based on a strategy previously described by Kortemme and Baker. The molecular modeling software Rosetta is used to scan an interface for point mutations that are predicted to weaken binding but can be rescued by mutations on the partner protein. The protocol typically identifies three types of specificity switches: knob‐in‐to‐hole redesigns, switching hydrophobic interactions to hydrogen bond interactions, and replacing polar interactions with nonpolar interactions. Computational predictions were tested with two separate protein complexes; the G‐protein Gα_i1 bound to the RGS14 GoLoco motif, and UbcH7 bound to the ubiquitin ligase E6AP. Eight designs were experimentally tested. Swapping a buried hydrophobic residue with a polar residue dramatically weakened binding affinities. In none of these cases were we able to identify compensating mutations that returned binding to wild‐type affinity, highlighting the challenges inherent in designing buried hydrogen bond networks. The strongest specificity switches were a knob‐in‐to‐hole design (20‐fold) and the replacement of a charge–charge interaction with nonpolar interactions (55‐fold). In two cases, specificity was further tuned by including mutations distant from the initial design. Proteins 2010. © 2009 Wiley‐Liss, Inc.     

Quantifying Escherichia coli glutaredoxin-3 substrate specificity using ligand-induced stability

Traditionally, quantification of protein-ligand affinity is performed using kinetic or equilibrium measurements. However, if the binding reaction proceeds via a stable covalent complex, these approaches are often limited. By exploiting the fact that the conformational stabilization of a protein is altered upon ligand binding due to specific interactions, and using an array of selectively chosen ligand analogs, one can quantify the contribution individual interactions have on specificity. We have used ligand-induced stability as a basis to dissect the interaction between glutaredoxin-3 (Grx3) and one of its native substrates, the tripeptide glutathione. Taking advantage of the fact that Grx3 can be trapped in a covalent mixed disulfide to glutathione or to selected synthetic glutathione analogs as part of the natural catalytic cycle, individual contributions to binding of specific molecular groups can be quantified by changes in ligand-induced stability. These changes in conformational stability are interpreted in terms of interaction energies (i.e. specificity) of the particular groups present on the ligand analog. Our results illustrate that although Grx3 recognizes glutathione predominantly through independent and additive ionic interactions at the N- and C-terminal of glutathione, van der Waals interactions from the unique gamma-glutamate moiety of glutathione also play an important role. This study places us closer to understanding the complex task of accommodating multiple substrate specificities in proteins of the thioredoxin superfamily and underscores the general applicability of ligand-induced stability to probe substrate specificity.     

The origins of specificity in polyketide synthase protein interactions

Polyketides, a diverse group of heteropolymers with antibiotic and antitumor properties, are assembled in bacteria by multiprotein chains of modular polyketide synthase (PKS) proteins. Specific protein-protein interactions determine the order of proteins within a multiprotein chain, and thereby the order in which chemically distinct monomers are added to the growing polyketide product. Here we investigate the evolutionary and molecular origins of protein interaction specificity. We focus on the short, conserved N- and C-terminal docking domains that mediate interactions between modular PKS proteins. Our computational analysis, which combines protein sequence data with experimental protein interaction data, reveals a hierarchical interaction specificity code. PKS docking domains are descended from a single ancestral interacting pair, but have split into three phylogenetic classes that are mutually noninteracting. Specificity within one such compatibility class is determined by a few key residues, which can be used to define compatibility subclasses. We identify these residues using a novel, highly sensitive co-evolution detection algorithm called CRoSS (correlated residues of statistical significance). The residue pairs selected by CRoSS are involved in direct physical interactions in a docked-domain NMR structure. A single PKS system can use docking domain pairs from multiple classes, as well as domain pairs from multiple subclasses of any given class. The termini of individual proteins are frequently shuffled, but docking domain pairs straddling two interacting proteins are linked as an evolutionary module. The hierarchical and modular organization of the specificity code is intimately related to the processes by which bacteria generate new PKS pathways.     

Evaluation of the energetic contribution of interhelical Coulombic interactions for coiled coil helix orientation specificity.

Coiled coils are formed by two or more alpha-helices that align in a parallel or an antiparallel relative orientation. The factors that determine a preference for a given relative helix orientation are incompletely understood. The helix orientation preference for the designed coiled coil, Acid-a1-Base-a1, was measured previously. This model system therefore provides a means for the experimental determination of the energetic contribution of a variety of interactions to helix orientation specificity.The antiparallel preference for Acid-a1-Base-a1 is imparted by a single buried polar interaction. Interhelical Coulombic interactions between residues at the e and g positions have been proposed to influence helix orientation preference. In the Acid-a1-Base-a1 heterodimer, potentially attractive Coulombic interactions are expected in both orientations. To determine the energetic consequences of Coulombic interactions for helix orientation preference, we have positioned a single charged residue in each peptide such that exclusively favorable interhelical Coulombic interactions can occur only in the parallel orientation. In contrast, two potentially repulsive interactions are expected in the antiparallel orientation. Because the buried polar interaction can occur only in the antiparallel orientation, interhelical Coulombic interactions favor the parallel orientation and the potential to form a buried polar interaction favors the antiparallel orientation. We find no clear preference for an antiparallel orientation in the resulting heterodimer, Acid-Ke-Base-Eg, suggesting that interhelical Coulombic interactions and a buried polar interaction are of approximately equal importance for helix orientation specificity. Stability measurements indicate that maintenance of all favorable electrostatic interactions and/or avoidance of two potentially repulsive interactions contributes approximately 2.1 kcal/mol to helix orientation preference.     

Designing specific protein-protein interactions using computation, experimental library screening, or integrated methods

Given the importance of protein-protein interactions for nearly all biological processes, the design of protein affinity reagents for use in research, diagnosis or therapy is an important endeavor. Engineered proteins would ideally have high specificities for their intended targets, but achieving interaction specificity by design can be challenging. There are two major approaches to protein design or redesign. Most commonly, proteins and peptides are engineered using experimental library screening and/or in vitro evolution. An alternative approach involves using protein structure and computational modeling to rationally choose sequences predicted to have desirable properties. Computational design has successfully produced novel proteins with enhanced stability, desired interactions and enzymatic function. Here we review the strengths and limitations of experimental library screening and computational structure-based design, giving examples where these methods have been applied to designing protein interaction specificity. We highlight recent studies that demonstrate strategies for combining computational modeling with library screening. The computational methods provide focused libraries predicted to be enriched in sequences with the properties of interest. Such integrated approaches represent a promising way to increase the efficiency of protein design and to engineer complex functionality such as interaction specificity.     

PDZ Motifs in PTP-BL and RIL Bind to Internal Protein Segments in the LIM Domain Protein RIL

The specificity of protein–protein interactions in cellular signaling cascades is dependent on the sequence and intramolecular location of distinct amino acid motifs. We used the two-hybrid interaction trap to identify proteins that can associate with the PDZ motif-rich segment in the protein tyrosine phosphatase PTP-BL. A specific interaction was found with the Lin-11, Isl-1, Mec-3 (LIM) domain containing protein RIL. More detailed analysis demonstrated that the binding specificity resides in the second and fourth PDZ motif of PTP-BL and the LIM domain in RIL. Immunohistochemistry on various mouse tissues revealed a submembranous colocalization of PTP-BL and RIL in epithelial cells. Remarkably, there is also an N-terminal PDZ motif in RIL itself that can bind to the RIL-LIM domain. We demonstrate here that the RIL-LIM domain can be phosphorylated on tyrosine in vitro and in vivo and can be dephosphorylated in vitro by the PTPase domain of PTP-BL. Our data point to the presence of a double PDZ-binding interface on the RIL-LIM domain and suggest tyrosine phosphorylation as a regulatory mechanism for LIM-PDZ associations in the assembly of multiprotein complexes. These findings are in line with an important role of PDZ-mediated interactions in the shaping and organization of submembranous microenvironments of polarized cells.     

High confidence determination of specific protein-protein interactions using quantitative mass spectrometry

In recent years, interactions between proteins have successfully been determined by mass spectrometry. A limitation of this technology has been the need for extensive purification, which restricts throughput and implies a tradeoff between specificity and the ability to detect weak or transient interactions. Quantitative proteomics sidesteps this problem by directly comparing specific and control pull-downs. Specific interaction partners are revealed by their quantitative ratios rather than by gel-based visualization and can be retrieved from a vast excess of background proteins. This principle is revolutionizing the protein interaction field as demonstrated by recent applications in fields as diverse as tyrosine signaling pathways, cell adhesion, and chromatin biology.     

On the Importance of Polar Interactions for Complexes Containing Intrinsically Disordered Proteins

There is a growing recognition for the importance of proteins with large intrinsically disordered (ID) segments in cell signaling and regulation. ID segments in these proteins often harbor regions that mediate molecular recognition. Coupled folding and binding of the recognition regions has been proposed to confer high specificity to interactions involving ID segments. However, researchers recently questioned the origin of the interaction specificity of ID proteins because of the overrepresentation of hydrophobic residues in their interaction interfaces. Here, we focused on the role of polar and charged residues in interactions mediated by ID segments. Making use of the extended nature of most ID segments when in complex with globular proteins, we first identified large numbers of complexes between globular proteins and ID segments by using radius-of-gyration-based selection criteria. Consistent with previous studies, we found the interfaces of these complexes to be enriched in hydrophobic residues, and that these residues contribute significantly to the stability of the interaction interface. However, our analyses also show that polar interactions play a larger role in these complexes than in structured protein complexes. Computational alanine scanning and salt-bridge analysis indicate that interfaces in ID complexes are highly complementary with respect to electrostatics, more so than interfaces of globular proteins. Follow-up calculations of the electrostatic contributions to the free energy of binding uncovered significantly stronger Coulombic interactions in complexes harbouring ID segments than in structured protein complexes. However, they are counter-balanced by even higher polar-desolvation penalties. We propose that polar interactions are a key contributing factor to the observed high specificity of ID segment-mediated interactions.     

Spatial and temporal variation in host–parasitoid interactions: leafcutter ant hosts and their phorid parasitoids

1. Parasitoid–host interactions are important components of ecological communities. Although parasitoid–host interactions are strongly shaped by evolutionary history, the abundance of both the parasitoid and the host may have a role in determining the nature of the interaction once phylogenetic relationships are considered. 2. Leafcutter ants are hosts of phorid parasitoids and represent a well‐defined and specialised module within a larger network of ant–symbiont interactions. A low specificity host taxa and a positive association between host abundance and parasitoid interaction frequency were expected due to the close phylogenetic relatedness of the hosts. 3. The interactions among all species of leafcutter ants and their parasitoids were quantified in two localities with different species richness. This study also characterised the spatial‐temporal variability of these interactions, determined the patterns of parasitoid specificity and host selection, and tested for an association between host abundance and parasitoid interaction frequency. 4. Contrary to expectation, most parasitoid species were highly specialised and interaction frequency for parasitoid species was not related to host abundance. All host ant species were attacked by more than one phorid species. Some phorid species used more than one host species and showed preference for the same species over space and time, suggesting that there are physiological and/or behavioural restrictions on host use. 5. These results show that there is a tendency for specialisation even when hosts are highly similar in their ecology. From a biological control perspective, these parasitoids may be effective candidates, due to the high specificity of some species and little host‐use variation through time.     

Inferring protein-protein interactions through high-throughput interaction data from diverse organisms

MOTIVATION: Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities. RESULTS: We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism. AVAILABILITY: The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.     

The multiple‐specificity landscape of modular peptide recognition domains

Modular protein interaction domains form the building blocks of eukaryotic signaling pathways. Many of them, known as peptide recognition domains, mediate protein interactions by recognizing short, linear amino acid stretches on the surface of their cognate partners with high specificity. Residues in these stretches are usually assumed to contribute independently to binding, which has led to a simplified understanding of protein interactions. Conversely, we observe in large binding peptide data sets that different residue positions display highly significant correlations for many domains in three distinct families (PDZ, SH3 and WW). These correlation patterns reveal a widespread occurrence of multiple binding specificities and give novel structural insights into protein interactions. For example, we predict a new binding mode of PDZ domains and structurally rationalize it for DLG1 PDZ1. We show that multiple specificity more accurately predicts protein interactions and experimentally validate some of the predictions for the human proteins DLG1 and SCRIB. Overall, our results reveal a rich specificity landscape in peptide recognition domains, suggesting new ways of encoding specificity in protein interaction networks.     

Prediction of protein-protein interactions using random decision forest framework

MOTIVATION: Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Domains are the building blocks of proteins; therefore, proteins are assumed to interact as a result of their interacting domains. Many domain-based models for protein interaction prediction have been developed, and preliminary results have demonstrated their feasibility. Most of the existing domain-based methods, however, consider only single-domain pairs (one domain from one protein) and assume independence between domain-domain interactions. RESULTS: In this paper, we introduce a domain-based random forest of decision trees to infer protein interactions. Our proposed method is capable of exploring all possible domain interactions and making predictions based on all the protein domains. Experimental results on Saccharomyces cerevisiae dataset demonstrate that our approach can predict protein-protein interactions with higher sensitivity (79.78%) and specificity (64.38%) compared with that of the maximum likelihood approach. Furthermore, our model can be used to infer interactions not only for single-domain pairs but also for multiple domain pairs.     

A novel mechanism of TRAF signaling revealed by structural and functional analyses of the TRADD-TRAF2 interaction

TRAF proteins are major mediators for the cell activation, cell survival, and antiapoptotic functions of the TNF receptor superfamily. They can be recruited to activated TNF receptors either by direct interactions with the receptors or indirectly via the adaptor protein TRADD. We now report the structure of the TRADD-TRAF2 complex, which is highly distinct from receptor-TRAF2 interactions. This interaction is significantly stronger and we show by an in vivo signaling assay that TRAF2 signaling is more readily initiated by TRADD than by direct receptor-TRAF2 interactions. TRADD is specific for TRAF1 and TRAF2, which ensures the recruitment of clAPs for the direct inhibition of caspase activation in the signaling complex. The stronger affinity and unique specificity of the TRADD-TRAF2 interaction are crucial for the suppression of apoptosis and provide a mechanistic basis for the perturbation of TRAF recruitment in sensitizing cell death induction.     

Genetic network inference: the effects of preprocessing

Clustering of gene expression data and gene network inference from such data has been a major research topic in recent years. In clustering, pairwise measurements are performed when calculating the distance matrix upon which the clustering is based. Pairwise measurements can also be used for gene network inference, by deriving potential interactions above a certain correlation or distance threshold. Our experiments show how interaction networks derived by this simple approach exhibit low-but significant-sensitivity and specificity. We also explore the effects that normalization and prefiltering have on the results of methods for identifying interactions from expression data. Before derivation of interactions or clustering, preprocessing is often performed by applying normalization to rescale the expression profiles and prefiltering where genes that do not appear to contribute to regulation are removed. In this paper, different ways of normalizing in combination with different distance measurements are tested on both unfiltered and prefiltered data, different prefiltering criteria are considered.     

Structures of ionic liquids dictate the conversion and selectivity of enzymatic glycerolysis: theoretical characterization by COSMO-RS

Lipase-catalyzed glycerolysis of triolein has been examined using a group of tetraammonium-based ionic liquids (ILs) as media, specifically with functional groups in cation part. The results demonstrated that the reaction evolution and profile specificity of respective IL system could be quantitatively associated with the structural characteristics of the IL by means of quantum chemical and COSMO-RS calculation. Misfit interaction, Van der Waals interaction and chemical potential, etc. derived from COSMO-RS calculation are shown to be effective measures to delineate multiple interactions of ILs and then can be used to understand the effects of ILs on reactions. The hydrophobic substituents in the cation are found to contribute to the increase of triolein solubility and enhancement of initial reaction rate; while strong polar anion and polyethoxyl and free hydroxyl groups in the cation part dictate improved product selectivity through reducing activity coefficients of monoglycerides. Integration of these structures into the same molecule constitutes a promising group of ILs that could produce over 90% monoglyceride with almost 100% triglyceride conversion, as well as bulky productivity, of particular potential for industrial applications. Overall, this work has presented a first attempt to characterize the IL structure-dependency of reaction specificity by associating structural variations of ILs with thermodynamic property changes of resided compounds and subsequent effects on reaction specificity. This might be of general value to help to understand the multiple solvation interaction among IL reaction systems at molecular level and promote the application of IL-mediated reactions to practical interests.