Codep: maximizing co-evolutionary interdependencies to discover interacting proteins

Approaches for the determination of interacting partners from different protein families (such as ligands and their receptors) have made use of the property that interacting proteins follow similar patterns and relative rates of evolution. Interacting protein partners can then be predicted from the similarity of their phylogenetic trees or evolutionary distances matrices. We present a novel method called Codep, for the determination of interacting protein partners by maximizing co-evolutionary signals. The order of sequences in the multiple sequence alignments from two protein families is determined in such a manner as to maximize the similarity of substitution patterns at amino acid sites in the two alignments and, thus, phylogenetic congruency. This is achieved by maximizing the total number of interdependencies of amino acids sites between the alignments. Once ordered, the corresponding sequences in the two alignments indicate the predicted interacting partners. We demonstrate the efficacy of this approach with computer simulations and in analyses of several protein families. A program implementing our method, Codep, is freely available to academic users from our website: http://www.uhnresearch.ca/labs/tillier/.     

Correlated mutations: advances and limitations. A study on fusion proteins and on the Cohesin-Dockerin families

Correlated mutations have been repeatedly exploited for intramolecular contact map prediction. Over the last decade these efforts yielded several methods for measuring correlated mutations. Nevertheless, the application of correlated mutations for the prediction of intermolecular interactions has not yet been explored. This gap is due to several obstacles, such as 3D complexes availability, paralog discrimination, and the availability of sequence pairs that are required for inter- but not intramolecular analyses. Here we selected for analysis fusion protein families that bypass some of these obstacles. We find that several correlated mutation measurements yield reasonable accuracy for intramolecular contact map prediction on the fusion dataset. However, the accuracy level drops sharply in intermolecular contacts prediction. This drop in accuracy does not occur always. In the Cohesin-Dockerin family, reasonable accuracy is achieved in the prediction of both intra- and intermolecular contacts. The Cohesin-Dockerin family is well suited for correlated mutation analysis. Because, however, this family constitutes a special case (it has radical mutations, has domain repeats, within each species each Dockerin domain interacts with each Cohesin domain, see below), the successful prediction in this family does not point to a general potential in using correlated mutations for predicting intermolecular contacts. Overall, the results of our study indicate that current methodologies of correlated mutations analysis are not suitable for large-scale intermolecular contact prediction, and thus cannot assist in docking. With current measurements, sequence availability, sequence annotations, and underdeveloped sequence pairing methods, correlated mutations can yield reasonable accuracy only for a handful of families.     

A fast and simple method for probing the interaction of peptides and proteins with lipids and membrane-mimetics using GB1 fusion proteins and NMR spectroscopy

The expression of peptides and proteins as fusions to the B1 domain of streptococcal protein G (GB1) is very popular since GB1 often improves the solubility of the target protein and because the first purification step using IgG affinity chromatography is simple and efficient. However, the following protease digest is not always complete or can result in a digest of the target protein. In addition, a further purification step such as RP-HPLC has to be used to get rid of the GB1 tag and undigested fusion protein. Because the protease digest and the following purification step are not only time-consuming but generally also expensive, we tested if GB1 fusion proteins can directly be used for NMR interaction studies using lipids or membrane-mimetics. Based on NMR binding studies using only the GB1 part, this fusion tag does not significantly interact with different membrane-mimetics such as micelles, bicelles, or liposomes. Thus spectral changes observed using GB1-fusion proteins indicate lipid- and membrane interactions of the target protein. The method was initially established to probe membrane interactions of a large number of mutants of the FATC domain of the ser/thr kinase TOR. To demonstrate the usefulness of the approach, we show NMR binding data for the wild type protein and a leucine to alanine mutant.     

In silico two-hybrid system for the selection of physically interacting protein pairs

Deciphering the interaction links between proteins has become one of the main tasks of experimental and bioinformatic methodologies. Reconstruction of complex networks of interactions in simple cellular systems by integrating predicted interaction networks with available experimental data is becoming one of the most demanding needs in the postgenomic era. On the basis of the study of correlated mutations in multiple sequence alignments, we propose a new method (in silico two-hybrid, i2h) that directly addresses the detection of physically interacting protein pairs and identifies the most likely sequence regions involved in the interactions. We have applied the system to several test sets, showing that it can discriminate between true and false interactions in a significant number of cases. We have also analyzed a large collection of E. coli protein pairs as a first step toward the virtual reconstruction of its complete interaction network.     

Interaction interfaces of protein domains are not topologically equivalent across families within superfamilies: Implications for metabolic and signaling pathways

Using a data set of aligned protein domain superfamilies of known three-dimensional structure, we compared the location of interdomain interfaces on the tertiary folds between members of distantly related protein domain superfamilies. The data set analyzed is comprised of interdomain interfaces, with domains occurring within a polypeptide chain and those between two polypeptide chains. We observe that, in general, the interfaces between protein domains are formed entirely in different locations on the tertiary folds in such pairs. This variation in the location of interface happens in protein domains involved in a wide range of functions, such as enzymes, adapters, and domains that bind protein ligands, or cofactors. While basic biochemical functionality is preserved at the domain superfamily level, the effect of biochemical function on protein assemblies is different in these protein domains related by superfamily. The divergence between proteins, in most cases, is coupled with domain recruitment, with different modes of interaction with the recruited domain. This is in complete contrast to the observation that in closely related homologous protein domains, almost always the interaction interfaces are topologically equivalent. In a small subset of interacting domains within proteins related by remote homology, we observe that the relative positioning of domains with respect to one another is preserved. Based on the analysis of multidomain proteins of known or unknown structure, we suggest that variation in protein-protein interactions in members within a superfamily could serve as diverging points in otherwise parallel metabolic or signaling pathways. We discuss a few representative cases of diverging pathways involving domains in a superfamily.     

Finding biologically relevant protein domain interactions: conserved binding mode analysis

Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.     

Integration of cell-free protein coexpression with an enzyme-linked immunosorbent assay enables rapid analysis of protein-protein interactions directly from DNA

Assays that integrate detection of binding with cell-free protein expression directly from DNA can dramatically increase the pace at which protein-protein interactions (PPIs) can be analyzed by mutagenesis. In this study, we present a method that combines in vitro protein production with an enzyme-linked immunosorbent assay (ELISA) to measure PPIs. This method uses readily available commodity instrumentation and generic antibody-affinity tag interactions. It is straightforward and rapid to execute, enabling many interactions to be assessed in parallel. In traditional ELISAs, reporter complexes are assembled stepwise with one layer at a time. In the method presented here, all the members of the reporter complex are present and assembled together. The signal strength is dependent on all the intercomponent interaction affinities and concentrations. Although this assay is straightforward to execute, establishing proper conditions and analysis of the results require a thorough understanding of the processes that determine the signal strength. The formation of the fully assembled reporter sandwich can be modeled as a competition between Langmuir adsorption isotherms for the immobilized components and binding equilibria of the solution components. We have shown that modeling this process provides semiquantitative understanding of the effects of affinity and concentration and can guide strategies for the development of experimental protocols. We tested the method experimentally using the interaction between a synthetic ankyrin repeat protein (Off7) and maltose-binding protein. Measurements obtained for a collection of alanine mutations in the interface between these two proteins demonstrate that a range of affinities can be analyzed.     

Toward a systems level view of the ECM and related proteins: a framework for the systematic definition and analysis of biological systems

Advances in high throughput 'omic technologies are starting to provide unprecedented insights into how components of biological systems are organized and interact. Key to exploiting these datasets is the definition of the components that comprise the system of interest. Although a variety of knowledge bases exist that capture such information, a major challenge is determining how these resources may be best utilized. Here we present a systematic curation strategy to define a systems-level view of the human extracellular matrix (ECM)--a three-dimensional meshwork of proteins and polysaccharides that impart structure and mechanical stability to tissues. Employing our curation strategy we define a set of 357 proteins that represent core components of the ECM, together with an additional 524 genes that mediate related functional roles, and construct a map of their physical interactions. Topological properties help identify modules of functionally related proteins, including those involved in cell adhesion, bone formation and blood clotting. Because of its major role in cell adhesion, proliferation and morphogenesis, defects in the ECM have been implicated in cancer, atherosclerosis, asthma, fibrosis, and arthritis. We use MeSH annotations to identify modules enriched for specific disease terms that aid to strengthen existing as well as predict novel gene-disease associations. Mapping expression and conservation data onto the network reveal modules evolved in parallel to convey tissue-specific functionality on otherwise broadly expressed units. In addition to demonstrating an effective workflow for defining biological systems, this study crystallizes our current knowledge surrounding the organization of the ECM.     

The third dimension for protein interactions and complexes

Interaction discovery methods, such as the two-hybrid system and affinity purification, suggest thousands of protein–protein interactions. Structural biology provides atomic details for many interactions but, to date, there has been limited discussion of how these two fields complement each other. Here, we apply a structural perspective to interpret interactions discovered by different techniques. This perspective reveals indirect interactions in two-hybrid systems, instances where molecular labels might obstruct interfaces, and possible explanations for why certain promiscuous proteins interact with many others. It also highlights that some methods favour tight complexes whereas others favour interactions of a more transient nature. We conclude by discussing how a combination of interaction discovery and structural biology will enhance our understanding of complex cellular processes.     

The toxin-antitoxin proteins relBE2Spn of Streptococcus pneumoniae: characterization and association to their DNA target

The chromosome of the pathogenic Gram-positive bacterium Streptococcus pneumoniae contains between six to 10 operons encoding toxin-antitoxin systems (TAS). TAS are widespread and redundant in bacteria and archaea and their role, albeit still obscure, may be related to important aspects of bacteria lifestyle like response to stress. One of the most abundant TAS is the relBE family, being present in the chromosome of many bacteria and archaea. Because of the high rates of morbility and mortality caused by S. pneumoniae, it has been interesting to gain knowledge on the pneumococcal TAS, among them the RelBE2Spn proteins. Here, we have analyzed the DNA binding capacity of the RelB2Spn antitoxin and the RelB2Spn-RelE2Spn proteins by band-shift assays. Thus, a DNA region encompassing the operator region of the proteins was identified. In addition, we have used analytical ultracentrifugation and native mass spectrometry to measure the oligomerization state of the antitoxin alone and the RelBE2Spn complex in solution bound or unbound to its DNA substrate. Using native mass spectrometry allowed us to unambiguously determine the stoichiometry of the RelB2Spn and of the RelBE2Spn complex alone or associated to its DNA target.     

Systematic analysis of tropomodulin/tropomyosin interactions uncovers fine-tuned binding specificity of intrinsically disordered proteins

An intriguing regulatory mechanism is the ability of some proteins to recognize their binding partners in an isoform‐specific manner. In this study we undertook a systematic analysis of the specificity of the tropomodulin (Tmod) interaction with tropomyosin (TM) to show that affinities of different Tmod isoforms to TM are isoform‐dependent. Intrinsic disorder predictions, alignment of sequences, and circular dichroism were utilized to establish a structural basis for these isoform‐specific interactions. The affinity of model peptides derived from the N‐terminus of different TM isoforms to protein fragments that correspond to the two TM‐binding sites of different Tmod isoforms were analyzed. Several residues were determined to be responsible for the isoform‐dependent differences in affinity. We suggest that changing a set of residues rather than a single residue is needed to alter the binding affinity of one isoform to mimic the affinity of another isoform. The general intrinsic disorder predictor, PONDR® VLXT, was shown to be a useful tool for analyzing regions involved in isoform‐specific binding and for predicting the residues important for isoform differences in binding. Knowing the residues responsible for isoform‐specific affinity creates a tool suitable for studying the influence of Tmod/TM interactions on sarcomere assembly in muscle cells or actin dynamics in non‐muscle cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Blue copper proteins: a comparative analysis of their molecular interaction properties

Blue copper proteins are type-I copper-containing redox proteins whose role is to shuttle electrons from an electron donor to an electron acceptor in bacteria and plants. A large amount of experimental data is available on blue copper proteins; however, their functional characterization is hindered by the complexity of redox processes in biological systems. We describe here the application of a semiquantitative method based on a comparative analysis of molecular interaction fields to gain insights into the recognition properties of blue copper proteins. Molecular electrostatic and hydrophobic potentials were computed and compared for a set of 33 experimentally-determined structures of proteins from seven blue copper subfamilies, and the results were quantified by means of similarity indices. The analysis provides a classification of the blue copper proteins and shows that (I) comparison of the molecular electrostatic potentials provides useful information complementary to that highlighted by sequence analysis; (2) similarities in recognition properties can be detected for proteins belonging to different subfamilies, such as amicyanins and pseudoazurins, that may be isofunctional proteins; (3) dissimilarities in interaction properties, consistent with experimentally different binding specificities, may be observed between proteins belonging to the same subfamily, such as cyanobacterial and eukaryotic plastocyanins; (4) proteins with low sequence identity, such as azurins and pseudoazurins, can have sufficient similarity to bind to similar electron donors and acceptors while having different binding specificity profiles.     

Virtual identification of essential proteins within the protein interaction network of yeast

Topological analysis of large scale protein-protein interaction networks (PINs) is important for understanding the organizational and functional principles of individual proteins. The number of interactions that a protein has in a PIN has been observed to be correlated with its indispensability. Essential proteins generally have more interactions than the nonessential ones. We show here that the lethality associated with removal of a protein from the yeast proteome correlates with different centrality measures of the nodes in the PIN, such as the closeness of a protein to many other proteins, or the number of pairs of proteins which need a specific protein as an intermediary in their communications, or the participation of a protein in different protein clusters in the PIN. These measures are significantly better than random selection in identifying essential proteins in a PIN. Centrality measures based on graph spectral properties of the network, in particular the subgraph centrality, show the best performance in identifying essential proteins in the yeast PIN. Subgraph centrality gives important structural information about the role of individual proteins, and permits the selection of possible targets for rational drug discovery through the identification of essential proteins in the PIN.     

Charged residues at protein interaction interfaces: unexpected conservation and orchestrated divergence

Protein-protein interactions play an essential role in the functioning of cell. The importance of charged residues and their diverse role in protein-protein interactions have been well studied using experimental and computational methods. Often, charged residues located in protein interaction interfaces are conserved across the families of homologous proteins and protein complexes. However, on a large scale, it has been recently shown that charged residues are significantly less conserved than other residue types in protein interaction interfaces. The goal of this work is to understand the role of charged residues in the protein interaction interfaces through their conservation patterns. Here, we propose a simple approach where the structural conservation of the charged residue pairs is analyzed among the pairs of homologous binary complexes. Specifically, we determine a large set of homologous interactions using an interaction interface similarity measure and catalog the basic types of conservation patterns among the charged residue pairs. We find an unexpected conservation pattern, which we call the correlated reappearance, occurring among the pairs of homologous interfaces more frequently than the fully conserved pairs of charged residues. Furthermore, the analysis of the conservation patterns across different superkingdoms as well as structural classes of proteins has revealed that the correlated reappearance of charged residues is by far the most prevalent conservation pattern, often occurring more frequently than the unconserved charged residues. We discuss a possible role that the new conservation pattern may play in the long-range electrostatic steering effect.     

On the nature of cavities on protein surfaces: application to the identification of drug-binding sites

In this article we introduce a new method for the identification and the accurate characterization of protein surface cavities. The method is encoded in the program SCREEN (Surface Cavity REcognition and EvaluatioN). As a first test of the utility of our approach we used SCREEN to locate and analyze the surface cavities of a nonredundant set of 99 proteins cocrystallized with drugs. We find that this set of proteins has on average about 14 distinct cavities per protein. In all cases, a drug is bound at one (and sometimes more than one) of these cavities. Using cavity size alone as a criterion for predicting drug-binding sites yields a high balanced error rate of 15.7%, with only 71.7% coverage. Here we characterize each surface cavity by computing a comprehensive set of 408 physicochemical, structural, and geometric attributes. By applying modern machine learning techniques (Random Forests) we were able to develop a classifier that can identify drug-binding cavities with a balanced error rate of 7.2% and coverage of 88.9%. Only 18 of the 408 cavity attributes had a statistically significant role in the prediction. Of these 18 important attributes, almost all involved size and shape rather than physicochemical properties of the surface cavity. The implications of these results are discussed. A SCREEN Web server is available at http://interface.bioc.columbia.edu/screen.     

Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false‐positive and false‐negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co‐complex relationship, and (3) pathway co‐membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity‐based k‐Nearest‐Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co‐complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top‐ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast‐2‐hybrid system were not among the top‐ranking features under any condition. Proteins 2006. © 2006 Wiley‐Liss, Inc.