Using indirect protein-protein interactions for protein complex prediction

Protein complexes are fundamental for understanding principles of cellular organizations. As the sizes of protein-protein interaction (PPI) networks are increasing, accurate and fast protein complex prediction from these PPI networks can serve as a guide for biological experiments to discover novel protein complexes. However, it is not easy to predict protein complexes from PPI networks, especially in situations where the PPI network is noisy and still incomplete. Here, we study the use of indirect interactions between level-2 neighbors (level-2 interactions) for protein complex prediction. We know from previous work that proteins which do not interact but share interaction partners (level-2 neighbors) often share biological functions. We have proposed a method in which all direct and indirect interactions are first weighted using topological weight (FS-Weight), which estimates the strength of functional association. Interactions with low weight are removed from the network, while level-2 interactions with high weight are introduced into the interaction network. Existing clustering algorithms can then be applied to this modified network. We have also proposed a novel algorithm that searches for cliques in the modified network, and merge cliques to form clusters using a "partial clique merging" method. Experiments show that (1) the use of indirect interactions and topological weight to augment protein-protein interactions can be used to improve the precision of clusters predicted by various existing clustering algorithms; and (2) our complex-finding algorithm performs very well on interaction networks modified in this way. Since no other information except the original PPI network is used, our approach would be very useful for protein complex prediction, especially for prediction of novel protein complexes.     

Comparison of protein-protein interactions in serine protease-inhibitor and antibody-antigen complexes: implications for the protein docking problem

The protein-protein interaction energy of 12 nonhomologous serine protease-inhibitor and 15 antibody-antigen complexes is calculated using a molecular mechanics formalism and dissected in terms of the main-chain vs. side-chain contribution, nonrotameric side-chain contributions, and amino acid residue type involvement in the interface interaction. There are major differences in the interactions of the two types of protein-protein complex. Protease-inhibitor complexes interact predominantly through a main-chain-main-chain mechanism while antibody-antigen complexes interact predominantly through a side-chain-side-chain or a side-chain-main-chain mechanism. However, there is no simple correlation between the main-chain-main-chain interaction energy and the percentage of main-chain surface area buried on binding. The interaction energy is equally effected by the presence of nonrotameric side-chain conformations, which constitute approximately 20% of the interaction energy. The ability to reproduce the interface interaction energy of the crystal structure if original side-chain conformations are removed from the calculation is much greater in the protease-inhibitor complexes than the antibody-antigen complexes. The success of a rotameric model for protein-protein docking appears dependent on the extent of the main-chain-main-chain contribution to binding. Analysis of (1) residue type and (2) residue pair interactions at the interface show that antibody-antigen interactions are very restricted with over 70% of the antibody energy attributable to just six residue types (Tyr > Asp > Asn > Ser > Glu > Trp) in agreement with previous studies on residue propensity. However, it is found here that 50% of the antigen energy is attributable to just four residue types (Arg = Lys > Asn > Asp). On average just 12 residue pair interactions (6%) contribute over 40% of the favorable interaction energy in the antibody-antigen complexes, with charge-charge and charge/polar-tyrosine interactions being prominent. In contrast protease inhibitors use a diverse set of residue types and residue pair interactions.     

Isolation of protein subpopulations undergoing protein-protein interactions

A new method is described for isolating and identifying proteins participating in protein-protein interactions in a complex mixture. The method uses a cyanogen bromide-activated Sepharose matrix to isolate proteins that are non-covalently bound to other proteins. Because the proteins are accessible to chemical manipulation, mass spectrometric identification of the proteins can yield information on specific classes of interacting proteins, such as calcium-dependent or substrate-dependent protein interactions. This permits selection of a subpopulation of proteins from a complex mixture on the basis of specified interaction criteria. The new method has the advantage of screening the entire proteome simultaneously, unlike the two-hybrid system or phage display, which can only detect proteins binding to a single bait protein at a time. The method was tested by selecting rat brain extract for proteins exhibiting calcium-dependent protein interactions. Of 12 proteins identified by mass spectrometry, eight were either known calcium-binding proteins or proteins with known calcium-dependent protein interactions, indicating that the method is capable of enriching a subpopulation of proteins from a complex mixture on the basis of a specific class of protein interactions. Because only naturally occurring interactions of proteins in their native state are observed, this method will have wide applicability to studies of protein interactions in tissue samples and autopsy specimens, for screening for perturbations of protein-protein interactions by signaling molecules, pharmacological agents or toxins, and screening for differences between cancerous and untransformed cells.     

Solution structure of the Rhodobacter sphaeroides PufX membrane protein: implications for the quinone exchange and protein-protein interactions

PufX membrane protein is found in Rhodobacter species of purple photosynthetic bacteria and has been known to play an essential role in ubiquinone/ubiquinol exchange between the reaction center and cytochrome bc1 complex and also contribute to the dimerization of the reaction center-light-harvesting core complex. We have determined the solution structure of the Rhodobacter sphaeroides PufX using multidimensional NMR spectroscopy. The PufX, functionally expressed in Escherichia coli, forms a stable alpha helix consisting of 21 residues over the central transmembrane domain. The overall structure of the PufX is very similar to those of the LH1 alpha- and beta-polypeptides from Rhodospirillum rubrum and LH2 polypeptides. A short segment (Lys28-Gly35) rich in Gly and Ala residues revealed a relatively fast exchange between the backbone amide protons and deuteriums in the hydroxyl groups of the solvent, indicating that the backbone of this segment is more easily accessible to the surrounding solvent molecules compared to those of its neighboring portions. The Gly- and Ala-rich segment is located in the middle of the central helix and forms an extensive groove-like conformation on the surface with the neighboring residues, where the residues with large side chains are aligned on one side of the helix, and small residues are aligned on the other face. Such a structural motif may serve as a functional site responsible for ubiquinol transport from the core complex to the membrane phase and for sequence-specific helix-helix interactions with the neighboring polypeptides.     

Detection of protein-protein interactions in vivo based on protein splicing

In mammalian cells, protein-protein interactions constitute essential regulatory steps that modulate the activity of signaling pathways. In recent years, several approaches towards understanding the interactions have been developed. We describe herein a new method for detecting protein-protein interactions in vivo based on protein splicing and highlight some potential applications of this technique.     

Prediction of protein-protein interactions using protein signature profiling

Protein domains are conserved and functionally independent structures that play an important role in interactions among related proteins. Domain-domain interactions have been recently used to predict protein-protein interactions (PPI). In general, the interaction probability of a pair of domains is scored using a trained scoring function. Satisfying a threshold, the protein pairs carrying those domains are regarded as "interacting". In this study, the signature contents of proteins were utilized to predict PPI pairs in Saccharomyces cerevisiae, Caenorhabditis elegans, and Homo sapiens. Similarity between protein signature patterns was scored and PPI predictions were drawn based on the binary similarity scoring function. Results show that the true positive rate of prediction by the proposed approach is approximately 32% higher than that using the maximum likelihood estimation method when compared with a test set, resulting in 22% increase in the area under the receiver operating characteristic (ROC) curve. When proteins containing one or two signatures were removed, the sensitivity of the predicted PPI pairs increased significantly. The predicted PPI pairs are on average 11 times more likely to interact than the random selection at a confidence level of 0.95, and on average 4 times better than those predicted by either phylogenetic profiling or gene expression profiling.     

Green fluorescent protein as a signal for protein-protein interactions.

Green fluorescent protein (GFP) is autofluorescent. This property has made GFP useful in monitoring in vivo activities such as gene expression and protein localization. We find that GFP can be used in vitro to reveal and characterize protein-protein interactions. The interaction between the S-peptide and S-protein fragments of ribonuclease A was chosen as a model system. GFP-tagged S-peptide was produced, and the interaction of this fusion protein with S-protein was analyzed by two distinct methods: fluorescence gel retardation and fluorescence polarization. The fluorescence gel retardation assay is a rapid method to demonstrate the existence of a protein-protein interaction and to estimate the dissociation constant (Kd) of the resulting complex. The fluorescence polarization assay is an accurate method to evaluate Kd in a specified homogeneous solution and can be adapted for the high-throughput screening of protein or peptide libraries. These two methods are powerful new tools to probe protein-protein interactions.     

Effects of pH on tubulin-nucleotide interactions

Significant GTP-independent, temperature-dependent turbidity development occurs with purified tubulin stored in the absence of unbound nucleotide, and this can be minimized with a higher reaction pH. Since microtubule assembly is optimal at lower pH values, we examined pH effects on tubulin-nucleotide interactions. While the lowest concentration of GTP required for assembly changed little, GDP was more inhibitory at higher pH values. The amounts of exogenous GTP bound to tubulin at all pH values were similar, but the amounts of exogenous GDP bound and endogenous GDP (i.e., GDP originally bound in the exchangeable site) retained by tubulin rose as reaction pH increased. Endogenous GDP was more efficiently displaced by exogenous GTP than GDP at all pH values, but displacement by GTP was 10-15% greater at pH 6 than at pH 7. Dissociation constants for GDP and GTP were about 1.0 microM at pH 6 and 0.02 microM at pH 7. A small increase in the affinity of GDP relative to that of GTP occurs at pH 7 as compared to pH 6, together with a 50-fold absolute increase in the affinity of both nucleotides for tubulin at pH 7. The time courses of microtubule assembly and GTP hydrolysis were compared at pH 6 and pH 7. At pH 6, the two reactions were simultaneous in onset and initially stoichiometric. At pH 7, although the reactions began simultaneously, hydrolysis seemed to lag substantially behind assembly. Unhydrolyzed radiolabeled GTP was not incorporated into microtubules, however, indicating that GTP hydrolysis is actually closely coupled to assembly. The apparent lag in hydrolysis probably results from a methodological artifact rather than incorporation of GTP into the microtubule with delayed hydrolysis.     

Learning to predict protein-protein interactions from protein sequences

In order to understand the molecular machinery of the cell, we need to know about the multitude of protein-protein interactions that allow the cell to function. High-throughput technologies provide some data about these interactions, but so far that data is fairly noisy. Therefore, computational techniques for predicting protein-protein interactions could be of significant value. One approach to predicting interactions in silico is to produce from first principles a detailed model of a candidate interaction. We take an alternative approach, employing a relatively simple model that learns dynamically from a large collection of data. In this work, we describe an attraction-repulsion model, in which the interaction between a pair of proteins is represented as the sum of attractive and repulsive forces associated with small, domain- or motif-sized features along the length of each protein. The model is discriminative, learning simultaneously from known interactions and from pairs of proteins that are known (or suspected) not to interact. The model is efficient to compute and scales well to very large collections of data. In a cross-validated comparison using known yeast interactions, the attraction-repulsion method performs better than several competing techniques.     

Mining literature for protein-protein interactions

MOTIVATION: A central problem in bioinformatics is how to capture information from the vast current scientific literature in a form suitable for analysis by computer. We address the special case of information on protein-protein interactions, and show that the frequencies of words in Medline abstracts can be used to determine whether or not a given paper discusses protein-protein interactions. For those papers determined to discuss this topic, the relevant information can be captured for the Database of Interacting PROTEINS: Furthermore, suitable gene annotations can also be captured. RESULTS: Our Bayesian approach scores Medline abstracts for probability of discussing the topic of interest according to the frequencies of discriminating words found in the abstract. More than 80 discriminating words (e.g. complex, interaction, two-hybrid) were determined from a training set of 260 Medline abstracts corresponding to previously validated entries in the Database of Interacting Proteins. Using these words and a log likelihood scoring function, approximately 2000 Medline abstracts were identified as describing interactions between yeast proteins. This approach now forms the basis for the rapid expansion of the Database of Interacting Proteins.     

A novel method to predict protein-protein interactions based on the information of protein-protein interaction networks and protein sequence

Protein-protein interactions (PPIs) are crucial to most biochemical processes in human beings. Although many human PPIs have been identified by experiments, the number is still limited compared to the available protein sequences of human organisms. Recently, many computational methods have been proposed to facilitate the recognition of novel human PPIs. However the existing methods only concentrated on the information of individual PPI, while the systematic characteristic of protein-protein interaction networks (PINs) was ignored. In this study, a new method was proposed by combining the global information of PINs and protein sequence information. Random forest (RF) algorithm was implemented to develop the prediction model, and a high accuracy of 91.88% was obtained. Furthermore, the RF model was tested using three independent datasets with good performances, suggesting that our method is a useful tool for identification of PPIs and investigation into PINs as well.     

Modulation of nociceptive ion channels and receptors via protein-protein interactions: implications for pain relief

In the last 2 decades biomedical research has provided great insights into the molecular signatures underlying painful conditions. However, chronic pain still imposes substantial challenges to researchers, clinicians and patients alike. Under pathological conditions, pain therapeutics often lack efficacy and exhibit only minimal safety profiles, which can be largely attributed to the targeting of molecules with key physiological functions throughout the body. In light of these difficulties, the identification of molecules and associated protein complexes specifically involved in chronic pain states is of paramount importance for designing selective interventions. Ion channels and receptors represent primary targets, as they critically shape nociceptive signaling from the periphery to the brain. Moreover, their function requires tight control, which is usually implemented by protein-protein interactions (PPIs). Indeed, manipulation of such PPIs entails the modulation of ion channel activity with widespread implications for influencing nociceptive signaling in a more specific way. In this review, we highlight recent advances in modulating ion channels and receptors via their PPI networks in the pursuit of relieving chronic pain. Moreover, we critically discuss the potential of targeting PPIs for developing novel pain therapies exhibiting higher efficacy and improved safety profiles.     

Modulation of nociceptive ion channels and receptors via protein-protein interactions: implications for pain relief

In the last 2 decades biomedical research has provided great insights into the molecular signatures underlying painful conditions. However, chronic pain still imposes substantial challenges to researchers, clinicians and patients alike. Under pathological conditions, pain therapeutics often lack efficacy and exhibit only minimal safety profiles, which can be largely attributed to the targeting of molecules with key physiological functions throughout the body. In light of these difficulties, the identification of molecules and associated protein complexes specifically involved in chronic pain states is of paramount importance for designing selective interventions. Ion channels and receptors represent primary targets, as they critically shape nociceptive signaling from the periphery to the brain. Moreover, their function requires tight control, which is usually implemented by protein-protein interactions (PPIs). Indeed, manipulation of such PPIs entails the modulation of ion channel activity with widespread implications for influencing nociceptive signaling in a more specific way. In this review, we highlight recent advances in modulating ion channels and receptors via their PPI networks in the pursuit of relieving chronic pain. Moreover, we critically discuss the potential of targeting PPIs for developing novel pain therapies exhibiting higher efficacy and improved safety profiles.     

Solid phase proteomics: dramatic reinforcement of very weak protein-protein interactions

Very weak protein-protein interactions may play a critical role in cell physiology but they are not easily detectable in "in vitro" experiments. To detect these weak interactions, we have developed a strategy that included: (a) design of a rapid and very effective crosslinking of protein-protein complexes with poly-functional reagents; (b) selective adsorption of very large proteins on lowly activated ionic exchangers, based on the need of a multipoint physical adsorption to incorporate the proteins into the matrix; (c) purification by selective adsorption of protein-protein complexes formed by strong protein-protein interactions, via selective adsorption of the complexes on lowly activated ionic exchangers via multi-protein physical adsorption and leaving the non-associated proteins in the solution; (d) reinforcement of very weak protein-protein interactions by selective adsorption of the complex on lowly activated ionic exchange supports via a synergetic cooperation of the weak protein-protein interaction plus the interactions of both proteins with the support enabling the almost full shifting of the equilibrium towards the association position; (e) control of the aggregation state of proteins like BSA, formed by weak protein-protein interactions. In this last case, it seems that the interaction of the protein molecules placed on the borders of the aggregate with the groups on the support partially stabilizes the whole aggregate, although, some molecules of the aggregate cannot interact with the support. The size of the aggregates may be defined by controlling the concentration of ionised groups on the support: the less activated the supports are, the bigger the complexes. In this way, solid-phase proteomics could be a very interesting tool to detect weak protein-protein interactions.     

Oligomeric protein structure networks: insights into protein-protein interactions

Background  

Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues) with special emphasis to protein interfaces.     

High-throughput and multiplexed protein array technology: protein-DNA and protein-protein interactions

Miniaturized protein arrays address protein interactions with various types of molecules in a high-throughput and multiplexed fashion. This review focuses on achievements in the analysis of protein-DNA and protein-protein interactions. The technological feasibility of protein arrays depends on the different factors that enable the arrayed proteins to recognize molecular partners and on the specificity of the interactions involved. Proteome-scale studies of molecular interactions require high-throughput approaches for both the production and purification of functionally active proteins. Various solutions have been proposed to avoid non-specific protein interactions on array supports and to monitor low-abundance molecules. The data accumulated indicate that this emerging technology is perfectly suited to resolve networks of protein interactions involved in complex physiological and pathological phenomena in different organisms and to develop sensitive tools for biomedical applications.     

Analysis on multi-domain cooperation for predicting protein-protein interactions

Background  

Domains are the basic functional units of proteins. It is believed that protein-protein interactions are realized through domain interactions. Revealing multi-domain cooperation can provide deep insights into the essential mechanism of protein-protein interactions at the domain level and be further exploited to improve the accuracy of protein interaction prediction.     

Prediction of protein-protein interactions: the CAPRI experiment, its evaluation and implications

Given the increasing interest in protein-protein interactions, the prediction of these interactions from sequence and structural information has become a booming activity. CAPRI, the community-wide experiment for assessing blind predictions of protein-protein interactions, is playing an important role in fostering progress in docking procedures. At the same time, novel methods are being derived for predicting regions of a protein that are likely to interact and for characterizing putative intermolecular contacts from sequence and structural data. Together with docking procedures, these methods provide an integrated computational approach that should be a valuable complement to genome-scale experimental studies of protein-protein interactions.     

Hydrophobic folding units at protein-protein interfaces: implications to protein folding and to protein-protein association.

A hydrophobic folding unit cutting algorithm, originally developed for dissecting single-chain proteins, has been applied to a dataset of dissimilar two-chain protein-protein interfaces. Rather than consider each individual chain separately, the two-chain complex has been treated as a single chain. The two-chain parsing results presented in this work show hydrophobicity to be a critical attribute of two-state versus three-state protein-protein complexes. The hydrophobic folding units at the interfaces of two-state complexes suggest that the cooperative nature of the two-chain protein folding is the outcome of the hydrophobic effect, similar to its being the driving force in a single-chain folding. In analogy to the protein-folding process, the two-chain, two-state model complex may correspond to the formation of compact, hydrophobic nuclei. On the other hand, the three-state model complex involves binding of already folded monomers, similar to the association of the hydrophobic folding units within a single chain. The similarity between folding entities in protein cores and in two-state protein-protein interfaces, despite the absence of some chain connectivities in the latter, indicates that chain linkage does not necessarily affect the native conformation. This further substantiates the notion that tertiary, non-local interactions play a critical role in protein folding. These compact, hydrophobic, two-chain folding units, derived from structurally dissimilar protein-protein interfaces, provide a rich set of data useful in investigations of the role played by chain connectivity and by tertiary interactions in studies of binding and of folding. Since they are composed of non-contiguous pieces of protein backbones, they may also aid in defining folding nuclei.