In vivo protein complex topologies: sights through a cross-linking lens

Proteins are a remarkable class of molecules that exhibit wide diversity of shapes or topological features that underpin protein interactions and give rise to biological function. In addition to quantitation of abundance levels of proteins in biological systems under a variety of conditions, the field of proteome research has as a primary mission the assignment of function for proteins and if possible, illumination of factors that enable function. For many years, chemical cross-linking methods have been used to provide structural data on single purified proteins and purified protein complexes. However, these methods also offer the alluring possibility to extend capabilities to complex biological samples such as cell lysates or intact living cells where proteins may exhibit native topological features that do not exist in purified form. Recent efforts are beginning to provide glimpses of protein complexes and topologies in cells that suggest continued development will yield novel capabilities to view functional topological features of many proteins and complexes as they exist in cells, tissues, or other complex samples. This review will describe rationale, challenges, and a few success stories along the path of development of cross-linking technologies for measurement of in vivo protein interaction topologies.     

In vivo application of photocleavable protein interaction reporter technology

In vivo protein structures and protein-protein interactions are critical to the function of proteins in biological systems. As a complementary approach to traditional protein interaction identification methods, cross-linking strategies are beginning to provide additional data on protein and protein complex topological features. Previously, photocleavable protein interaction reporter (pcPIR) technology was demonstrated by cross-linking pure proteins and protein complexes and the use of ultraviolet light to cleave or release cross-linked peptides to enable identification. In the present report, the pcPIR strategy is applied to Escherichia coli cells, and in vivo protein interactions and topologies are measured. More than 1600 labeled peptides from E. coli were identified, indicating that many protein sites react with pcPIR in vivo. From those labeled sites, 53 in vivo intercross-linked peptide pairs were identified and manually validated. Approximately half of the interactions have been reported using other techniques, although detailed structures exist for very few. Three proteins or protein complexes with detailed crystallography structures are compared to the cross-linking results obtained from in vivo application of pcPIR technology.     

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.In addressing and solving these problems we present a novel random walk based algorithm that converts the incomplete and binary PPI network into a protein-protein topological similarity matrix (PP-TS matrix). We believe that if two proteins share some high-order topological similarities they are likely to be interacting with each other. Using the obtained PP-TS matrix, we constructed and used weighted networks to further study and analyze the interaction among proteins. Specifically, we applied a fully automated community structure finding algorithm (Auto-HQcut) on the obtained weighted network to cluster protein complexes. We then analyzed the protein complexes for significance in biological processes. To help visualize and analyze these protein complexes we also developed an interface that displays the resulting complexes as well as the characteristics associated with each complex.Applying our approach to a yeast protein-protein interaction network, we found that the predicted protein-protein interaction pairs with high topological similarities have more significant biological relevance than the original protein-protein interactions pairs. When we compared our PPI network reconstruction algorithm with other existing algorithms using gene ontology and gene co-expression, our algorithm produced the highest similarity scores. Also, our predicted protein complexes showed higher accuracy measure compared to the other protein complex predictions.     

Molecular dynamics shows complex interplay and long-range effects of post-translational modifications in yeast protein interactions

Post-translational modifications (PTMs) play a vital, yet often overlooked role in the living cells through modulation of protein properties, such as localization and affinity towards their interactors, thereby enabling quick adaptation to changing environmental conditions. We have previously benchmarked a computational framework for the prediction of PTMs’ effects on the stability of protein-protein interactions, which has molecular dynamics simulations followed by free energy calculations at its core. In the present work, we apply this framework to publicly available data on Saccharomyces cerevisiae protein structures and PTM sites, identified in both normal and stress conditions. We predict proteome-wide effects of acetylations and phosphorylations on protein-protein interactions and find that acetylations more frequently have locally stabilizing roles in protein interactions, while the opposite is true for phosphorylations. However, the overall impact of PTMs on protein-protein interactions is more complex than a simple sum of local changes caused by the introduction of PTMs and adds to our understanding of PTM cross-talk. We further use the obtained data to calculate the conformational changes brought about by PTMs. Finally, conservation of the analyzed PTM residues in orthologues shows that some predictions for yeast proteins will be mirrored to other organisms, including human. This work, therefore, contributes to our overall understanding of the modulation of the cellular protein interaction networks in yeast and beyond.     

Pairwise interactions of the six human MCM protein subunits

The eukaryotic minichromosome maintenance (MCM) proteins have six subunits, Mcm2 to 7p. Together they play essential roles in the initiation and elongation of DNA replication, and the human MCM proteins present attractive targets for potential anticancer drugs. The six MCM subunits interact and form a ring-shaped heterohexameric complex containing one of each subunit in a variety of eukaryotes, and subcomplexes have also been observed. However, the architecture of the human MCM heterohexameric complex is still unknown. We systematically studied pairwise interactions of individual human MCM subunits by using the yeast two-hybrid system and in vivo protein-protein crosslinking with a non-cleavable crosslinker in human cells followed by co-immunoprecipitation. In the yeast two-hybrid assays, we revealed multiple binary interactions among the six human MCM proteins, and a subset of these interactions was also detected as direct interactions in human cells. Based on our results, we propose a model for the architecture of the human MCM protein heterohexameric complex. We also propose models for the structures of subcomplexes. Thus, this study may serve as a foundation for understanding the overall architecture and function of eukaryotic MCM protein complexes and as clues for developing anticancer drugs targeted to the human MCM proteins.     

Voltage-dependent anion-selective channel (VDAC) interacts with the dynein light chain Tctex1 and the heat-shock protein PBP74

The voltage-dependent anion-selective channel 1 (VDAC1), i.e. eukaryotic porin, functions as a channel in membranous structures as described for the outer mitochondrial membrane, the cell membrane, endosomes, caveolae, the sarcoplasmatic reticulum, synaptosomes, and post-synaptic density fraction.The identification of VDAC1 interacting proteins may be a promising approach for better understanding the biological context and function of the channel protein. In this study human VDAC1 was used as a bait protein in a two-hybrid screening, which is based on the Sos recruitment system (SRS). hVDAC1 interacts with the dynein light chain Tctex-1 and the heat-shock protein peptide-binding protein 74 (PBP74)/mitochondrial heat-shock protein 70 (mtHSP70)/glucose-regulated protein 75 (GRP75)/mortalin in vivo. Both interactions were confirmed by overlay-assays using recombinant partner proteins and purified hVDAC1. Indirect immunofluorescence on HeLa cells indicates a co-localisation of hVDAC1 with the dynein light chain and the PBP74. In addition, HeLa cells were transfected transiently with enhanced green fluorescent protein (EGFP)-hVDAC1 fusion proteins, which also clearly co-localise with both proteins. The functional relevance of the identified protein interactions was analysed in planar lipid bilayer (PLB) experiments. In these experiments both recombinant binding partners altered the electrophysiological properties of hVDAC1. While rTctex-1 increases the voltage-dependence of hVDAC1 slightly, the rPBP74 drastically minimises the voltage-dependence, indicating a modulation of channel properties in each case. Since the identified proteins are known to be involved in the transport or processing of proteins, the results of this study represent additional evidence of membrane-associated trafficking of the voltage-dependent anion-selective channel 1.     

Protein complex detection with semi-supervised learning in protein interaction networks

Background

Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes. The systematic analysis of PPI networks can enable a great understanding of cellular organization, processes and function. In this paper, we investigate the problem of protein complex detection from noisy protein interaction data, i.e., finding the subsets of proteins that are closely coupled via protein interactions. However, protein complexes are likely to overlap and the interaction data are very noisy. It is a great challenge to effectively analyze the massive data for biologically meaningful protein complex detection.

Results

Many people try to solve the problem by using the traditional unsupervised graph clustering methods. Here, we stand from a different point of view, redefining the properties and features for protein complexes and designing a “semi-supervised” method to analyze the problem. In this paper, we utilize the neural network with the “semi-supervised” mechanism to detect the protein complexes. By retraining the neural network model recursively, we could find the optimized parameters for the model, in such a way we can successfully detect the protein complexes. The comparison results show that our algorithm could identify protein complexes that are missed by other methods. We also have shown that our method achieve better precision and recall rates for the identified protein complexes than other existing methods. In addition, the framework we proposed is easy to be extended in the future.

Conclusions

Using a weighted network to represent the protein interaction network is more appropriate than using a traditional unweighted network. In addition, integrating biological features and topological features to represent protein complexes is more meaningful than using dense subgraphs. Last, the “semi-supervised” learning model is a promising model to detect protein complexes with more biological and topological features available.

     

Noninvasive imaging of protein-protein interactions in living organisms

Genomic research is expected to generate new types of complex observational data, changing the types of experiments as well as our understanding of biological processes. The investigation and definition of relationships among proteins is essential for understanding the function of each gene and the mechanisms of biological processes that specific genes are involved in. Recently, a study by Paulmurugan et al. demonstrated a tool for in vivo noninvasive imaging of protein-protein interactions and intracellular networks.     

Knobs, knob proteins and cytoadherence in falciparum malaria.

1. The sequestration of trophozoite and schizont infected erythrocytes (IRBC) in post-capillary venules of host internal organs causes most of the morbidity and mortality in falciparum malaria. It is a knob mediated cytoadherence phenomenon where knobs act as the focal junction between IRBC and host endothelial cell. Knobless (K-) parasites, isolated from cultures (not yet isolated from in vivo), do not cause virulent infections. Knobs thus play an important role in pathophysiology of falciparum malaria. 2. The chemical composition of knobs is partly explored, several proteins (Known as knob proteins) have been identified. According to their function they can be classified as (a) knob-inducing protein, "KAHRP" (b) knob-associated cytoadherent proteins, e.g. PFEMP-1, modified band 3 and an antigen recognized by monoclonal 33G2 and (c) knob-associated structural protein, e.g. PFEMP-2/MESA/PP-300. Most of them show size polymorphism among different isolates. Only KAHRP and MESA/PFEMP-2 have been studied at molecular level. Their chromosomal locations have been identified such as KAHRP on chromosome 2 and MESA/PFEMP-2 on chromosomes 5 and 6. 3. The receptor molecules on endothelial cells for knob ligands have been identified and partially characterized. 4. Knob ligands and their receptor molecules can play an important role in developing the immunotherapeutic reagents. 5. Based on the available data a tentative hypothesis has been proposed about the loss of knobs in vitro. Nevertheless, this needs further support from other experimental evidence. 6. Future work should be directed towards the structure and function of knob proteins and their interactions with each other as well as with host proteins. Regulation of expression of knobs and knob protein(s), evaluation of knob antigens for immunotherapy of severe falciparum malaria and for a malaria vaccine also require further investigations.     

Systems for the detection and analysis of protein–protein interactions

The analysis of protein–protein interactions is important for developing a better understanding of the functional annotations of proteins that are involved in various biochemical reactions in vivo. The discovery that a protein with an unknown function binds to a protein with a known function could provide a significant clue to the cellular pathway concerning the unknown protein. Therefore, information on protein–protein interactions obtained by the comprehensive analysis of all gene products is available for the construction of interactive networks consisting of individual protein–protein interactions, which, in turn, permit elaborate biological phenomena to be understood. Systems for detecting protein–protein interactions in vitro and in vivo have been developed, and have been modified to compensate for limitations. Using these novel approaches, comprehensive and reliable information on protein–protein interactions can be determined. Systems that permit this to be achieved are described in this review.K. Kuroda, M. Kato and J. Mima contributed equally to this work.     

3D complex: a structural classification of protein complexes

Most of the proteins in a cell assemble into complexes to carry out their function. It is therefore crucial to understand the physicochemical properties as well as the evolution of interactions between proteins. The Protein Data Bank represents an important source of information for such studies, because more than half of the structures are homo- or heteromeric protein complexes. Here we propose the first hierarchical classification of whole protein complexes of known 3-D structure, based on representing their fundamental structural features as a graph. This classification provides the first overview of all the complexes in the Protein Data Bank and allows nonredundant sets to be derived at different levels of detail. This reveals that between one-half and two-thirds of known structures are multimeric, depending on the level of redundancy accepted. We also analyse the structures in terms of the topological arrangement of their subunits and find that they form a small number of arrangements compared with all theoretically possible ones. This is because most complexes contain four subunits or less, and the large majority are homomeric. In addition, there is a strong tendency for symmetry in complexes, even for heteromeric complexes. Finally, through comparison of Biological Units in the Protein Data Bank with the Protein Quaternary Structure database, we identified many possible errors in quaternary structure assignments. Our classification, available as a database and Web server at http://www.3Dcomplex.org, will be a starting point for future work aimed at understanding the structure and evolution of protein complexes.     

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.     

Assortative mixing in Protein Contact Networks and protein folding kinetics

MOTIVATION: Starting from linear chains of amino acids, the spontaneous folding of proteins into their elaborate 3D structures is one of the remarkable examples of biological self-organization. We investigated native state structures of 30 single-domain, two-state proteins, from complex networks perspective, to understand the role of topological parameters in proteins' folding kinetics, at two length scales--as 'Protein Contact Networks (PCNs)' and their corresponding 'Long-range Interaction Networks (LINs)' constructed by ignoring the short-range interactions. RESULTS: Our results show that, both PCNs and LINs exhibit the exceptional topological property of 'assortative mixing' that is absent in all other biological and technological networks studied so far. We show that the degree distribution of these contact networks is partly responsible for the observed assortativity. The coefficient of assortativity also shows a positive correlation with the rate of protein folding at both short- and long-contact scale, whereas, the clustering coefficients of only the LINs exhibit a negative correlation. The results indicate that the general topological parameters of these naturally evolved protein networks can effectively represent the structural and functional properties required for fast information transfer among the residues facilitating biochemical/kinetic functions, such as, allostery, stability and the rate of folding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Piecing together the type III injectisome of bacterial pathogens

The Type III secretion system is a bacterial 'injectisome' which allows Gram-negative bacteria to shuttle virulence proteins directly into the host cells they infect. This macromolecular assembly consists of more than 20 different proteins put together to collectively span three biological membranes. The recent T3SS crystal structures of the major oligomeric inner membrane ring, the helical needle filament, needle tip protein, the associated ATPase, and outer membrane pilotin together with electron microscopy reconstructions have dramatically furthered our understanding of how this protein translocator functions. The crucial details that describe how these proteins assemble into this oligomeric complex will need a hybrid of structural methodologies including EM, crystallography, and NMR to clarify the intra- and inter-molecular interactions between different structural components of the apparatus.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.     

Classifying RNA-binding proteins based on electrostatic properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein-protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs.     

Host pathogen protein interactions predicted by comparative modeling

Pathogens have evolved numerous strategies to infect their hosts, while hosts have evolved immune responses and other defenses to these foreign challenges. The vast majority of host-pathogen interactions involve protein-protein recognition, yet our current understanding of these interactions is limited. Here, we present and apply a computational whole-genome protocol that generates testable predictions of host-pathogen protein interactions. The protocol first scans the host and pathogen genomes for proteins with similarity to known protein complexes, then assesses these putative interactions, using structure if available, and, finally, filters the remaining interactions using biological context, such as the stage-specific expression of pathogen proteins and tissue expression of host proteins. The technique was applied to 10 pathogens, including species of Mycobacterium, apicomplexa, and kinetoplastida, responsible for "neglected" human diseases. The method was assessed by (1) comparison to a set of known host-pathogen interactions, (2) comparison to gene expression and essentiality data describing host and pathogen genes involved in infection, and (3) analysis of the functional properties of the human proteins predicted to interact with pathogen proteins, demonstrating an enrichment for functionally relevant host-pathogen interactions. We present several specific predictions that warrant experimental follow-up, including interactions from previously characterized mechanisms, such as cytoadhesion and protease inhibition, as well as suspected interactions in hypothesized networks, such as apoptotic pathways. Our computational method provides a means to mine whole-genome data and is complementary to experimental efforts in elucidating networks of host-pathogen protein interactions.     

Construction and Analyses of Human Large-Scale Tissue Specific Networks

Construction and analyses of tissue specific networks is crucial to unveil the function and organizational structure of biological systems. As a direct method to detect protein dynamics, human proteome-wide expression data provide an valuable resource to investigate the tissue specificity of proteins and interactions. By integrating protein expression data with large-scale interaction network, we constructed 30 tissue/cell specific networks in human and analyzed their properties and functions. Rather than the tissue specificity of proteins, we mainly focused on the tissue specificity of interactions to distill tissue specific networks. Through comparing our tissue specific networks with those inferred from gene expression data, we found our networks have larger scales and higher reliability. Furthermore, we investigated the similar extent of multiple tissue specific networks, which proved that tissues with similar functions tend to contain more common interactions. Finally, we found that the tissue specific networks differed from the static network in multiple topological properties. The proteins in tissue specific networks are interacting looser and the hubs play more important roles than those in the static network.