An empirical framework for binary interactome mapping

Mammalian two-hybrids come of age

A diverse series of mammalian two-hybrid technologies for the detection of protein-protein interactions have emerged in the past few years, complementing the established yeast two-hybrid approach. Given the mammalian background in which they operate, these assays open new avenues to study the dynamics of mammalian protein interaction networks, i.e. the temporal, spatial and functional modulation of protein-protein associations. In addition, novel assay formats are available that enable high-throughput mammalian two-hybrid applications, facilitating their use in large-scale interactome mapping projects. Finally, as they can be applied in drug discovery and development programs, these techniques also offer exciting new opportunities for biomedical research.

Systems biology: Untangling the protein web

Researchers have identified thousands of macromolecular interactions within cells. But, as Nathan Blow finds out, joining them up in networks and figuring out how they work still poses a big challenge.

Systems biology of stem cell fate and cellular reprogramming

Stem cell differentiation and the maintenance of self-renewal are intrinsically complex processes requiring the coordinated dynamic expression of hundreds of genes and proteins in precise response to external signalling cues. Numerous recent reports have used both experimental and computational techniques to dissect this complexity. These reports suggest that the control of cell fate has both deterministic and stochastic elements: complex underlying regulatory networks define stable molecular 'attractor' states towards which individual cells are drawn over time, whereas stochastic fluctuations in gene and protein expression levels drive transitions between coexisting attractors, ensuring robustness at the population level.

The human protein coevolution network

Coevolution maintains interactions between phenotypic traits through the process of reciprocal natural selection. Detecting molecular coevolution can expose functional interactions between molecules in the cell, generating insights into biological processes, pathways, and the networks of interactions important for cellular function. Prediction of interaction partners from different protein families exploits the property that interacting proteins can follow similar patterns and relative rates of evolution. Current methods for detecting coevolution based on the similarity of phylogenetic trees or evolutionary distance matrices have, however, been limited by requiring coevolution over the entire evolutionary history considered and are inaccurate in the presence of paralogous copies. We present a novel method for determining coevolving protein partners by finding the largest common submatrix in a given pair of distance matrices, with the size of the largest common submatrix measuring the strength of coevolution. This approach permits us to consider matrices of different size and scale, to find lineage-specific coevolution, and to predict multiple interaction partners. We used MatrixMatchMaker to predict protein-protein interactions in the human genome. We show that proteins that are known to interact physically are more strongly coevolving than proteins that simply belong to the same biochemical pathway. The human coevolution network is highly connected, suggesting many more protein-protein interactions than are currently known from high-throughput and other experimental evidence. These most strongly coevolving proteins suggest interactions that have been maintained over long periods of evolutionary time, and that are thus likely to be of fundamental importance to cellular function.

Connecting viral with cellular interactomes

Genome-scale screens for intraviral and virus–host protein interactions and the analysis of literature-curated datasets are able to provide a novel, comprehensive perspective of viruses, and virus-infected cells. Until now, large-scale interaction screens were predominantly performed with the yeast-two-hybrid (Y2H) system; however, alternative high-throughput technologies detecting binary protein interactions or protein complexes have been developed. Although many of the previous studies suffer from a rather poor validation of the results and few biological implications, these technologies potentially lead to a plethora of novel hypotheses. Here, we will give an overview of current approaches and their technical limitations, present recent examples and novel developments.

Sequence, structure, function, immunity: structural genomics of costimulation

SUMMARY

Costimulatory receptors and ligands trigger the signaling pathways that are responsible for modulating the strength, course, and duration of an immune response. High-resolution structures have provided invaluable mechanistic insights by defining the chemical and physical features underlying costimulatory receptor:ligand specificity, affinity, oligomeric state, and valency. Furthermore, these structures revealed general architectural features that are important for the integration of these interactions and their associated signaling pathways into overall cellular physiology. Recent technological advances in structural biology promise unprecedented opportunities for furthering our understanding of the structural features and mechanisms that govern costimulation. In this review, we highlight unique insights that have been revealed by structures of costimulatory molecules from the immunoglobulin and tumor necrosis factor superfamilies and describe a vision for future structural and mechanistic analysis of costimulation. This vision includes simple strategies for the selection of candidate molecules for structure determination and highlights the critical role of structure in the design of mutant costimulatory molecules for the generation of in vivo structure-function correlations in a mammalian model system. This integrated 'atoms-to-animals' paradigm provides a comprehensive approach for defining atomic and molecular mechanisms.

Triangle network motifs predict complexes by complementing high-error interactomes with structural information

Background

A lot of high-throughput studies produce protein-protein interaction networks (PPINs) with many errors and missing information. Even for genome-wide approaches, there is often a low overlap between PPINs produced by different studies. Second-level neighbors separated by two protein-protein interactions (PPIs) were previously used for predicting protein function and finding complexes in high-error PPINs. We retrieve second level neighbors in PPINs, and complement these with structural domain-domain interactions (SDDIs) representing binding evidence on proteins, forming PPI-SDDI-PPI triangles.

Results

We find low overlap between PPINs, SDDIs and known complexes, all well below 10%. We evaluate the overlap of PPI-SDDI-PPI triangles with known complexes from Munich Information center for Protein Sequences (MIPS). PPI-SDDI-PPI triangles have ~20 times higher overlap with MIPS complexes than using second-level neighbors in PPINs without SDDIs. The biological interpretation for triangles is that a SDDI causes two proteins to be observed with common interaction partners in high-throughput experiments. The relatively few SDDIs overlapping with PPINs are part of highly connected SDDI components, and are more likely to be detected in experimental studies. We demonstrate the utility of PPI-SDDI-PPI triangles by reconstructing myosin-actin processes in the nucleus, cytoplasm, and cytoskeleton, which were not obvious in the original PPIN. Using other complementary datatypes in place of SDDIs to form triangles, such as PubMed co-occurrences or threading information, results in a similar ability to find protein complexes.

Conclusion

Given high-error PPINs with missing information, triangles of mixed datatypes are a promising direction for finding protein complexes. Integrating PPINs with SDDIs improves finding complexes. Structural SDDIs partially explain the high functional similarity of second-level neighbors in PPINs. We estimate that relatively little structural information would be sufficient for finding complexes involving most of the proteins and interactions in a typical PPIN.

The first draft of the endostatin interaction network

Endostatin is a C-terminal proteolytic fragment of collagen XVIII that is localized in vascular basement membrane zones in various organs. It binds to heparin/heparan sulfate and to a number of proteins, but its molecular mechanisms of action are not fully elucidated. We have used surface plasmon resonance (SPR) arrays to identify new partners of endostatin, and to give further insights on its molecular mechanism of action. New partners of endostatin include glycosaminoglycans (chondroitin and dermatan sulfate), matricellular proteins (thrombospondin-1 and SPARC), collagens (I, IV, and VI), the amyloid peptide Abeta-(1-42), and transglutaminase-2. The biological functions of the endostatin network involve a number of extracellular proteins containing epidermal growth factor and epidermal growth factor-like domains, and able to bind calcium. Depending on the trigger event, and on the availability of its members in a given tissue at a given time, the endostatin network might be involved either in the control of angiogenesis, and tumor growth, or in neurogenesis and neurodegenerative diseases.

DASMIweb: online integration, analysis and assessment of distributed protein interaction data

In recent years, we have witnessed a substantial increase of the amount of available protein interaction data. However, most data are currently not readily accessible to the biologist at a single site, but scattered over multiple online repositories. Therefore, we have developed the DASMIweb server that affords the integration, analysis and qualitative assessment of distributed sources of interaction data in a dynamic fashion. Since DASMIweb allows for querying many different resources of protein and domain interactions simultaneously, it serves as an important starting point for interactome studies and assists the user in finding publicly accessible interaction data with minimal effort. The pool of queried resources is fully configurable and supports the inclusion of own interaction data or confidence scores. In particular, DASMIweb integrates confidence measures like functional similarity scores to assess individual interactions. The retrieved results can be exported in different file formats like MITAB or SIF. DASMIweb is freely available at http://www.dasmiweb.de.

Shifted Transversal Design smart-pooling for high coverage interactome mapping

"Smart-pooling," in which test reagents are multiplexed in a highly redundant manner, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. However, previous applications relied on low redundancy designs that do not leverage the full potential of smart-pooling, and more powerful theoretical constructions, such as the Shifted Transversal Design (STD), lack experimental validation. Here we evaluate STD smart-pooling in yeast two-hybrid (Y2H) interactome mapping. We employed two STD designs and two established methods to perform ORFeome-wide Y2H screens with 12 baits. We found that STD pooling achieves similar levels of sensitivity and specificity as one-on-one array-based Y2H, while the costs and workloads are divided by three. The screening-sequencing approach is the most cost- and labor-efficient, yet STD identifies about twofold more interactions. Screening-sequencing remains an appropriate method for quickly producing low-coverage interactomes, while STD pooling appears as the method of choice for obtaining maps with higher coverage.

A network medicine approach to human disease

High-throughput interaction discovery initiatives are providing thousands of novel protein interactions which are unveiling many unexpected links between apparently unrelated biological processes. In particular, analyses of the first draft human interactomes highlight a strong association between protein network connectivity and disease. Indeed, recent exciting studies have exploited the information contained within protein networks to disclose some of the molecular mechanisms underlying complex pathological processes. These findings suggest that both protein-protein interactions and the networks themselves could emerge as a new class of targetable entities, boosting the quest for novel therapeutic strategies.

Auditory proteomics: methods, accomplishments and challenges

The advent of contemporary proteomic technologies has ushered in definite advances to the field of auditory research and has provided the potential for a dramatic increase in applications in the near future. Two dimensional-differential gel electrophoresis (2D-DIGE) followed by matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), antibody microarrays and tandem mass spectrometry have evolved as the major tools. Each of these techniques has unique features with distinct advantages. This review attempts to highlight the common as well as diverse characteristics of these methods and their suitability and application to different experimental conditions employed to investigate the auditory system. In addition a glimpse of the valuable scientific information that has been gained in the hearing field using a proteomic approach is given. Finally, a brief view of the directions that auditory proteomics research is headed for has been discussed.

Literature-curated protein interaction datasets

High quality datasets are needed to understand how global and local properties of protein-protein interaction, or “interactome”, networks relate to biological mechanisms, and to guide research on individual proteins. Evaluations of existing curation of protein interaction experiments reported in the literature find that curation can be error prone and possibly of lower quality than commonly assumed.

Empirically controlled mapping of the Caenorhabditis elegans protein-protein interactome network

To provide accurate biological hypotheses and elucidate global properties of cellular networks, systematic identification of protein-protein interactions must meet high quality standards.We present an expanded C. elegans protein-protein interaction network, or 'interactome' map, derived from testing a matrix of approximately 10,000 x approximately 10,000 proteins using a highly specific, high-throughput yeast two-hybrid system. Through a new empirical quality control framework, we show that the resulting data set (Worm Interactome 2007, or WI-2007) was similar in quality to low-throughput data curated from the literature. We filtered previous interaction data sets and integrated them with WI-2007 to generate a high-confidence consolidated map (Worm Interactome version 8, or WI8). This work allowed us to estimate the size of the worm interactome at approximately 116,000 interactions. Comparison with other types of functional genomic data shows the complementarity of distinct experimental approaches in predicting different functional relationships between genes or proteins

An experimentally derived confidence score for binary protein-protein interactions

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

Characteristics of the binding of aminoglycoside antibiotics to teichoic acids. A potential model system for interaction of aminoglycosides with polyanions

The binding of the aminoglycoside antibiotic dihydrostreptomycin to defined cell-wall teichoic acids and to lipoteichoic acid isolated from various gram-positive eubacteria was followed by equilibrium dialysis. Dihydrostreptomycin was used at a wide range of concentration under different conditions of ionic strength, concentration of teichoic acid, presence of cationic molecules like Mg2+, spermidine, other aminoglycoside antibiotics (gentamicin, neomycin, paromomycin). Interaction of dihydrostreptomycin with teichoic acid was found to be a cooperative binding process. The binding characteristics seem to be dependent on structural features of teichoic acid and are influenced by cationic molecules. Mg2+, spermidine and other aminoglycosides antibiotics inhibit the binding of dihydrostreptomycin to teichoic acid competitively. The binding of aminoglycosides to teichoic acids is considered as a model system for the interaction of aminoglycoside antibiotics with cellular polyanions. Conclusions of physiological significance are drawn.