Applications of Protein Fragment Complementation Assays for Analyzing Biomolecular Interactions and Biochemical Networks in Living Cells.

Protein-protein interactions (PPIs) are indispensable for the dynamic assembly of multiprotein complexes that are central players of nearly all of the intracellular biological processes, such as signaling pathways, metabolic pathways, formation of intracellular organelles, establishment of cytoplasmic skeletons, etc. Numerous approaches have been invented to study PPIs both in vivo and in vitro, including the protein-fragment complementation assay (PCA), which is a widely applied technology to study PPIs and biomolecular interactions. PCA is a technology based on the expression of the bait and prey proteins in fusion with two complementary reporter protein fragments, respectively, that will reassemble when in close proximity. The reporter protein can be the enzymes or fluorescent proteins. Recovery of the enzymatic activity or fluorescent signal can be the indicator of PPI between the bait and prey proteins. Significant effort has been invested in developing many derivatives of PCA, along with various applications, in order to address specific questions. Therefore, a prompt review of these applications is important. In this review, we will categorize these applications according to the scenarios that the PCAs were applied and expect to provide a reference guideline for the future selection of PCA methods in solving a specific problem.

Cross-Linking/Mass Spectrometry for Studying Protein Structures and Protein-Protein Interactions: Where Are We Now and Where Should We Go from Here?

Structural mass spectrometry (MS) is gaining increasing importance for deriving valuable three-dimensional structural information on proteins and protein complexes, and it complements existing techniques, such as NMR spectroscopy and X-ray crystallography. Structural MS unites different MS-based techniques, such as hydrogen/deuterium exchange, native MS, ion-mobility MS, protein footprinting, and chemical cross-linking/MS, and it allows fundamental questions in structural biology to be addressed. In this Minireview, I will focus on the cross-linking/MS strategy. This method not only delivers tertiary structural information on proteins, but is also increasingly being used to decipher protein interaction networks, both in vitro and in vivo. Cross-linking/MS is currently one of the most promising MS-based approaches to derive structural information on very large and transient protein assemblies and intrinsically disordered proteins.

Current innovations and future challenges of network motif detection.

Network motif detection is the search for statistically overrepresented subgraphs present in a larger target network. They are thought to represent key structure and control mechanisms. Although the problem is exponential in nature, several algorithms and tools have been developed for efficiently detecting network motifs. This work analyzes 11 network motif detection tools and algorithms. Detailed comparisons and insightful directions for using these tools and algorithms are discussed. Key aspects of network motif detection are investigated. Network motif types and common network motifs as well as their biological functions are discussed. Applications of network motifs are also presented. Finally, the challenges, future improvements and future research directions for network motif detection are also discussed.

Protein-protein interaction predictions using text mining methods.

It is beyond any doubt that proteins and their interactions play an essential role in most complex biological processes. The understanding of their function individually, but also in the form of protein complexes is of a great importance. Nowadays, despite the plethora of various high-throughput experimental approaches for detecting protein-protein interactions, many computational methods aiming to predict new interactions have appeared and gained interest. In this review, we focus on text-mining based computational methodologies, aiming to extract information for proteins and their interactions from public repositories such as literature and various biological databases. We discuss their strengths, their weaknesses and how they complement existing experimental techniques by simultaneously commenting on the biological databases which hold such information and the benchmark datasets that can be used for evaluating new tools.

Searching for the Holy Grail; protein-protein interaction analysis and modulation.

The first EMBO workshop on 'Protein-Protein Interaction Analysis & Modulation' took place in June 2012 in Roscoff, France. It brought together researchers to discuss the growing field of protein network analysis and the modulation of protein-protein interactions, as well as outstanding related issues including the daunting challenge of integrating interactomes in systems biology and in the modelling of signalling networks.

Split-luciferase complementary assay: applications, recent developments, and future perspectives.

Bioluminescent systems are considered as potent reporter systems for bioanalysis since they have specific characteristics, such as relatively high quantum yields and photon emission over a wide range of colors from green to red. Biochemical events are mostly accomplished through large protein machines. These molecular complexes are built from a few to many proteins organized through their interactions. These protein-protein interactions are vital to facilitate the biological activity of cells. The split-luciferase complementation assay makes the study of two or more interacting proteins possible. In this technique, each of the two domains of luciferase is attached to each partner of two interacting proteins. On interaction of those proteins, luciferase fragments are placed close to each other and form a complemented luciferase, which produces a luminescent signal. Split luciferase is an effective tool for assaying biochemical metabolites, where a domain or an intact protein is inserted into an internally fragmented luciferase, resulting in ligand binding, which causes a change in the emitted signals. We review the various applications of this novel luminescent biosensor in studying protein-protein interactions and assaying metabolites involved in analytical biochemistry, cell communication and cell signaling, molecular biology, and the fate of the whole cell, and show that luciferase-based biosensors are powerful tools that can be applied for diagnostic and therapeutic purposes.

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.

[Recent advances in the techniques of protein-protein interaction study].

Protein-protein interactions play key roles in the development of organisms and the response to biotic and abiotic stresses. Several wet-lab methods have been developed to study this challenging area,including yeast two-hybrid system, tandem affinity purification, Co-immunoprecipitation, GST Pull-down, bimolecular fluorescence complementation, fluorescence resonance energy transfer and surface plasmon resonance analysis. In this review, we discuss theoretical principles and relative advantages and disvantages of these techniques,with an emphasis on recent advances to compensate for limitations.

Isoenergetic microarrays to study the structure and interactions of DsrA and OxyS RNAs in two- and three-component complexes.

Information on the secondary structure and interactions of RNA is important to understand the biological function of RNA as well as in applying RNA as a tool for therapeutic purposes. Recently, the isoenergetic microarray mapping method was developed to improve the prediction of RNA secondary structure. Herein, for the first time, isoenergetic microarrays were used to study the binding of RNA to protein or other RNAs as well as the interactions of two different RNAs and protein in a three-component complex. The RNAs used as models were the regulatory DsrA and OxyS RNAs from Escherichia coli, the fragments of their target mRNAs (fhlA and rpoS), and their complexes with Hfq protein. The collected results showed the advantages and some limitations of microarray mapping.

Present and future challenges and limitations in protein-protein docking.

The study of protein-protein interactions that are involved in essential life processes can largely benefit from the recent upraising of computational docking approaches. Predicting the structure of a protein-protein complex from their separate components is still a highly challenging task, but the field is rapidly improving. Recent advances in sampling algorithms and rigid-body scoring functions allow to produce, at least for some cases, high quality docking models that are perfectly suitable for biological and functional annotations, as it has been shown in the CAPRI blind tests. However, important challenges still remain in docking prediction. For example, in cases with significant mobility, such as multidomain proteins, fully unrestricted rigid-body docking approaches are clearly insufficient so they need to be combined with restraints derived from domain-domain linker residues, evolutionary information, or binding site predictions. Other challenging cases are weak or transient interactions, such as those between proteins involved in electron transfer, where the existence of alternative bound orientations and encounter complexes complicates the binding energy landscape. Docking methods also struggle when using in silico structural models for the interacting subunits. Bringing these challenges to a practical point of view, we have studied here the limitations of our docking and energy-based scoring approach, and have analyzed different parameters to overcome the limitations and improve the docking performance. For that, we have used the standard benchmark and some practical cases from CAPRI. Based on these results, we have devised a protocol to estimate the success of a given docking run.

Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes.

The study of metabolic pathways is becoming increasingly important to exploit an integrated, systems-level approach for optimizing a desired cellular property or phenotype. In this context, the integration of genomics data with genetic, metabolic and regulatory models is essential because the systematic design of artificial, biological systems requires the identification of robust building blocks like gene promoters, metabolic pathways or genetic circuits taken from natural organisms, and manipulated to develop ad hoc features. Computational tools allowing precise descriptions of natural pathways might thus allow improving the performance of artificial routes. In this review, we introduce the most recent bioinformatics tools enabling detailed characterizations of metabolic pathways in bacteria from different perspectives.

A more complete, complexed and structured interactome.

Multiprotein complexes are key players in virtually all important cellular processes. The past year has seen the publication of several papers that have illuminated what we know about the number and composition of these molecular machines, using high-throughput purification methods. Other studies have illuminated structural and functional aspects of protein interactions, networks and molecular assemblies. As a result, we have a more complete view of how many complexes are in living systems, what they look like and the roles they play in the cell.

Native electrophoretic techniques to identify protein-protein interactions.

Permanent protein-protein interactions are commonly identified by co-purification of two or more protein components using techniques like co-immunoprecipitation, tandem affinity purification and native electrophoresis. Here we focus on blue-native electrophoresis, clear-native electrophoresis, high-resolution clear-native electrophoresis and associated techniques to identify stable membrane protein complexes and detergent-labile physiological supercomplexes. Hints for dynamic protein-protein interactions can be obtained using two-hybrid techniques but not from native electrophoresis and other protein isolation techniques except after covalent cross-linking of interacting proteins in vivo prior to protein separation.

Current progress in computational metabolomics.

Being a relatively new addition to the 'omics' field, metabolomics is still evolving its own computational infrastructure and assessing its own computational needs. Due to its strong emphasis on chemical information and because of the importance of linking that chemical data to biological consequences, metabolomics must combine elements of traditional bioinformatics with traditional cheminformatics. This is a significant challenge as these two fields have evolved quite separately and require very different computational tools and skill sets. This review is intended to familiarize readers with the field of metabolomics and to outline the needs, the challenges and the recent progress being made in four areas of computational metabolomics: (i) metabolomics databases; (ii) metabolomics LIMS; (iii) spectral analysis tools for metabolomics and (iv) metabolic modeling.

Current progress in network research: toward reference networks for key model organisms.

The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.

Modulation of biomolecular interactions with complex-binding small molecules.

Although numerous biomolecular interactions have been identified as potential targets for the development of therapeutic agents, modulation of these interactions with small molecules has historically been considered an extremely difficult task. This difficulty is largely due to the low effectiveness of the traditionally used competitive approaches in which inhibitors are designed and screened for their ability to block biomolecules from establishing contacts with their targets. We propose a novel approach to modulate biomolecular interactions by de novo structure-based design of noncompetitive small molecules that bind to the intermolecular complexes and make molecular contacts with both interacting partners. Similar complex-binding mechanism has been observed and well documented for many natural compounds that bind to protein-protein, protein-DNA or protein-small molecule complexes. To implement the paradigm for structure-based drug design, we have developed a complex-binding modulation (CBM) algorithm for the rational design of compounds (CBM compounds) that can affect biomolecular interactions by binding to the intermolecular pockets or cavities of biomolecular complexes. In this paper, we describe our methodology used to design the CBM compounds and to study their effects on biomolecular interactions including protein-protein and protein-small molecule interactions.

A decade of Nucleic Acid Programmable Protein Arrays (NAPPA) availability: News, actors, progress, prospects and access.

Understanding the dynamic of the proteome is a critical challenge because it requires high sensitive methodologies in high-throughput formats in order to decipher its modifications and complexity. While molecular biology provides relevant information about cell physiology that may be reflected in post-translational changes, High-Throughput (HT) experimental proteomic techniques are essential to provide valuable functional information of the proteins, peptides and the interconnections between them. Hence, many methodological developments and innovations have been reported during the last decade. To study more dynamic protein networks and fine interactions, Nucleic Acid Programmable Protein Arrays (NAPPA) was introduced a decade ago. The tool is rapidly maturing and serving as a gateway to characterize biological systems and diseases thanks primarily to its accuracy, reproducibility, throughput and flexibility. Currently, NAPPA technology has proved successful in several research areas adding valuable information towards innovative diagnostic and therapeutic applications. Here, the basic and latest advances within this modern technology in basic, translational research are reviewed, in addition to presenting its exciting new directions. Our final goal is to encourage more scientists/researchers to incorporate this method, which can help to remove bottlenecks in their particular research or biomedical projects. SIGNIFICANCE: Nucleic Acid Programmable Protein Arrays (NAPPA) is becoming an essential tool for functional proteomics and protein-protein interaction studies. The technology impacts decisively on projects aiming massive screenings and the latest innovations like the multiplexing capability or printing consistency make this a promising method to be integrated in novel and combinatorial proteomic approaches.

The evolution of tools for protein phosphorylation site analysis: from discovery to clinical application.

The importance of the analysis of signaling pathways has been proven for many years by the elucidation of key signaling molecules. However, in most cases these pathways tend to represent a rather narrow view of the biological state under investigation. Clearly a more detailed understanding of the complexities of cross-talk between signaling pathways is required to further our knowledge of normal and disease processes. The tools that provide the framework for this increased understanding of biology, those that enable identification, characterization, and quantitation of sites of phosphorylation in proteins, have advanced over the past 25 years. This review will present a brief overview of the history of the tools used in phosphorylation analysis and the latest technologies that are being applied in this field, such as mass spectrometry (for broad-based discovery efforts) and flow cytometry (for translation to clinical applications).