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The two-hybrid system is a yeast-based genetic method to detect and analyse protein–protein interactions. Related systems have been developed to detect DNA–protein, RNA–protein and small molecule–protein interactions. Keywords: protein–protein interaction; DNA–protein interaction; RNA–protein interaction; yeast; transcription factor

Structural and mutational analyses reveal a central role for electrostatic interactions in protein-protein association. Experiment and theory both demonstrate that clusters of charged and polar residues that are located on protein-protein interfaces may enhance complex stability, although the total effect of electrostatics is generally net destabilizing. The past year also witnessed significant progress in our understanding of the effect of electrostatics on protein association kinetics, specifically in the characterization of a partially desolvated encounter complex.

Protein–protein interactions have been widely used to study gene expression pathways and may be considered as a new approach to drug discovery. Here I report the development of a universal protein array (UPA) system that provides a sensitive, quantitative, multi-purpose, effective and easy technology to determine not only specific protein–protein interactions, but also specific interactions of proteins with DNA, RNA, ligands and other small chemicals. (i) Since purified proteins are used, the results can be easily interpreted. (ii) UPA can be used multiple times for different targets, making it economically affordable for most laboratories, hospitals and biotechnology companies. (iii) Unlike DNA chips or DNA microarrays, no additional instrumentation is required. (iv) Since the UPA uses active proteins (without denaturation and renaturation), it is more sensitive compared with most existing methods. (v) Because the UPA can analyze hundreds (even thousands on a protein microarray) of proteins in a single experiment, it is a very effective method to screen proteins as drug targets in cancer and other human diseases.

Publisher Summary This chapter discusses two-hybrid analysis of protein–protein interactions in yeast. Protein–protein interactions underpin a wide range of complex cellular processes and in many cases make a critical contribution to the regulation of these processes. A key question in the study of the cellular function of any novel protein is to identify other proteins with which it interacts. There are many physical, biochemical, and immunological methods available to identify and quantify such interactions. These have been complemented by genetic-based methods of which one—the yeast two-hybrid system—was developed as a sensitive genetic tool to identify protein–protein interactions in vivo. The impact of the two-hybrid method for defining and characterizing protein–protein interactions has been remarkable; although it represents only one of the many available methods for studying such interactions, its application is enhanced considerably by an explosion in genome sequence information. The chapter describes the background to the two-hybrid system and the properties of the basic elements used to define interactions using the two-hybrid system. It also reviews some of the potential future developments of this technology.

Transient biomolecular interactions are essential for biological processes, but strategies for studying them have remained elusive. A paper in this issue shows how natural enzymatic activities can be exploited to examine protein-protein interactions in fatty acid synthase.

Two-hybrid systems have been the cornerstone of research into protein–protein interactions, but these systems typically rely on life/death reporters that put additional selective pressure on the host organism, and potentially lead to false positives. Here we report a bidirectional fluorescence-based bacterial two- hybrid system that enables both the association and dissociation of a given protein–protein interaction to be monitored. The functionality of this system and its compatibility with FACS screening are demon- strated in the forward and reverse direction using known interacting protein-partners and their cyclic peptide inhibitors. The reported fluorescent two-hybrid system may be used in the forward direction for the identification of interacting protein partners, or as a reverse two-hybrid system for the high- throughput identification of protein–protein interaction inhibitors.

An interactome is defined as a network of protein-protein interactions built from experimentally verified interactions. Basic science as well as application-based research of potential new drugs can be promoted by including proteins that are only predicted into interactomes. The disadvantage of doing so is the risk of devaluing the definition of interactomes. By adding proteins that have only been predicted, an interactome can no longer be classified as experimentally verified and the integrity of the interactome will be endured. Therefore, we propose the term 'hypothome' (collection of hypothetical interactions of predicted proteins). The purpose of such a term is to provide a denotation to the interactome concept allowing the interaction of predicted proteins without devaluing the integrity of the interactome. We define a rule-set for a hypothome and have integrated the predicted protein interaction partners to the hypothetical protein. EAW74251 is an example for the usage of a hypothome.

Publisher Summary Protein–protein interactions play crucial roles in a wide variety of processes, which define cell structure and function. This chapter describes the emerging tools and strategies for determining the kinetics and thermodynamics of protein–protein interactions. These biophysical methods can be used to play dominant roles as core tools for studying the structural and functional molecular properties of protein–protein interactions. In general, these methods require relatively small amounts (milligrams) of protein material and are universally applicable to the study of proteins. Although biophysical methods are capable of providing molecular-level details of protein–protein interactions, the increase in resolution to the molecular level is accompanied by an increased need to validate interpretation of the data. The chapter discusses the synergistic benefits of using multiple independent methods.

Protein–protein interactions are highly desirable targets in drug discovery, yet only a fraction of drugs act as binding inhibitors. Here, we review the different technologies used to find and validate protein–protein interactions. We then discuss how the novel combination of cell-free protein expression, AlphaScreen and single-molecule fluorescence spectroscopy can be used to rapidly map protein interaction networks, determine the architecture of protein complexes, and find new targets for drug discovery.

The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms. To date, structural information has had only limited impact on genome-scale efforts to predict protein–protein interactions (PPIs). A new algorithm, PrePPI, will be introduced that combines structural information with nonstructural clues and that is comparable in accuracy to high-throughput experiments. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models and the exploitation of both close and remote geometric relationships between proteins. More generally, the “structural BLAST” approach encapsulated in PrePPI significantly expands the range of application of protein structure in the annotation of protein function.

Publisher Summary This chapter discusses the identification of protein–protein interactions by λgt11 expression cloning. Protein–protein interactions govern a wide variety of fundamental biological processes, ranging from the control of enzymatic activity through subunit association and enzyme-substrate recognition, to the specific interactions of protein ligands with their receptors. Intermolecular associations are also central to the manner in which oncogene-encoded proteins regulate cell proliferation. The realization that highly specific protein–protein interactions mediate cell behavior at almost every level has led to attempts to develop general methods with which to identify molecules that physically associate with a given protein. These approaches fall roughly into two classes: biochemical and genetic. When correct processing and post-translational modification are a prerequisite for molecular interaction, a modified genetic strategy that employs transfection of a cDNA expression library into mammalian cells can be used. Such a technique has been used quite successfully to identify several cell surface receptors based on their ligand binding activities. A frequently used biochemical approach to identify specific molecular interactions has been to employ an immobilized target protein to adsorb unknown binding partners from cell extracts. One advantage of the expression cloning method is that the actual screening is carried out ex vivo. This permits a greater degree of control over the preparation and labeling of the probe than in the two-hybrid system.

Publisher Summary This chapter presents the analysis of protein–protein interactions using two-hybrid system. Protein-protein interactions play a critical role in most biological processes. The studies defining domains of proteins that are responsible for specific interactions have contributed significantly to unraveling the mechanisms of tumorigenesis. The two-hybrid system is a yeast-based genetic assay for detecting protein– protein interactions in vivo. It can be used to establish interactions between two known proteins or to search genomic or cDNA libraries for proteins that interact with a target protein. For this latter application, the gene encoding the protein that interacts with a target protein is immediately available on a plasmid, which is not the case for many biochemical methods to detect interacting proteins. The two-hybrid system has also been used to define the protein domains that mediate an interaction and to identify specific residues that are involved in a protein–protein interaction. The chapter also discusses the basis for this method and presents the protocols that are necessary to use this system.

BACKGROUND: Recent progress in computational methods for predicting physical and functional protein-protein interactions has provided new insights into the complexity of biological processes. Most of these methods assume that functionally interacting proteins are likely to have a shared evolutionary history. This history can be traced out for the protein pairs of a query genome by correlating different evolutionary aspects of their homologs in multiple genomes known as the reference genomes. These methods include phylogenetic profiling, gene neighborhood and co-occurrence of the orthologous protein coding genes in the same cluster or operon. These are collectively known as genomic context methods. On the other hand a method called mirrortree is based on the similarity of phylogenetic trees between two interacting proteins. Comprehensive performance analyses of these methods have been frequently reported in literature. However, very few studies provide insight into the effect of reference genome selection on detection of meaningful protein interactions. METHODS: We analyzed the performance of four methods and their variants to understand the effect of reference genome selection on prediction efficacy. We used six sets of reference genomes, sampled in accordance with phylogenetic diversity and relationship between organisms from 565 bacteria. We used Escherichia coli as a model organism and the gold standard datasets of interacting proteins reported in DIP, EcoCyc and KEGG databases to compare the performance of the prediction methods. CONCLUSIONS: Higher performance for predicting protein-protein interactions was achievable even with 100-150 bacterial genomes out of 565 genomes. Inclusion of archaeal genomes in the reference genome set improves performance. We find that in order to obtain a good performance, it is better to sample few genomes of related genera of prokaryotes from the large number of available genomes. Moreover, such a sampling allows for selecting 50-100 genomes for comparable accuracy of predictions when computational resources are limited.

Bcl-2 family proteins have important roles in tumor initiation, progression and resistance to therapy. Pro-survival Bcl-2 proteins are regulated by their interactions with pro-death BH3-only proteins making these protein-protein interactions attractive therapeutic targets. Although these interactions have been extensively characterized biochemically, there is a paucity of tools to assess these interactions in cells. Here, we address this limitation by developing quantitative, high throughput microscopy assays to characterize Bcl-2 and BH3-only protein interactions in live cells. We use fluorescent proteins to label the interacting proteins of interest, enabling visualization and quantification of their mitochondria-localized interactions. Using tool compounds, we demonstrate the suitability of our assays to characterize the cellular activity of putative therapeutic molecules that target the interaction between pro-survival Bcl-2 and pro-death BH3-only proteins. In addition to the relevance of our assays for drug discovery, we anticipate that our work will contribute to an improved understanding of the mechanisms that regulate these important protein-protein interactions within the cell.

Knowledge of Protein-Protein Interactions (PPIs) can give us new insights into molecular mechanisms and properties of the cell. In this paper, we propose a novel domain-based kernel method to predict PPIs. A new kernel that measures the similarity between protein pairs based on a new feature representation is developed and applied to a large scale PPI database. Experimental results demonstrate its effectiveness. Furthermore, we evaluate the problem of cross-species PPI prediction and the effect of the number of negative samples on the performance of PPI predictions, which are two fundamental problems in most in silico PPI methods.

Background High-throughput methods can directly detect the set of interacting proteins in model species but the results are often incomplete and exhibit high false positive and false negative rates. A number of researchers have recently presented methods for integrating direct and indirect data for predicting interactions. These methods utilize a common classifier for all pairs. However, due to missing data and high redundancy among the features used, different protein pairs may benefit from different features based on the set of attributes available. In addition, in many cases it is hard to directly determine which of the data sources contributed to a prediction. This information is important for biologists using these predications in the design of new experiments. Results To address these challenges we propose a Mixture-of-Feature-Experts method for protein-protein interaction prediction. We split the features into roughly homogeneous sets of feature experts. The individual experts use logistic regression and their scores are combined using another logistic regression. When combining the scores the weighting of each expert depends on the set of input attributes available for that pair. Thus, different experts will have different influence on the prediction depending on the available features. Conclusion We applied our method to predict the set of interacting proteins in yeast and human cells. Our method improved upon the best previous methods for this task. In addition, the weighting of the experts provides means to evaluate the prediction based on the high scoring features.

Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein–protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/, Synopsis The IntAct database contains about ten large-scale data sets of protein–protein interactions. Each set contains thousands of experimentally observed pair interactions. Most pairs were observed in yeast (Saccharomyces cerevisiae), fly (Drosophila melanogaster), and worm (Caenorhabditis elegans). These interactions are often perceived as model organisms in the sense that one can infer that two mouse proteins interact if one experimentally observes the two corresponding proteins in worm to interact. Here, the authors analyzed in detail how the sequence signals of physical protein–protein interactions are conserved. It is a common assumption that protein–protein interactions can easily be inferred through homology transfer from one model organism to another organism of interest. Here, the authors demonstrated that such homology transfers are only accurate at unexpectedly high levels of sequence identity. Even more surprisingly, homology transfers of protein–protein interactions are significantly more reliable for protein pairs from the same species than for two protein pairs from different organisms. The observation that interactions were much more conserved within than across species was valid for all levels of sequence similarity, i.e. for very similar as well as for more diverged interologs.

Protein–Protein Interactions (A Molecular Cloning Manual) edited by Erica GolemisCold Spring Harbor Laboratory Press, 2001. £100.00 pbk (600 pages)ISBN 0 879 69628 1Proteins control and mediate many of the biological activities of cells. Hence, to gain an understanding of cellular function, the function of every protein must be understood, both in isolation and in the context of other interacting proteins. This view was established many years ago, but developments in genomics have led several scientists to invert this statement: if we knew most or all protein–protein interactions in a cell, the functions of most proteins would become clearer. Recently, we have seen the development of large-scale biological experiments that are technology-driven rather than hypothesis-driven. And, it has become evident that one can easily forget the main goal of biological achievement – understanding mechanisms of life – while concentrating on high-throughput technologies to populate biological databases. In this regard, protein–protein interactions are a special case: recent high-throughput technologies derived from a genetic assay in yeast, or from complex purification and mass spectrometric analyses have obscured the fact that protein associations have long been the focus of geneticists and biochemists. In the future, data raising from these various high-throughput technologies will be merged with knowledge extracted from the literature thanks to appropriate bioinformatics tools. Biologists will still perform hypothesis-driven science, but they will be building their hypothesis not only on their own expertise, but also by taking advantage of this incredible mass of data.Protein–Protein Interactions, edited by E. Golemis, reconciles modern large-scale technological approaches with more classical approaches to understanding proteins. In the first part of the book, the key importance of protein–protein interaction is illustrated in several examples such as signal transduction and human genetics. A brief description of MAPK pathway is given, emphasizing the role of interactions with scaffolding proteins in addition to signaling proteins. Protein interactions are also key to pharmacogenetic studies on DNA repair, and BRCA1 and BRCA2 protein complexes. Mutations in these proteins are associated with a diseased state and affect their ability to interact with other proteins.Several technical chapters cover in vitro approaches (glutathione-S-transferase pull-down, far westerns, coimmunoprecipitation and chemical crosslinking) and genetic assays (yeast and bacterial two-hybrid techniques, phage display and classical yeast suppressor screens). These are classical assays, but the rationale of experiments is well explained and protocols, often adapted from Sambrook and Russell [1xSambrook, J. and Russell, D. See all References[1], will certainly aid most researchers in their initial setting up of experiments. Also covered are techniques that are compatible with the physiology of the cell: fluorescence resonance energy transfer (FRET), proximity imaging of polypeptides tagged with green-fluorescent protein (GFP–PRIM) experiments to follow homotypic interactions, multicomplex purification followed by mass spectrometry analyses. Technologies are also discussed that make direct measurements of the affinity between proteins: atomic force microscopy, Biacore or acoustic biosensor analyses.In final section, the editor has asked to several scientists to describe their own work on protein–protein interactions, using specific variations of classical assays or developing new assays to achieve specific goals. Protease fingerprinting, various examples of pull-down experiments, catalytic antibodies, computational tools for protein interactions, and manipulation of protein networks for new therapeutic approaches are all described. For example, Chapter 10 illustrates the measurement of the activation state of the protein kinase C. This is done by a FRET experiment measuring the interaction between the phosphorylated protein at a specific residue and a specific antibody. In another example, multiprotein complexes are analyzed by mass spectrometry (Chapter 12), allowing characterization of the stoichiometry of complexes or the arrangement of the subunits.As a whole, the book is a comprehensive survey of ways to approach protein–protein interactions: the concepts underlying each technology are described and operational protocols are given. Experts might consider that some of the suggested protocols are not necessarily the most appropriate, but, in general, they will help the reader to design his/her experiment in a productive way. More importantly, the book often shows how protein interaction measurements help the understanding of biological mechanisms. This book develops an appreciation of the wide range of techniques addressing protein–protein interaction issues, and why some are better suited than others to answer a specific biological question.

Many protein-protein interactions are mediated through independently folding modular domains. Proteome-wide efforts to model protein-protein interaction or "interactome" networks have largely ignored this modular organization of proteins. We developed an experimental strategy to efficiently identify interaction domains and generated a domain-based interactome network for proteins involved in C. elegans early-embryonic cell divisions. Minimal interacting regions were identified for over 200 proteins, providing important information on their domain organization. Furthermore, our approach increased the sensitivity of the two-hybrid system, resulting in a more complete interactome network. This interactome modeling strategy revealed insights into C. elegans centrosome function and is applicable to other biological processes in this and other organisms., Journal Article, Research Support, N.I.H. Extramural, Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, Non-P.H.S., info:eu-repo/semantics/published

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well.

Protein–protein interactions are facilitated by a myriad of residue–residue contacts on the interacting proteins. Identifying the site of interaction in the protein is a key for deciphering its functional mechanisms, and is crucial for drug development. Many studies indicate that the compositions of contacting residues are unique. Here, we describe a neural network that identifies protein–protein interfaces from sequence. For the most strongly predicted sites (in 34 of 333 proteins), 94% of the predictions were confirmed experimentally. When 70% of our predictions were right, we correctly predicted at least one interaction site in 20% of the complexes (66/333). These results indicate that the prediction of some interaction sites from sequence alone is possible. Incorporating evolutionary and predicted structural information may improve our method. However, even at this early stage, our tool might already assist wet-lab biology.

Identifying small molecules that induce the disruption of constitutive protein–protein interfaces is a challenging objective. Here, a targeted biophysical screening cascade was employed to specifically identify small molecules that could disrupt the constitutive, homodimeric protein–protein interface within CK2β. This approach could potentially be applied to achieve subunit disassembly of other homo-oligomeric proteins as a means of modulating protein function.

The elucidation of protein-protein interaction (PPI) networks is important for understanding cellular structure and function and structure-based drug design. However, the development of an effective method to conduct exhaustive PPI screening represents a computational challenge. We have been investigating a protein docking approach based on shape complementarity and physicochemical properties. We describe here the development of the protein-protein docking software package “MEGADOCK” that samples an extremely large number of protein dockings at high speed. MEGADOCK reduces the calculation time required for docking by using several techniques such as a novel scoring function called the real Pairwise Shape Complementarity (rPSC) score. We showed that MEGADOCK is capable of exhaustive PPI screening by completing docking calculations 7.5 times faster than the conventional docking software, ZDOCK, while maintaining an acceptable level of accuracy. When MEGADOCK was applied to a subset of a general benchmark dataset to predict 120 relevant interacting pairs from 120 x 120 = 14,400 combinations of proteins, an F-measure value of 0.231 was obtained. Further, we showed that MEGADOCK can be applied to a large-scale protein-protein interaction-screening problem with accuracy better than random. When our approach is combined with parallel high-performance computing systems, it is now feasible to search and analyze protein-protein interactions while taking into account three-dimensional structures at the interactome scale. MEGADOCK is freely available at http://www.bi.cs.titech.ac.jp/megadock.

Background Protein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution. Results In this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively. Conclusions In the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1585-0) contains supplementary material, which is available to authorized users.

Background: Advances in biotechnology have created “big-data” situations in molecular and cellular biology. Several sophisticated algorithms have been developed that process big data to generate hundreds of biomedical hypotheses (or predictions). The bottleneck to translating this large number of biological hypotheses is that each of them needs to be studied by experimentation for interpreting its functional significance. Even when the predictions are estimated to be very accurate, from a biologist’s perspective, the choice of which of these predictions is to be studied further is made based on factors like availability of reagents and resources and the possibility of formulating some reasonable hypothesis about its biological relevance. When viewed from a global perspective, say from that of a federal funding agency, ideally the choice of which prediction should be studied would be made based on which of them can make the most translational impact. Results: We propose that algorithms be developed to identify which of the computationally generated hypotheses have potential for high translational impact; this way, funding agencies and scientific community can invest resources and drive the research based on a global view of biomedical impact without being deterred by local view of feasibility. In short, data-analytic algorithms analyze big-data and generate hypotheses; in contrast, the proposed inference-analytic algorithms analyze these hypotheses and rank them by predicted biological impact. We demonstrate this through the development of an algorithm to predict biomedical impact of protein-protein interactions (PPIs) which is estimated by the number of future publications that cite the paper which originally reported the PPI. Conclusions: This position paper describes a new computational problem that is relevant in the era of big-data and discusses the challenges that exist in studying this problem, highlighting the need for the scientific community to engage in this line of research. The proposed class of algorithms, namely inference-analytic algorithms, is necessary to ensure that resources are invested in translating those computational outcomes that promise maximum biological impact. Application of this concept to predict biomedical impact of PPIs illustrates not only the concept, but also the challenges in designing these algorithms.

Several biophysical approaches are available to study protein-protein interactions. Most approaches are conducted in bulk solution, and are therefore limited to an average measurement of the ensemble of molecular interactions. Here, we show how single-particle EM can enrich our understanding of protein-protein interactions at the single-molecule level and potentially capture states that are unobservable with ensemble methods because they are below the limit of detection or not conducted on an appropriate time scale. Using the HIV-1 envelope glycoprotein (Env) and its interaction with receptor CD4-binding site neutralizing antibodies as a model system, we both corroborate ensemble kinetics-derived parameters and demonstrate how time-course EM can further dissect stoichiometric states of complexes that are not readily observable with other methods. Visualization of the kinetics and stoichiometry of Env-antibody complexes demonstrated the applicability of our approach to qualitatively and semi-quantitatively differentiate two highly similar neutralizing antibodies. Furthermore, implementation of machine-learning techniques for sorting class averages of these complexes into discrete subclasses of particles helped reduce human bias. Our data provide proof of concept that single-particle EM can be used to generate a "visual" kinetic profile that should be amenable to studying many other protein-protein interactions, is relatively simple and complementary to well-established biophysical approaches. Moreover, our method provides critical insights into broadly neutralizing antibody recognition of Env, which may inform vaccine immunogen design and immunotherapeutic development.

Background Protein–protein interactions can be seen as a hierarchical process occurring at three related levels: proteins bind by means of specific domains, which in turn form interfaces through patches of residues. Detailed knowledge about which domains and residues are involved in a given interaction has extensive applications to biology, including better understanding of the binding process and more efficient drug/enzyme design. Alas, most current interaction prediction methods do not identify which parts of a protein actually instantiate an interaction. Furthermore, they also fail to leverage the hierarchical nature of the problem, ignoring otherwise useful information available at the lower levels; when they do, they do not generate predictions that are guaranteed to be consistent between levels. Results Inspired by earlier ideas of Yip et al. (BMC Bioinformatics 10:241, 2009), in the present paper we view the problem as a multi-level learning task, with one task per level (proteins, domains and residues), and propose a machine learning method that collectively infers the binding state of all object pairs. Our method is based on Semantic Based Regularization (SBR), a flexible and theoretically sound machine learning framework that uses First Order Logic constraints to tie the learning tasks together. We introduce a set of biologically motivated rules that enforce consistent predictions between the hierarchy levels. Conclusions We study the empirical performance of our method using a standard validation procedure, and compare its performance against the only other existing multi-level prediction technique. We present results showing that our method substantially outperforms the competitor in several experimental settings, indicating that exploiting the hierarchical nature of the problem can lead to better predictions. In addition, our method is also guaranteed to produce interactions that are consistent with respect to the protein–domain–residue hierarchy.

Background Information about protein interaction networks is fundamental to understanding protein function and cellular processes. Interaction patterns among proteins can suggest new drug targets and aid in the design of new therapeutic interventions. Efforts have been made to map interactions on a proteomic-wide scale using both experimental and computational techniques. Reference datasets that contain known interacting proteins (positive cases) and non-interacting proteins (negative cases) are essential to support computational prediction and validation of protein-protein interactions. Information on known interacting and non interacting proteins are usually stored within databases. Extraction of these data can be both complex and time consuming. Although, the automatic construction of reference datasets for classification is a useful resource for researchers no public resource currently exists to perform this task. Results GRIP (Gold Reference dataset constructor from Information on Protein complexes) is a web-based system that provides researchers with the functionality to create reference datasets for protein-protein interaction prediction in Saccharomyces cerevisiae. Both positive and negative cases for a reference dataset can be extracted, organised and downloaded by the user. GRIP also provides an upload facility whereby users can submit proteins to determine protein complex membership. A search facility is provided where a user can search for protein complex information in Saccharomyces cerevisiae. Conclusion GRIP is developed to retrieve information on protein complex, cellular localisation, and physical and genetic interactions in Saccharomyces cerevisiae. Manual construction of reference datasets can be a time consuming process requiring programming knowledge. GRIP simplifies and speeds up this process by allowing users to automatically construct reference datasets. GRIP is free to access at http://rosalind.infj.ulst.ac.uk/GRIP/.

We approached to prediction of protein-protein interactions using rigid-body docking algorithm, generating many decoys including false positives. To search near-native decoys from a decoy set generated by a docking process, cluster analysis is useful with calculating similarities between decoys. However, this analysis goes well only in the case that a decoy set includes near-native decoys. Actually, there are some cases of protein pairs with no near-native decoys after an initial docking process.This problem is related with a size of docking search spaces. When a size of search space is small, near-native decoys cannot be obtained after a rigid-body docking process. We then applied a profile method to obtain a region including native interacting residue pairs for re-docking process by using Profile of Interaction FingerPrints (P-IFP).Interaction FingerPrints (IFP) has been developed for the post-docking analysis of protein-protein rigid-body docking. IFP is composed of binary states of interacting amino acid residues of each interacting proteins, as a scale for measuring unique similarities between the complex structures. Then, IFP can be used more easily to evaluate similarities between decoys than RMSD method, which depends on methods of superposing their structures. Additionally, IFP can be useful to assemble interaction profiles from various decoys.In this work, we examined re-docking process after generating P-IFP for rigid-body docking decoys. As results, we could obtain a set of decoys with higher similarities than that of decoys generated in the initial docking process. Re-docking method using the IFP method is expected to improve cases without near-native decoys in an initial docking process.

Background Protein-protein interaction (PPI) plays a core role in cellular functions. Massively parallel supercomputing systems have been actively developed over the past few years, which enable large-scale biological problems to be solved, such as PPI network prediction based on tertiary structures. Results We have developed a high throughput and ultra-fast PPI prediction system based on rigid docking, “MEGADOCK”, by employing a hybrid parallelization (MPI/OpenMP) technique assuming usages on massively parallel supercomputing systems. MEGADOCK displays significantly faster processing speed in the rigid-body docking process that leads to full utilization of protein tertiary structural data for large-scale and network-level problems in systems biology. Moreover, the system was scalable as shown by measurements carried out on two supercomputing environments. We then conducted prediction of biological PPI networks using the post-docking analysis. Conclusions We present a new protein-protein docking engine aimed at exhaustive docking of mega-order numbers of protein pairs. The system was shown to be scalable by running on thousands of nodes. The software package is available at: http://www.bi.cs.titech.ac.jp/megadock/k/.

Background With the development of genome-sequencing technologies, protein sequences are readily obtained by translating the measured mRNAs. Therefore predicting protein-protein interactions from the sequences is of great demand. The reason lies in the fact that identifying protein-protein interactions is becoming a bottleneck for eventually understanding the functions of proteins, especially for those organisms barely characterized. Although a few methods have been proposed, the converse problem, if the features used extract sufficient and unbiased information from protein sequences, is almost untouched. Results In this study, we interrogate this problem theoretically by an optimization scheme. Motivated by the theoretical investigation, we find novel encoding methods for both protein sequences and protein pairs. Our new methods exploit sufficiently the information of protein sequences and reduce artificial bias and computational cost. Thus, it significantly outperforms the available methods regarding sensitivity, specificity, precision, and recall with cross-validation evaluation and reaches ~80% and ~90% accuracy in Escherichia coli and Saccharomyces cerevisiae respectively. Our findings here hold important implication for other sequence-based prediction tasks because representation of biological sequence is always the first step in computational biology. Conclusions By considering the converse problem, we propose new representation methods for both protein sequences and protein pairs. The results show that our method significantly improves the accuracy of protein-protein interaction predictions.