Reverse MAPPIT detects disruptors of protein-protein interactions in human cells

Screening for molecular glues - Challenges and opportunities

Molecular glues are small molecules, typically smaller than PROTACs, and usually with improved physicochemical properties that aim to stabilise the interaction between two proteins. Most often this approach is used to improve or induce an interaction between the target and an E3 ligase, but other interactions which stabilise interactions to increase activity or to inhibit binding to a natural effector have also been demonstrated. This review will describe the effects of induced proximity, discuss current methods used to identify molecular glues and introduce approaches that could be adapted for molecular glue screening.

Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays

Protein interactions mediate essentially all biological processes and analysis of protein-protein interactions using both large-scale and small-scale approaches has contributed fundamental insights to the understanding of biological systems. In recent years, interactome network maps have emerged as an important tool for analyzing and interpreting genetic data of complex phenotypes. Complementary experimental approaches to test for binary, direct interactions, and for membership in protein complexes are used to explore the interactome. The two approaches are not redundant but yield orthogonal perspectives onto the complex network of physical interactions by which proteins mediate biological processes. In recent years, several publications have demonstrated that interactions from high-throughput experiments can be equally reliable as the high quality subset of interactions identified in small-scale studies. Critical for this insight was the introduction of standardized experimental benchmarking of interaction and validation assays using reference sets. The data obtained in these benchmarking experiments have resulted in greater appreciation of the limitations and the complementary strengths of different assays. Moreover, benchmarking is a central element of a conceptual framework to estimate interactome sizes and thereby measure progress toward near complete network maps. These estimates have revealed that current large-scale data sets, although often of high quality, cover only a small fraction of a given interactome. Here, I review the findings of assay benchmarking and discuss implications for quality control, and for strategies toward obtaining a near-complete map of the interactome of an organism.

The use of mammalian two-hybrid technologies for high-throughput drug screening

Developing agents that target protein-protein interactions (PPIs) is an emerging field in drug discovery. Although this target class has hitherto remained underexplored, it holds exceptional promise related to the large amount of potential PPI targets compared to single protein targets and it offers important opportunities to increase the specificity of therapeutic molecules. While several PPI modulating therapeutics have recently been reported and a number of these are in clinical trial, progress in the field has been hampered by the lack of efficient screening systems. Recently, a number of cellular approaches have been developed that complement classical in vitro screening methods and which exhibit a number of important assets related to the physiological context they provide. Here we discuss the utility of two-hybrid technologies towards high-throughput screening for PPI inhibitors, in particular those that operate in a mammalian cellular background. We review a number of cases where mammalian two-hybrids have been successfully applied to identify small molecule disruptors of PPIs and zoom in further on the MAPPIT (Mammalian Protein-Protein Interaction Trap) technology platform. The value of this approach for drug discovery is illustrated by recent data from MAPPIT-based screening projects.

Investigating metabolite-protein interactions: an overview of available techniques

Metabolites comprise the molar majority of chemical substances in living cells, and metabolite-protein interactions are expected to be quite common. Many interactions have already been identified and have been shown to be involved in the regulation of different types of cellular processes including signaling events, enzyme activities, protein localizations and interactions. Recent technological advances have greatly facilitated the detection of metabolite-protein interactions at high sensitivity and some of these have been applied on a large scale. In this manuscript, we review the available in vitro, in silico and in vivo technologies for mapping small-molecule-protein interactions. Although some of these were developed for drug-protein interactions they can be applied for mapping metabolite-protein interactions. Information gained from the use of these approaches can be applied to the manipulation of cellular processes and therapeutic applications.

MAPPIT: a protein interaction toolbox built on insights in cytokine receptor signaling

MAPPIT (mammalian protein-protein interaction trap) is a two-hybrid interaction mapping technique based on functional complementation of a type I cytokine receptor signaling pathway. Over the last decade, the technology has been extended into a platform of complementary assays for the detection of interactions among proteins and between chemical compounds and proteins, and for the identification of small molecules that interfere with protein-protein interactions. Additionally, several screening approaches have been developed to broaden the utility of the platform. In this review we provide an overview of the different components of the MAPPIT toolbox and highlight a number of applications in interactomics, drug screening and compound target profiling.

Strategies for protein synthetic biology

Proteins are the most versatile among the various biological building blocks and a mature field of protein engineering has lead to many industrial and biomedical applications. But the strength of proteins—their versatility, dynamics and interactions—also complicates and hinders systems engineering. Therefore, the design of more sophisticated, multi-component protein systems appears to lag behind, in particular, when compared to the engineering of gene regulatory networks. Yet, synthetic biologists have started to tinker with the information flow through natural signaling networks or integrated protein switches. A successful strategy common to most of these experiments is their focus on modular interactions between protein domains or domains and peptide motifs. Such modular interaction swapping has rewired signaling in yeast, put mammalian cell morphology under the control of light, or increased the flux through a synthetic metabolic pathway. Based on this experience, we outline an engineering framework for the connection of reusable protein interaction devices into self-sufficient circuits. Such a framework should help to ‘refacture’ protein complexity into well-defined exchangeable devices for predictive engineering. We review the foundations and initial success stories of protein synthetic biology and discuss the challenges and promises on the way from protein- to protein systems design.

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.

An empirical framework for binary interactome mapping

Several attempts have been made at systematically mapping protein-protein interaction, or “interactome” networks. However, it remains difficult to assess the quality and coverage of existing datasets. We describe a framework that uses an empirically-based approach to rigorously dissect quality parameters of currently available human interactome maps. Our results indicate that high-throughput yeast two-hybrid (HT-Y2H) interactions for human are superior in precision to literature-curated interactions supported by only a single publication, suggesting that HT-Y2H is suitable to map a significant portion of the human interactome. We estimate that the human interactome contains ~130,000 binary interactions, most of which remain to be mapped. Similar to estimates of DNA sequence data quality and genome size early in the human genome project, estimates of protein interaction data quality and interactome size are critical to establish the magnitude of the task of comprehensive human interactome mapping and to illuminate a path towards this goal.