High-efficiency recovery of target cells using improved yeast display system for detection of protein–protein interactions

We constructed a high-throughput screening (HTS) system for target cells based on the detection of protein–protein interactions by flow cytometric sorting due to the improvement in the yeast cell surface display system. Interaction model proteins, which are the ZZ domain derived from Staphylococcus aureus and the Fc part of human immunoglobulin G (IgG), were displayed on the yeast cell surface. We achieved a rapid and enhanced expression of these proteins as a result of adopting an appropriate yeast strain and a suitable promoter. The displayed ZZ domain had an ability to bind to rabbit IgG and the displayed Fc part to protein A. These were confirmed by flow cytometry and fluorescence microscopy. Furthermore, the cells displaying the ZZ domain or Fc part were isolated from the model libraries constructed by mixing the control yeast cells with the target yeast cells. The ratio of the target cells was increased from 0.0001% to more than 70% by two cycles of cell sorting. These results indicate that we can achieve a rapid and highly efficient isolation method for the target cells with FACSCalibur and that this method will further extend the application of flow cytometric sorting to library selections.     

Scoring functions for protein–protein interactions

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Prediction of protein–protein interactions based on PseAA composition and hybrid feature selection

Based on pseudo amino acid (PseAA) composition and a novel hybrid feature selection frame, this paper presents a computational system to predict the PPIs (protein–protein interactions) using 8796 protein pairs. These pairs are coded by PseAA composition, resulting in 114 features. A hybrid feature selection system, mRMR–KNNs–wrapper, is applied to obtain an optimized feature set by excluding poor-performed and/or redundant features, resulting in 103 remaining features. Using the optimized 103-feature subset, a prediction model is trained and tested in the k-nearest neighbors (KNNs) learning system. This prediction model achieves an overall accurate prediction rate of 76.18%, evaluated by 10-fold cross-validation test, which is 1.46% higher than using the initial 114 features and is 6.51% higher than the 20 features, coded by amino acid compositions. The PPIs predictor, developed for this research, is available for public use at http://chemdata.shu.edu.cn/ppi.     

Hot spot-based design of small-molecule inhibitors for protein–protein interactions

Protein–protein interactions (PPIs) are important targets for the development of chemical probes and therapeutic agents. From the initial discovery of the existence of hot spots at PPI interfaces, it has been proposed that hot spots might provide the key for developing small-molecule PPI inhibitors. However, there has been no review on the ways in which the knowledge of hot spots can be used to achieve inhibitor design, nor critical examination of successful examples. This Digest discusses the characteristics of hot spots and the identification of druggable hot spot pockets. An analysis of four examples of hot spot-based design reveals the importance of this strategy in discovering potent and selective PPI inhibitors. A general procedure for hot spot-based design of PPI inhibitors is outlined.     

Systems for the detection and analysis of protein–protein interactions

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

Mapping protein–ligand interactions using whole genome phage display libraries

The function of many genes cannot be deduced from sequence similarity, and biochemical methods are usually required. Whole genome sequences can be thought of as not only a set of genes but also collections of functional domains. These domains can be studied by affinity methods whereby identification of the ligand can provide information on biochemical function. To take advantage of this method, one must express all functional domains in a form suitable for affinity studies. Phage display technology provides a means for accomplishing this. The pJuFo phage display system, based on the interaction between the leucine zippers Jun and Fos, has been modified and used to create a genomic phage display library from Escherichia coli MG1655. The system has been tested by using the library to map the dominant binding epitopes for an anti-RecA protein polyclonal antibody sera. This methodology provides a general biochemical approach to functional analysis of protein–ligand interactions on a genome-wide basis.     

Peptide-based covalent inhibitors of protein–protein interactions

Protein–protein interactions (PPI) are involved in all cellular processes and many represent attractive therapeutic targets. However, the frequently rather flat and large interaction areas render the identification of small molecular PPI inhibitors very challenging. As an alternative, peptide interaction motifs derived from a PPI interface can serve as starting points for the development of inhibitors. However, certain proteins remain challenging targets when applying inhibitors with a competitive mode of action. For that reason, peptide-based ligands with an irreversible binding mode have gained attention in recent years. This review summarizes examples of covalent inhibitors that employ peptidic binders and have been tested in a biological context.     

Direct molecular fishing in molecular partners investigation in protein–protein and protein–peptide interactions

An original experimental method of direct molecular fishing has been developed for identification of potential partners of protein–protein and protein–peptide interactions. It is based on combination of surface plasmon resonance technology (SPR), size exclusion and affinity chromatography and mass spectrometric identification of proteins (LC-MS/MS). Previously, we demonstrated applicability of this method for protein interactomics using experimental model system, as well as in the pilot study in the frame of the Human Proteome Project (HPP). In the present paper, this method was successfully applied to identify possible molecular partners of 7 target proteins encoded by genes of 18 chromosome (also in the frame of the HPP). Fishing on the affinity sorbents with immobilized target proteins as ligands was carried out using total lysate of human liver tissue as well as pooled sets of fractions (individual for each bait-protein) obtained by means of a combination of size exclusion chromatography and SPR analysis for the presence of potential prey-proteins in each fraction. As a result we obtained lists of possible molecular partners of all 7 proteins and performed a comparative evaluation of direct fishing specificity for these target proteins. Direct molecular fishing was also successfully used for search of potential protein partners interacting with different isoforms of amyloid-beta peptide, playing a key role in the development of Alzheimer’s disease. The synthetic peptides that are analogues of the metal-binding domain isoforms of beta-amyloid were used as molecular baits and the fishing was performed in various fractions of immortalized human neural cells. As a result, 13 potential partner proteins were identified in the cytosol fraction of the cells by fishing on amyloid-beta peptide (1-16).     

Comparative mapping of host–pathogen protein–protein interactions

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Chemoproteomic profiling of protein–metabolite interactions

Small molecule metabolites play important roles in regulating protein functions, which are acted through either covalent non-enzymatic post-translational modifications or non-covalent binding interactions. Chemical proteomic strategies can help delineate global landscapes of cellular protein–metabolite interactions and provide molecular insights about their mechanisms of action. In this review, we summarized the recent progress in developments and applications of chemoproteomic strategies to profile protein–metabolite interactions.     

Robust Bayesian variable selection for gene–environment interactions

Gene–environment (G× E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of G× E studies have been commonly encountered, leading to the development of a broad spectrum of robust regularization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a fully Bayesian robust variable selection method for G× E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, for the robust sparse group selection, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects robustly. An efficient Gibbs sampler has been developed to facilitate fast computation. Extensive simulation studies, analysis of diabetes data with single-nucleotide polymorphism measurements from the Nurses' Health Study, and The Cancer Genome Atlas melanoma data with gene expression measurements demonstrate the superior performance of the proposed method over multiple competing alternatives.