Studying protein-protein interactions using peptide arrays

Screening of arrays and libraries of compounds is well-established as a high-throughput method for detecting and analyzing interactions in both biological and chemical systems. Arrays and libraries can be composed from various types of molecules, ranging from small organic compounds to DNA, proteins and peptides. The applications of libraries for detecting and characterizing biological interactions are wide and diverse, including for example epitope mapping, carbohydrate arrays, enzyme binding and protein-protein interactions. Here, we will focus on the use of peptide arrays to study protein-protein interactions. Characterization of protein-protein interactions is crucial for understanding cell functionality. Using peptides, it is possible to map the precise binding sites in such complexes. Peptide array libraries usually contain partly overlapping peptides derived from the sequence of one protein from the complex of interest. The peptides are attached to a solid support using various techniques such as SPOT-synthesis and photolithography. Then, the array is incubated with the partner protein from the complex of interest. Finally, the detection of the protein-bound peptides is carried out by using immunodetection assays. Peptide array screening is semi-quantitative, and quantitative studies with selected peptides in solution are required to validate and complement the screening results. These studies can improve our fundamental understanding of cellular processes by characterizing amino acid patterns of protein-protein interactions, which may even develop into prediction algorithms. The binding peptides can then serve as a basis for the design of drugs that inhibit or activate the target protein-protein interactions. In the current review, we will introduce the recent work on this subject performed in our and in other laboratories. We will discuss the applications, advantages and disadvantages of using peptide arrays as a tool to study protein-protein interactions.     

Computational analyses of high-throughput protein-protein interaction data

Protein-protein interactions play important roles in nearly all events that take place in a cell. High-throughput experimental techniques enable the study of protein-protein interactions at the proteome scale through systematic identification of physical interactions among all proteins in an organism. High-throughput protein-protein interaction data, with ever-increasing volume, are becoming the foundation for new biological discoveries. A great challenge to bioinformatics is to manage, analyze, and model these data. In this review, we describe several databases that store, query, and visualize protein-protein interaction data. Comparison between experimental techniques shows that each high-throughput technique such as yeast two-hybrid assay or protein complex identification through mass spectrometry has its limitations in detecting certain types of interactions and they are complementary to each other. In silico methods using protein/DNA sequences, domain and structure information to predict protein-protein interaction can expand the scope of experimental data and increase the confidence of certain protein-protein interaction pairs. Protein-protein interaction data correlate with other types of data, including protein function, subcellular location, and gene expression profile. Highly connected proteins are more likely to be essential based on the analyses of the global architecture of large-scale interaction network in yeast. Use of protein-protein interaction networks, preferably in conjunction with other types of data, allows assignment of cellular functions to novel proteins and derivation of new biological pathways. As demonstrated in our study on the yeast signal transduction pathway for amino acid transport, integration of high-throughput data with traditional biology resources can transform the protein-protein interaction data from noisy information into knowledge of cellular mechanisms.     

Solid-phase parallel synthesis of 1,3,4-oxadiazole based peptidomimetic library as a potential modulator of protein-protein interactions

Design and solid phase synthesis of the 1,3,4-oxadiazole based peptidomimetic library is presented. Library synthesis starts from the coupling of the thiosemicarbazide resin with Fmoc-protected amino acid following desulfurative cyclization to 1,3,4-oxadiazoles. Following substitution of the secondary amine with the 3-nitrobenzoyl functional group and its further reduction were performed. Thus, the functionalization with amino acids could be performed on both sides of the core skeleton. After diversification and cleavage from the resin using TFA: DCM cleavage cocktail, an enantiopure library of compounds was obtained. Further evaluation of physicochemical properties was performed.     

An alpha-helical peptidomimetic inhibitor of the HIV-1 Rev-RRE interaction

The interaction between the HIV-1 Rev protein and the Rev-Responsive Element (RRE) RNA is an attractive target for anti-viral therapy. We have designed alpha-helical peptidomimetics of Rev-like peptides using side chain-side chain macrolactam formation between positions i and i+4. One peptidomimetic having an appropriate location, orientation, and length of the macrolactam exhibited both significant helical character and specific RRE binding. This molecule displays 2-fold greater RNA specificity than the wild-type Rev peptide and more than 20-fold greater specificity than an uncyclized control peptide. Thus, specific, high affinity recognition of the RRE is feasible utilizing a small, relatively rigid peptidomimetic scaffold.     

Computational methods for protein-protein interaction and their application

Protein-protein interactions play a central role in numerous processes in cell and are one of the main research fields in current functional proteomics. The increase of finished genomic sequences has greatly stimulated the progress for detecting the functions of the genes and their encoded proteins. As complementary ways to the high through-put experimental methods, various methods of bioinformatics have been developed for the study of the protein-protein interaction. These methods range from the sequence homology-based to the genomic-context based. Recently, it tends to integrate the data from different methods to build the protein-protein interaction network, and to predict the protein function from the analysis of the network structure. Efforts are ongoing to improve these methods and to search for novel aspects in genomes that could be exploited for function prediction. This review highlights the recent advances of the bioinformatics methods in protein-protein interaction researches. In the end, the application of the protein-protein interaction has also been discussed.     

Recent advances in peptide and peptidomimetic stereoisomer separations by capillary electromigration techniques

As the stereochemistry of peptides determines their physicochemical properties and biological activities, analytical methods able to discriminate between peptide stereoisomers are important especially with regard to pharmaceutical peptides and peptidomimetics. The present review summarizes recent developments in peptide and peptidomimetic stereoisomer separations by capillary electromigration techniques. The majority of separations were performed by CE while only few reports have been published on the subject of electrochromatography. In addition to systematic studies on the applicability of certain buffer additives and the evaluation of specific experimental conditions, there have been attempts to understand the mechanistic aspects of peptide stereoisomer separations as well as to analyze the structure of peptide-CD complexes.     

Molecular aptamers for real-time protein-protein interaction study

Protein-protein interactions play critical roles in cellular functions, but current techniques for real-time study of these interactions are limited. We report the real-time monitoring of protein-protein interactions without labeling either of the two interacting proteins; this procedure poses minimum effects on the binding properties of the proteins. Our strategy uses a protein/aptamer complex to probe the interactions in a competitive assay where the binding of an aptamer to its target protein is altered by a second protein that interacts with the target protein. Two signal transduction strategies, fluorescence resonance energy transfer (FRET) and fluorescence anisotropy, have been designed to study the interactions of human alpha-thrombin with different proteins by using two aptamers specific for two binding sites on alpha-thrombin. Our method has been shown to be simple and effective, does not require labeling of proteins, makes use of easily obtainable aptamers, provides detailed protein-protein interaction information and has excellent sensitivity for protein detection and protein-protein interaction studies. The FRET and the fluorescent anisotropy approaches complement each other in providing insight into the kinetics, mechanisms, binding sites and binding dynamics of the interacting proteins.     

Monitoring the effects of antagonists on protein-protein interactions with NMR spectroscopy

We describe an NMR method that directly monitors the influence of ligands on protein-protein interactions. For a two-protein interaction complex, the size of one component should be small enough (less than ca. 15 kDa) to provide a good quality (15)N((13)C) HSQC spectrum after (15)N((13)C) labeling. The size of the second unlabeled component should be large enough so that the molecular weight of the preformed complex is larger than ca. 40 kDa. When the smaller protein binds to a larger one, broadening of NMR resonances results in the disappearance of most of its cross-peaks in the HSQC spectrum. Addition of an antagonist that can dissociate the complex would restore the HSQC spectrum of the smaller component. The method directly shows whether an antagonist releases proteins in their wild-type folded states or whether it induces their denaturation, partial unfolding, or precipitation. We illustrate the method by studying lead compounds that have recently been reported to block the MDM2-p53 interaction. Activation of p53 in tumor cells by inhibiting its interaction with MDM2 offers new strategy for cancer therapy.     

Improving neural protein-protein interaction extraction with knowledge selection

Protein-protein interaction (PPI) extraction from published scientific literature provides additional support for precision medicine efforts. Meanwhile, knowledge bases (KBs) contain huge amounts of structured information of protein entities and their relations, which can be encoded in entity and relation embeddings to help PPI extraction. However, the prior knowledge of protein-protein pairs must be selectively used so that it is suitable for different contexts. This paper proposes a Knowledge Selection Model (KSM) to fuse the selected prior knowledge and context information for PPI extraction. Firstly, two Transformers encode the context sequence of a protein pair according to each protein embedding, respectively. Then, the two outputs are fed to a mutual attention to capture the important context features towards the protein pair. Next, the context features are used to distill the relation embedding by a knowledge selector. Finally, the selected relation embedding and the context features are concatenated for PPI extraction. Experiments on the BioCreative VI PPI dataset show that KSM achieves a new state-of-the-art performance (38.08 % F1-score) by adding knowledge selection.     

Relationship between degree-rank function and degree distribution of protein-protein interaction networks

It is argued that both the degree-rank function r=f(d), which describes the relationship between the degree d and the rank r of a degree sequence, and the degree distribution P(k), which describes the probability that a randomly chosen vertex has degree k, are important statistical properties to characterize protein-protein interaction (PPI) networks, both rank-degree plot and frequency-degree plot are reliable tools to analyze PPI networks. An exact mathematical relationship between degree-rank functions and degree distributions of PPI networks is derived. It is demonstrated that a power law degree distribution is equivalent to a power law degree-rank function only if scaling exponent is greater than 2. The puzzle that the degree distributions of some PPI networks follow a power law using frequency-degree plots, whereas the degree sequences do not follow a power law using rank-degree plots is explained using the mathematical relationship.     

Design and synthesis of novel conformationally restricted peptide secondary structure mimetics

[structure: see text] A facile synthesis of the novel conformationally restricted reverse turn mimetic is described. The key features are the preparation of the alpha-keto amide and tandem bicyclic ring formation.     

Biomarkers for ischemic stroke subtypes: A protein-protein interaction analysis

According to the Trial of Org 10172 in Acute Stroke Treatment, ischemic stroke is classified into five subtypes. However, the predictive biomarkers of ischemic stroke subtypes are still largely unknown. The utmost objective of this study is to map, construct and analyze protein-protein interaction (PPI) networks for all subtypes of ischemic stroke, and to suggest the predominant biological pathways for each subtypes. Through 6285 protein data retrieved from PolySearch2 and STRING database, the first PPI networks for all subtypes of ischemic stroke were constructed. Notably, F2 and PLG were identified as the critical proteins for large artery atherosclerosis (LAA), lacunar, cardioembolic, stroke of other determined etiology (SOE) and stroke of undetermined etiology (SUE). Gene ontology and DAVID analysis revealed that GO:0030193 regulation of blood coagulation and GO:0051917 regulation of fibrinolysis were the important functional clusters for all the subtypes. In addition, inflammatory pathway was the key etiology for LAA and lacunar, while FOS and JAK2/STAT3 signaling pathways might contribute to cardioembolic stroke. Due to many risk factors associated with SOE and SUE, the precise etiology for these two subtypes remained to be concluded.     

Effect of the peptide secondary structure on the peptide amphiphile supramolecular structure and interactions

Bottom-up fabrication of self-assembled nanomaterials requires control over forces and interactions between building blocks. We report here on the formation and architecture of supramolecular structures constructed from two different peptide amphiphiles. Inclusion of four alanines between a 16-mer peptide and a 16 carbon long aliphatic tail resulted in a secondary structure shift of the peptide headgroups from α helices to β sheets. A concomitant shift in self-assembled morphology from nanoribbons to core-shell worm-like micelles was observed by cryogenic transmission electron microscopy (cryo-TEM) and atomic force microscopy (AFM). In the presence of divalent magnesium ions, these a priori formed supramolecular structures interacted in distinct manners, highlighting the importance of peptide amphiphile design in self-assembly.     

Characterization of the allosteric inhibition of a protein-protein interaction by mass spectrometry

The allosteric inhibition of the lymphocyte function associated antigen-1/intercellullar adhesion molecule (LFA-1/ICAM-1) interaction, by a class of small molecules, is characterized by a battery of mass spectrometric techniques. Binding of hydantoins to the I domain of LFA-1 is observed by size exclusion chromatography/mass spectrometry (SEC/MS) and by direct electrospray ionization mass spectrometry (ESI/MS). A photoactive hydantoin analog specifically labels an amino acid residue of LFA-1 I domain. Competition with this photoaffinity labeling by a panel of inhibitors is correlated with their Kd's for inhibition of the LFA-1/ICAM interaction. Alterations to the tertiary structure of LFA-1 I domain, upon compound binding, are inferred from perturbation in the ESI mass spectrum of the polypeptide's charge state distribution and by an altered level of nonspecific multimer formation. The results demonstrate specific, stoichiometric, reversible binding of the hydantoins to LFA-1. They further show correlation of this binding with activity and indicate alterations in the polypeptide's tertiary structure, on hydantoin binding, consistent with the proposed mechanism for inhibition of the protein-protein interaction.     

Reconstruction and crosstalk of protein-protein interaction networks of Wnt and Hedgehog signaling in Drosophila melanogaster

In the last few years, researchers have an intense interest in the evolutionarily conserved signaling pathways which have crucial roles during embryonic development. The most intriguing factor of this interest is that malfunctioning of these signaling pathways (Hedgehog, Notch, Wnt etc.) leads to several human diseases, especially to cancer. This study deals with the β-catenin dependent branch of Wnt signaling and the Hedgehog signaling pathways which offer potential targeting points for cancer drug development. The identification of all proteins functioning in these signaling networks is crucial for the efforts of preventing tumor formation. Here, through integration of protein-protein interaction data and Gene Ontology annotations, Wnt/β-catenin and Hedgehog signaling networks consisting of proteins that have statistically high probability of being biologically related to these signaling pathways were reconstructed in Drosophila melanogaster. Next, by the structural network analyses, the crucial components functioning in these pathways were identified. The proteins Arm, Frizzled receptors (Fz and Fz2), Arr, Apc, Axn, Ci and Ptc were detected as the key proteins in these networks. Futhermore, the hub protein Mer having tumor suppressor function may be proposed as a putative drug target for cancer and deserves further investigation via experimental methods. Finally, the crosstalk analysis between the reconstructed networks reveals that these two signaling networks crosstalk to each other.     

Proteomic dissection of biological pathways/processes through profiling protein-protein interaction networks

Cellular functions, either under the normal or pathological conditions or under different stresses, are the results of the coordinated action of multiple proteins interacting in macromolecular complexes or assemblies. The precise determination of the specific composition of protein complexes, especially using scalable and high-throughput methods, represents a systematic approach toward revealing particular cellular biological functions. In this regard, the direct profiling protein-protein interactions (PPIs...