Characterization Of Protein Interactions Research Articles

Investigating Protein-Nucleic Acid Binding Interactions with Diethylpyrocarbonate Covalent Labeling-Mass Spectrometry.

Nucleic acids are important biomolecules that facilitate numerous cellular functions and have in recent years become promising candidates for treating disease. Consequently, there is a need for methods to characterize protein interactions with these molecules. Here, we demonstrate that diethylpyrocarbonate (DEPC) covalent labeling-mass spectrometry (CL-MS) can provide structural information for protein-nucleic acid binding by characterizing the binding sites of two DNA aptamers specific to thrombin. Reductions in thrombin labeling are observed at the pair's binding interfaces. Furthermore, we find that binding of the aptamers causes changes in labeling at residues in the thrombin active site and known exosites for each aptamer, showcasing the sensitivity of DEPC CL-MS to significant allosteric changes.

Label-Free Tracking of Proteins through Plasmon-Enhanced Interference.

Single unmodified biomolecules in solution can be observed and characterized by interferometric imaging approaches; however, Rayleigh scattering limits this to larger proteins (typically >30 kDa). We observe real-time image tracking of unmodified proteins down to 14 kDa using interference imaging enhanced by surface plasmons launched at an aperture in a metal film. The larger proteins show slower diffusion, quantified by tracking. When the diffusing protein is finally trapped by the nanoaperture, we perform complementary power spectral density and noise amplitude analysis, which gives information about the protein. This approach allows for rapid protein characterization with minimal sample preparation and opens the door to characterizing protein interactions in real time.

Identification and characterization of protein interactions with the major Niemann-Pick type C disease protein in yeast reveals pathways of therapeutic potential.

Niemann-Pick type C (NP-C) disease is a rare lysosomal storage disease caused by mutations in NPC1 (95% cases) or NPC2 (5% cases). These proteins function together in cholesterol egress from the lysosome, whereby upon mutation, cholesterol and other lipids accumulate causing major pathologies. However, it is not fully understood how cholesterol is transported from NPC1 residing at the lysosomal membrane to the endoplasmic reticulum (ER) and plasma membrane. The yeast ortholog of NPC1, Niemann-Pick type C-related protein-1 (Ncr1), functions similarly to NPC1; when transfected into a mammalian cell lacking NPC1, Ncr1 rescues the diagnostic hallmarks of cholesterol and sphingolipid accumulation. Here, we aimed to identify and characterize protein-protein interactions (PPIs) with the yeast Ncr1 protein. A genome-wide split-ubiquitin membrane yeast two-hybrid (MYTH) protein interaction screen identified 11 ER membrane-localized, full-length proteins interacting with Ncr1 at the lysosomal/vacuolar membrane. These highlight the importance of ER-vacuole membrane interface and include PPIs with the Cyb5/Cbr1 electron transfer system, the ceramide synthase complex, and the Sec61/Sbh1 protein translocation complex. These PPIs were not detected in a sterol auxotrophy condition and thus depend on normal sterol metabolism. To provide biological context for the Ncr1-Cyb5 PPI, a yeast strain lacking this PPI (via gene deletions) exhibited altered levels of sterols and sphingolipids including increased levels of glucosylceramide that mimic NP-C disease. Overall, the results herein provide new physical and genetic interaction models to further use the yeast model of NP-C disease to better understand human NP-C disease.

Characterizing protein interactions in biomolecular phase separation.

Protein interactions are ubiquitous to life as they enable essential functions such as protein synthesis, signalling cascades or sequestration of foreign antigens. Pairwise interactions, in particular, have been studied extensively for centuries leading to the development of various techniques that can characterise binding events reliably. Recently, phase separation of proteins into membraneless compartments within the cellular environment has emerged as novel context of protein interactions. Although, great progress has been made in the discovery and study of phase separating systems, the protein interactions underlying these macromolecular assemblies remain difficult to characterize given their transient, multivalent nature. Here we show novel approaches for characterizing protein interactions within phase separating systems involving the usage of microfluidic tools such as microfluidic diffusional sizing and construction of tie lines from confocal detection. In doing so, we discover DNA-induced phase separation of antibodies as well as the formation of nanoscale clusters in antibody-DNA mixtures. Furthermore, we characterize the mechanistic impact of an RNA aptamer on multicomponent FUS condensate stability and determine the interactions of salt excipients with the protein dense phase. Crucially, establishing such novel pathways for characterizing protein interactions in phase separating systems will be key to accelerate the discovery of molecules that amend or interfere with phase separation, hence, show the potential to alter disease courses.

PPIDomainMiner: Inferring domain-domain interactions from multiple sources of protein-protein interactions.

Many biological processes are mediated by protein-protein interactions (PPIs). Because protein domains are the building blocks of proteins, PPIs likely rely on domain-domain interactions (DDIs). Several attempts exist to infer DDIs from PPI networks but the produced datasets are heterogeneous and sometimes not accessible, while the PPI interactome data keeps growing.We describe a new computational approach called “PPIDM” (Protein-Protein Interactions Domain Miner) for inferring DDIs using multiple sources of PPIs. The approach is an extension of our previously described “CODAC” (Computational Discovery of Direct Associations using Common neighbors) method for inferring new edges in a tripartite graph. The PPIDM method has been applied to seven widely used PPI resources, using as “Gold-Standard” a set of DDIs extracted from 3D structural databases. Overall, PPIDM has produced a dataset of 84,552 non-redundant DDIs. Statistical significance (p-value) is calculated for each source of PPI and used to classify the PPIDM DDIs in Gold (9,175 DDIs), Silver (24,934 DDIs) and Bronze (50,443 DDIs) categories. Dataset comparison reveals that PPIDM has inferred from the 2017 releases of PPI sources about 46% of the DDIs present in the 2020 release of the 3did database, not counting the DDIs present in the Gold-Standard. The PPIDM dataset contains 10,229 DDIs that are consistent with more than 13,300 PPIs extracted from the IMEx database, and nearly 23,300 DDIs (27.5%) that are consistent with more than 214,000 human PPIs extracted from the STRING database. Examples of newly inferred DDIs covering more than 10 PPIs in the IMEx database are provided.Further exploitation of the PPIDM DDI reservoir includes the inventory of possible partners of a protein of interest and characterization of protein interactions at the domain level in combination with other methods. The result is publicly available at http://ppidm.loria.fr/.

Size-based characterization of adalimumab and TNF-\u03b1 interactions using flow induced dispersion analysis: assessment of avidity-stabilized multiple bound species

The understanding and characterization of protein interactions is crucial for elucidation of complicated biomolecular processes as well as for the development of new biopharmaceutical therapies. Often, protein interactions involve multiple binding, avidity, oligomerization, and are dependent on the local environment. Current analytical methodologies are unable to provide a detailed mechanistic characterization considering all these parameters, since they often rely on surface immobilization, cannot measure under biorelevant conditions, or do not feature a structurally-related readout for indicating formation of multiple bound species. In this work, we report the use of flow induced dispersion analysis (FIDA) for in-solution characterization of complex protein interactions under in vivo like conditions. FIDA is an immobilization-free ligand binding methodology employing Taylor dispersion analysis for measuring the hydrodynamic radius (size) of biomolecular complexes. Here, the FIDA technology is utilized for a size-based characterization of the interaction between TNF-α and adalimumab. We report concentration-dependent complex sizes, binding affinities (Kd), kinetics, and higher order stoichiometries, thus providing essential information on the TNF-α–adalimumab binding mechanism. Furthermore, it is shown that the avidity stabilized complexes involving formation of multiple non-covalent bonds are formed on a longer timescale than the primary complexes formed in a simple 1 to 1 binding event.

The characterization of protein interactions - what, how and how much?

Protein interactions underlie most molecular events in biology. Many methods have been developed to identify protein partners, to measure the affinity with which these biomolecules interact and to characterise the structures of the complexes. Each approach has its own advantages and limitations, and it can be difficult for the newcomer to determine which methodology would best suit their system. This review provides an overview of many of the techniques most widely used to identify protein partners, assess stoichiometry and binding affinity, and determine low-resolution models for complexes. Key methods covered include: yeast two-hybrid analysis, affinity purification mass spectrometry and proximity labelling to identify partners; size-exclusion chromatography, scattering methods, native mass spectrometry and analytical ultracentrifugation to estimate stoichiometry; isothermal titration calorimetry, biosensors and fluorometric methods (including microscale thermophoresis, anisotropy/polarisation, resonance energy transfer, AlphaScreen, and differential scanning fluorimetry) to measure binding affinity; and crosslinking and hydrogen-deuterium exchange mass spectrometry to probe the structure of complexes.

High-Throughput Identification of Nuclear Envelope Protein Interactions in Schizosaccharomyces pombe Using an Arrayed Membrane Yeast-Two Hybrid Library.

The nuclear envelope (NE) contains a specialized set of integral membrane proteins that maintain nuclear shape and integrity and influence chromatin organization and gene expression. Advances in proteomics techniques and studies in model organisms have identified hundreds of proteins that localize to the NE. However, the function of many of these proteins at the NE remains unclear, in part due to a lack of understanding of the interactions that these proteins participate in at the NE membrane. To assist in the characterization of NE transmembrane protein interactions we developed an arrayed library of integral and peripheral membrane proteins from the fission yeast Schizosaccharomyces pombe for high-throughput screening using the split-ubiquitin based membrane yeast two -hybrid system. We used this approach to characterize protein interactions for three conserved proteins that localize to the inner nuclear membrane: Cut11/Ndc1, Lem2 and Ima1/Samp1/Net5. Additionally, we determined how the interaction network for Cut11 is altered in canonical temperature-sensitive cut11-ts mutants. This library and screening approach is readily applicable to characterizing the interactomes of integral membrane proteins localizing to various subcellular compartments.

Bait Correlation Improves Interactor Identification by Tandem Mass Tag-Affinity Purification-Mass Spectrometry.

The quantitative multiplexing capacity of isobaric tandem mass tags (TMT) has increased the throughput of affinity purification mass spectrometry (AP-MS) to characterize protein interaction networks of immunoprecipitated bait proteins. However, variable bait levels between replicates can convolute interactor identification. We compared the Student's t-test and Pearson's R correlation as methods to generate t-statistics and assessed the significance of interactors following TMT-AP-MS. Using a simple linear model of protein recovery in immunoprecipitates to simulate reporter ion ratio distributions, we found that correlation-derived t-statistics protect against bait variance while robustly controlling type I errors (false positives). We experimentally determined the performance of these two approaches for determining t-statistics under two experimental conditions: irreversible prey association to the Hsp40 mutant DNAJB8H31Q followed by stringent washing, and reversible association to 14-3-3ζ with gentle washing. Correlation-derived t-statistics performed at least as well as Student's t-statistics for each sample and with substantial improvement in performance for experiments with high bait-level variance. Deliberately varying bait levels over a large range fails to improve selectivity but does increase the robustness between runs. The use of correlation-derived t-statistics should improve identification of interactors using TMT-AP-MS. Data are available via ProteomeXchange with identifier PXD016613.

Locus-Specific Chromatin Proteome Revealed by Mass Spectrometry-Based CasID.

Biotin proximity labeling has largely extended the toolbox of mass spectrometry-based interactomics. To date, BirA, engineered BirA variants, or other biotinylating enzymes have been widely applied to characterize protein interactions. By implementing chromatin purification-based methods the genome-wide interactome of proteins can be defined. However, acquiring a high-resolution interactome of a single genomic locus preferably by multiplexed measurements of several distinct genomic loci in parallel remains challenging. We recently developed CasID, a novel approach where the catalytically inactive Cas9 (dCas9) is coupled to the promiscuous biotin ligase BirA (BirA∗). With CasID, first the local proteome at repetitive telomeric, major satellite, and minor satellite regions was determined. With more efficient biotin ligases and sensitive mass spectrometry, others have successfully identified the chromatin composition at even smaller genomic, non-repetitive regions of a few hundred base pairs in length. Here, we summarize the most recent developments towards interactomics at a single genomic locus and provide a step-by-step protocol based on the CasID approach.

Genetic variation across RNA metabolism and cell death gene networks is implicated in the semantic variant of primary progressive aphasia

The semantic variant of primary progressive aphasia (svPPA) is a clinical syndrome characterized by neurodegeneration and progressive loss of semantic knowledge. Unlike many other forms of frontotemporal lobar degeneration (FTLD), svPPA has a highly consistent underlying pathology composed of TDP-43 (a regulator of RNA and DNA transcription metabolism). Previous genetic studies of svPPA are limited by small sample sizes and a paucity of common risk variants. Despite this, svPPA’s relatively homogenous clinicopathologic phenotype makes it an ideal investigative model to examine genetic processes that may drive neurodegenerative disease. In this study, we used GWAS metadata, tissue samples from pathologically confirmed frontotemporal lobar degeneration, and in silico techniques to identify and characterize protein interaction networks associated with svPPA risk. We identified 64 svPPA risk genes that interact at the protein level. The protein pathways represented in this svPPA gene network are critical regulators of RNA metabolism and cell death, such as SMAD proteins and NOTCH1. Many of the genes in this network are involved in TDP-43 metabolism. Contrary to the conventional notion that svPPA is a clinical syndrome with few genetic risk factors, our analyses show that svPPA risk is complex and polygenic in nature. Risk for svPPA is likely driven by multiple common variants in genes interacting with TDP-43, along with cell death,x` working in combination to promote neurodegeneration.

Dynamic and Sequential Protein Reconstitution on Negatively Curved Membranes by Giant Vesicles Fusion

In vitro investigation of the interaction between proteins and positively curved membranes can be performed using a classic nanotube pulling method. However, characterizing protein interaction with negatively curved membranes still represents a formidable challenge. Here, we describe our recently developed approach based on laser-triggered Giant Unilamellar Vesicles (GUVs) fusion. Our protocol allows sequential addition of proteins to a negatively curved membrane, while at the same time controlling the buffer composition, lipid composition and membrane tension. Moreover, this method does not require a step of protein detachment, greatly simplifying the process of protein encapsulation over existing methods.

Methods to Characterize Protein Interactions with β-Arrestin In Cellulo.

β-Arrestins 1 and 2 (β-arr1 and β-arr2) are ubiquitous proteins with common and distinct functions. They were initially identified as proteins recruited to stimulated G protein-coupled receptors (GPCRs), regulating their desensitization and internalization. The discovery that β-arrs could alsointeract with more than 400 non-GPCR protein partners brought to light their central roles as multifunctional scaffold proteins regulating multiple signalling pathways from the plasma membrane to the nucleus, downstream of GPCRs or independently from these receptors. Through the regulation of the activities and subcellular localization of their binding partners, β-arrs control various cell processes such as proliferation, cytoskeletal rearrangement, cell motility, and apoptosis. Thus, the identification of β-arrs binding partners and the characterization of their mode of interaction in cells are central to the understanding of their function. Here we provide methods to explore the molecular interaction of β-arrs with other proteins in cellulo.

FCS Analysis of Protein Mobility on Lipid Monolayers

In vitro membrane model systems are used to dissect complex biological phenomena under controlled unadulterated conditions. In this context, lipid monolayers are a powerful tool to particularly study the influence of lipid packing on the behavior of membrane proteins. Here, monolayers deposited in miniaturized fixed area-chambers, which require only minute amounts of protein, were used and shown to faithfully reproduce the characteristics of Langmuir monolayers. This assay is ideally suited to be combined with single-molecule sensitive fluorescence correlation spectroscopy (FCS) to characterize diffusion dynamics. Our results confirm the influence of lipid packing on lipid mobility and validate the use of FCS as an alternative to conventional surface pressure measurements for characterizing the monolayer. Furthermore, we demonstrate the effect of lipid density on the diffusional behavior of membrane-bound components. We exploit the sensitivity of FCS to characterize protein interactions with the lipid monolayer in a regime in which the monolayer physical properties are not altered. To demonstrate the potential of our approach, we analyzed the diffusion behavior of objects of different nature, ranging from a small peptide to a large DNA-based nanostructure. Moreover, in this work we quantify the surface viscosity of lipid monolayers. We present a detailed strategy for the conduction of point FCS experiments on lipid monolayers, which is the first step toward extensive studies of protein-monolayer interactions.

PD-1/PD-L1 binding studies using microscale thermophoresis

The characterization of protein interactions has become essential in many fields of life science, especially drug discovery. Microscale thermophoresis (MST) is a powerful new method for the quantitative analysis of protein-protein interactions (PPIs) with low sample consumption. In addition, one of the major advantages of this technique is that no tedious purification step is necessary to access the protein of interest. Here, we describe a protocol using MST to determine the binding affinity of the PD-1/PD-L1 couple, which is involved in tumour escape processes, without purification of the target protein from cell lysates. The method requires the overexpression of fluorescent proteins in CHO-K1 cells and describes the optimal conditions for determining the dissociation constant. The protocol has a variety of potential applications in studying the interactions of these proteins with small molecules and demonstrates that MST is a valuable method for studying the PD-1/PD-L1 pathway.

High-throughput characterization of protein–protein interactions by reprogramming yeast mating

High-throughput methods for screening protein-protein interactions enable the rapid characterization of engineered binding proteins and interaction networks. While existing approaches are powerful, none allow quantitative library-on-library characterization of protein interactions in a modifiable extracellular environment. Here, we show that sexual agglutination of Saccharomyces cerevisiae can be reprogrammed to link interaction strength with mating efficiency using synthetic agglutination (SynAg). Validation of SynAg with 89 previously characterized interactions shows a log-linear relationship between mating efficiency and protein binding strength for interactions with Kds ranging from below 500 pM to above 300 μM. Using induced chromosomal translocation to pair barcodes representing binding proteins, thousands of distinct interactions can be screened in a single pot. We demonstrate the ability to characterize protein interaction networks in a modifiable environment by introducing a soluble peptide that selectively disrupts a subset of interactions in a representative network by up to 800-fold. SynAg enables the high-throughput, quantitative characterization of protein-protein interaction networks in a fully defined extracellular environment at a library-on-library scale.

Student award for outstanding research winner in the Ph.D. category for the 2017 society for biomaterials annual meeting and exposition, april 5-8, 2017, Minneapolis, Minnesota: Characterization of protein interactions with molecularly imprinted hydrogels that possess engineered affinity for high isoelectric point biomarkers.

Molecularly imprinted polymers (MIPs) with selective affinity for protein biomarkers could find extensive utility as environmentally robust, cost-efficient biomaterials for diagnostic and therapeutic applications. In order to develop recognitive, synthetic biomaterials for prohibitively expensive protein biomarkers, we have developed a molecular imprinting technique that utilizes structurally similar, analogue proteins. Hydrogel microparticles synthesized by molecular imprinting with trypsin, lysozyme, and cytochrome c possessed an increased affinity for alternate high isoelectric point biomarkers both in isolation and plasma-mimicking adsorption conditions. Imprinted and non-imprinted P(MAA-co-AAm-co-DEAEMA) microgels containing PMAO-PEGMA functionalized polycaprolactone nanoparticles were net-anionic, polydisperse, and irregularly shaped. MIPs and control non-imprinted polymers (NIPs) exhibited regions of Freundlich and BET isotherm adsorption behavior in a range of non-competitive protein solutions, where MIPs exhibited enhanced adsorption capacity in the Freundlich isotherm regions. In a competitive condition, imprinting with analogue templates (trypsin, lysozyme) increased the adsorption capacity of microgels for cytochrome c by 162% and 219%, respectively, as compared to a 122% increase provided by traditional bulk imprinting with cytochrome c. Our results suggest that molecular imprinting with analogue protein templates is a viable synthetic strategy for enhancing hydrogel-biomarker affinity and promoting specific protein adsorption behavior in biological fluids. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 1565-1574, 2017.

Modeling Binding Affinity of Pathological Mutations for Computational Protein Design.

An important aspect of protein functionality is the formation of specific complexes with other proteins, which are involved in the majority of biological processes. The functional characterization of such interactions at molecular level is necessary, not only to understand biological and pathological phenomena but also to design improved, or even new interfaces, or to develop new therapeutic approaches. X-ray crystallography and NMR spectroscopy have increased the number of 3D protein complex structures deposited in the Protein Data Bank (PDB). However, one of the more challenging objectives in biological research is to functionally characterize protein interactions and thus identify residues that significantly contribute to the binding. Considering that the experimental characterization of protein interfaces remains expensive, time-consuming, and labor-intensive, computational approaches represent a significant breakthrough in proteomics, assisting or even replacing experimental efforts. Thanks to the technological advances in computing and data processing, these techniques now cover a vast range of protocols, from the estimation of the evolutionary conservation of amino acid positions in a protein, to the energetic contribution of each residue to the binding affinity. In this chapter, we review several existing computational protocols to model the phylogenetic, structural, and energetic properties of residues within protein-protein interfaces.

Developing an Acidic Residue Reactive and Sulfoxide-Containing MS-Cleavable Homobifunctional Cross-Linker for Probing Protein-Protein Interactions.

Cross-linking mass spectrometry (XL-MS) has become a powerful strategy for defining protein–protein interactions and elucidating architectures of large protein complexes. However, one of the inherent challenges in MS analysis of cross-linked peptides is their unambiguous identification. To facilitate this process, we have previously developed a series of amine-reactive sulfoxide-containing MS-cleavable cross-linkers. These MS-cleavable reagents have allowed us to establish a common robust XL-MS workflow that enables fast and accurate identification of cross-linked peptides using multistage tandem mass spectrometry (MSn). Although amine-reactive reagents targeting lysine residues have been successful, it remains difficult to characterize protein interaction interfaces with little or no lysine residues. To expand the coverage of protein interaction regions, we present here the development of a new acidic residue-targeting sulfoxide-containing MS-cleavable homobifunctional cross-linker, dihydrazide sulfoxide (DHSO). We demonstrate that DHSO cross-linked peptides display the same predictable and characteristic fragmentation pattern during collision induced dissociation as amine-reactive sulfoxide-containing MS-cleavable cross-linked peptides, thus permitting their simplified analysis and unambiguous identification by MSn. Additionally, we show that DHSO can provide complementary data to amine-reactive reagents. Collectively, this work not only enlarges the range of the application of XL-MS approaches but also further demonstrates the robustness and applicability of sulfoxide-based MS-cleavability in conjunction with various cross-linking chemistries.

Predicting protein–protein interaction sites using modified support vector machine

Protein–protein interaction plays a fundamental role in many biological processes and diseases. Characterizing protein interaction sites is crucial for the understanding of the mechanism of protein–protein interaction and their cellular functions. In this paper, we proposed a method based on integrated support vector machine (SVM) with a hybrid kernel to predict protein interaction sites. First, a number of features of the protein interaction sites were extracted. Secondly, the technique of sliding window was used to construct a protein feature space based on the influence of the adjacent residues. Thirdly, to avoid the impact of imbalance of the data set on prediction accuracy, we employed boost-strap to re-sample the data. Finally, we built a SVM classifier, whose hybrid kernel comprised a Gaussian kernel and a Polynomial kernel. In addition, an improved particle swarm optimization (PSO) algorithm was applied to optimize the SVM parameters. Experimental results show that the PSO-optimized SVM classifier outperforms existing methods.