Protein Interaction Strength Research Articles

Improved protein interaction models predict differences in complexes between human cell lines.

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Kinetic interplay between droplet maturation and coalescence modulates shape of aged protein condensates

Biomolecular condensates formed by the process of liquid–liquid phase separation (LLPS) play diverse roles inside cells, from spatiotemporal compartmentalisation to speeding up chemical reactions. Upon maturation, the liquid-like properties of condensates, which underpin their functions, are gradually lost, eventually giving rise to solid-like states with potential pathological implications. Enhancement of inter-protein interactions is one of the main mechanisms suggested to trigger the formation of solid-like condensates. To gain a molecular-level understanding of how the accumulation of stronger interactions among proteins inside condensates affect the kinetic and thermodynamic properties of biomolecular condensates, and their shapes over time, we develop a tailored coarse-grained model of proteins that transition from establishing weak to stronger inter-protein interactions inside condensates. Our simulations reveal that the fast accumulation of strongly binding proteins during the nucleation and growth stages of condensate formation results in aspherical solid-like condensates. In contrast, when strong inter-protein interactions appear only after the equilibrium condensate has been formed, or when they accumulate slowly over time with respect to the time needed for droplets to fuse and grow, spherical solid-like droplets emerge. By conducting atomistic potential-of-mean-force simulations of NUP-98 peptides—prone to forming inter-protein beta-sheets—we observe that formation of inter-peptide beta-sheets increases the strength of the interactions consistently with the loss of liquid-like condensate properties we observe at the coarse-grained level. Overall, our work aids in elucidating fundamental molecular, kinetic, and thermodynamic mechanisms linking the rate of change in protein interaction strength to condensate shape and maturation during ageing.

The Arabidopsis Rho of Plants GTPase ROP1 Is a Potential Calcium-Dependent Protein Kinase (CDPK) Substrate.

Plant Rho-type GTPases (ROPs) are versatile molecular switches involved in a number of signal transduction pathways. Although it is well known that they are indirectly linked to protein kinases, our knowledge about their direct functional interaction with upstream or downstream protein kinases is scarce. It is reasonable to suppose that similarly to their animal counterparts, ROPs might also be regulated by phosphorylation. There is only, however, very limited experimental evidence to support this view. Here, we present the analysis of two potential phosphorylation sites of AtROP1 and two types of potential ROP-kinases. The S74 site of AtROP1 has been previously shown to potentially regulate AtROP1 activation dependent on its phosphorylation state. However, the kinase phosphorylating this evolutionarily conserved site could not be identified: we show here that despite of the appropriate phosphorylation site consensus sequences around S74 neither the selected AGC nor CPK kinases phosphorylate S74 of AtROP1 in vitro. However, we identified several phosphorylation sites other than S74 for the CPK17 and 34 kinases in AtROP1. One of these sites, S97, was tested for biological relevance. Although the mutation of S97 to alanine (which cannot be phosphorylated) or glutamic acid (which mimics phosphorylation) somewhat altered the protein interaction strength of AtROP1 in yeast cells, the mutant proteins did not modify pollen tube growth in an in vivo test.

Role of Transmembrane Proteins for Phase Separation and Domain Registration in Asymmetric Lipid Bilayers.

It is well known that the formation and spatial correlation of lipid domains in the two apposed leaflets of a bilayer are influenced by weak lipid–lipid interactions across the bilayer’s midplane. Transmembrane proteins span through both leaflets and thus offer an alternative domain coupling mechanism. Using a mean-field approximation of a simple bilayer-type lattice model, with two two-dimensional lattices stacked one on top of the other, we explore the role of this “structural” inter-leaflet coupling for the ability of a lipid membrane to phase separate and form spatially correlated domains. We present calculated phase diagrams for various effective lipid–lipid and lipid–protein interaction strengths in membranes that contain a binary lipid mixture in each leaflet plus a small amount of added transmembrane proteins. The influence of the transmembrane nature of the proteins is assessed by a comparison with “peripheral” proteins, which result from the separation of one single integral protein into two independent units that are no longer structurally connected across the bilayer. We demonstrate that the ability of membrane-spanning proteins to facilitate domain formation requires sufficiently strong lipid–protein interactions. Weak lipid–protein interactions generally tend to inhibit phase separation in a similar manner for transmembrane as for peripheral proteins.

Analyzing protein-ligand and protein-interface interactions using high pressure

All protein function is based on interactions with the environment. Proteins can bind molecules for their transport, their catalytic conversion, or for signal transduction. They can bind to each other, and they adsorb at interfaces, such as lipid membranes or material surfaces. An experimental characterization is needed to understand the underlying mechanisms, but also to make use of proteins in biotechnology or biomedicine. When protein interactions are studied under high pressure, volume changes are revealed that directly describe spatial contributions to these interactions. Moreover, the strength of protein interactions with ligands or interfaces can be tuned in a smooth way by pressure modulation, which can be utilized in the design of drugs and bio-responsive interfaces. In this short review, selected studies of protein-ligand and protein-interface interactions are presented that were carried out under high pressure. Furthermore, a perspective on bio-responsive interfaces is given where protein-ligand binding is applied to create functional interfacial structures.

Investigating How Membrane Localization Regulates Protein Assembly During Clathrin-Mediated Endocytosis

Cell growth, development and proper cellular function depends heavily on the precisely controlled mechanisms of entry and exit of macromolecules into and out of cells. Clathrin mediated endocytosis (CME) is one such process that mediates the entry of many biologically important molecules, including growth factors, nutrient molecules and cell signaling molecules into cells. Although CME is a well-studied, multi-step process, it is still not known how the localization of components to the membrane surface can modify the strength of protein interactions and control the speed and stability of clathrin-coat assembly. It has been observed that when proteins are on the membrane surface, binding rate constants and equilibrium constants (Ka2D) are fundamentally distinct from their solution values (Ka3D). The Ka2D values control association strength between proteins on the surface and yet they have rarely been measured experimentally for any proteins. Our group has developed a theory to obtain the Ka2D from the experimentally obtained binding strengths (Ka3D). In this study, we used liposome sedimentation assays to investigate how the binding events between specific components of the CME pathway are altered by membrane localization. For our assays, we used both native (AP-2 and Clathrin) and recombinant (Epsin and AP180) proteins purified in our laboratory. The Ka3Dvalues of lipid-protein interactions were characterized using 1% PI(4,5)P2 containing liposomes via one-stage liposome sedimentation assays. Protein-protein interactions were investigated via two-stage liposome sedimentation assays. Ka3D values obtained by quantifying the amount of protein bound to the liposomes via western blotting were then used to calculate Ka2D using the above mentioned theory. Our study sheds light on how developing accurate quantitative models of assembly dynamics helps us better understand how membrane localization regulates protein assembly.

Selection of Protein–Protein Interactions of Desired Affinities with a Bandpass Circuit

We have developed a genetic circuit in Escherichia coli that can be used to select for protein–protein interactions of different strengths by changing antibiotic concentrations in the media. The genetic circuit links protein–protein interaction strength to β-lactamase activity while simultaneously imposing tuneable positive and negative selection pressure for β-lactamase activity. Cells only survive if they express interacting proteins with affinities that fall within set high- and low-pass thresholds; i.e. the circuit therefore acts as a bandpass filter for protein–protein interactions. We show that the circuit can be used to recover protein–protein interactions of desired affinity from a mixed population with a range of affinities. The circuit can also be used to select for inhibitors of protein–protein interactions of defined strength.

Looping and clustering model for the organization of protein-DNA complexes on the bacterial genome

The bacterial genome is organized by a variety of associated proteins inside a structure called the nucleoid. These proteins can form complexes on DNA that play a central role in various biological processes, including chromosome segregation. A prominent example is the large ParB-DNA complex, which forms an essential component of the segregation machinery in many bacteria. ChIP-Seq experiments show that ParB proteins localize around centromere-like parS sites on the DNA to which ParB binds specifically, and spreads from there over large sections of the chromosome. Recent theoretical and experimental studies suggest that DNA-bound ParB proteins can interact with each other to condense into a coherent 3D complex on the DNA. However, the structural organization of this protein-DNA complex remains unclear, and a predictive quantitative theory for the distribution of ParB proteins on DNA is lacking. Here, we propose the looping and clustering model, which employs a statistical physics approach to describe protein-DNA complexes. The looping and clustering model accounts for the extrusion of DNA loops from a cluster of interacting DNA-bound proteins that is organized around a single high-affinity binding site. Conceptually, the structure of the protein-DNA complex is determined by a competition between attractive protein interactions and loop closure entropy of this protein-DNA cluster on the one hand, and the positional entropy for placing loops within the cluster on the other. Indeed, we show that the protein interaction strength determines the ‘tightness’ of the loopy protein-DNA complex. Thus, our model provides a theoretical framework for quantitatively computing the binding profiles of ParB-like proteins around a cognate (parS) binding site.

Effect of enzymatic transglutaminase treatment on textural and sensory properties of low-fat UF-Feta cheese incorporated with whey proteins using response surface optimization.

شواهد علمی نشان داده است که مصرف چربی ارتباط مستقیمی با بیماری های مختلفی از جمله چاقی، دیابت، سفت شدن دیواره رگ ها و فشار خون دارد. به همین دلیل طی سال های اخیر تقاضا برای محصولات کم‌چرب افزایش چشمگیری پیدا کرده است. پنیر به‌عنوان یک فراورده لبنی با ارزش نقش پررنگی در رژیم غذایی جامعه دارد. با این حال این فراورده حاوی مقادیر بالایی از چربی می باشد که سبب ایجاد نگرانی های از جانب مصرف‌کنندگان شده است. در این پژوهش از روش سطح پاسخ به‌منظور مطالعه اثر آنزیم ترانس گلوتامیناز (2-0 واحد به ازای هرگرم پروتئین)، محلول کنسانتره پروتئینی آب پنیر یا WPC (16-0 درصد) و سطوح چربی (10-0 درصد) بر خواص کیفی و بهینه‌سازی فرمولاسیون پنیر فراپالایش کم‌چرب استفاده گردید. نتایج نشان داد که کاهش چربی باعث افزایش معنی‌دار سفتی، ارتجاع‌پذیری و میزان رطوبت می شود اما چسبندگی و امتیاز پذیرش کلی نمونه های پنیر کاهش می یابد. برخلاف WPC، تیمار آنزیمی با ترانس گلوتامیناز سفتی، پیوستگی و ارتجاع‌پذیری را بطور معنی‌داری افزایش داد اما میزان رطوبت و چسبندگی پنیر را کاهش داد. در این میان آنزیم ترانس گلوتامیناز اثر معنی‌داری بر امتیاز کلی ارزیابی حسی نداشت اما خواص حسی نمونه‌های پنیر کم‌چرب با جایگزینی WPC با ناتراوه تا میزان 8% بهبود یافت. نتایج بهینه‌سازی با طرح مرکب مرکزی نشان داد که بهترین نمونه با خواص حسی و بافتی مطلوب زمانی حاصل می‌شود که فرمول پنیر شامل 95/5% چربی، 56/0 واحد آنزیم ترانس گلوتامیناز و 79/8% محلول WPC باشد.

Simple biophysics underpins collective conformations of the intrinsically disordered proteins of the Nuclear Pore Complex.

Nuclear Pore Complexes (NPCs) are key cellular transporter that control nucleocytoplasmic transport in eukaryotic cells, but its transport mechanism is still not understood. The centerpiece of NPC transport is the assembly of intrinsically disordered polypeptides, known as FG nucleoporins, lining its passageway. Their conformations and collective dynamics during transport are difficult to assess in vivo. In vitro investigations provide partially conflicting results, lending support to different models of transport, which invoke various conformational transitions of the FG nucleoporins induced by the cargo-carrying transport proteins. We show that the spatial organization of FG nucleoporin assemblies with the transport proteins can be understood within a first principles biophysical model with a minimal number of key physical variables, such as the average protein interaction strengths and spatial densities. These results address some of the outstanding controversies and suggest how molecularly divergent NPCs in different species can perform essentially the same function.

Abundance and Temperature Dependency of Protein-Protein Interaction Revealed by Interface Structure Analysis and Stability Evolution.

Protein complexes are major forms of protein-protein interactions and implement essential biological functions. The subunit interface in a protein complex is related to its thermostability. Though the roles of interface properties in thermal adaptation have been investigated for protein complexes, the relationship between the interface size and the expression level of the subunits remains unknown. In the present work, we studied this relationship and found a positive correlation in thermophiles rather than mesophiles. Moreover, we found that the protein interaction strength in complexes is not only temperature-dependent but also abundance-dependent. The underlying mechanism for the observed correlation was explored by simulating the evolution of protein interface stability, which highlights the avoidance of misinteraction. Our findings make more complete the picture of the mechanisms for protein complex thermal adaptation and provide new insights into the principles of protein-protein interactions.

Ethanol Concentration Influences the Mechanisms of Wine Tannin Interactions with Poly(L-proline) in Model Wine.

Changes in ethanol concentration influence red wine astringency, and yet the effect of ethanol on wine tannin-salivary protein interactions is not well understood. Isothermal titration calorimetry (ITC) was used to measure the binding strength between the model salivary protein, poly(L-proline) (PLP) and a range of wine tannins (tannin fractions from a 3- and a 7-year old Cabernet Sauvignon wine) across different ethanol concentrations (5, 10, 15, and 40% v/v). Tannin-PLP interactions were stronger at 5% ethanol than at 40% ethanol. The mechanism of interaction changed for most tannin samples across the wine-like ethanol range (10-15%) from a combination of hydrophobic and hydrogen binding at 10% ethanol to only hydrogen binding at 15% ethanol. These results indicate that ethanol concentration can influence the mechanisms of wine tannin-protein interactions and that the previously reported decrease in wine astringency with increasing alcohol may, in part, relate to a decrease tannin-protein interaction strength.

Pathways for Virus Assembly around Nucleic Acids

Understanding the pathways by which viral capsid proteins assemble around their genomes could identify key intermediates as potential drug targets. In this work, we use computer simulations to characterize assembly over a wide range of capsid protein–protein interaction strengths and solution ionic strengths. We find that assembly pathways can be categorized into two classes, in which intermediates are either predominantly ordered or disordered. Our results suggest that estimating the protein–protein and the protein–genome binding affinities may be sufficient to predict which pathway occurs. Furthermore, the calculated phase diagrams suggest that knowledge of the dominant assembly pathway and its relationship to control parameters could identify optimal strategies to thwart or redirect assembly to block infection. Finally, analysis of simulation trajectories suggests that the two classes of assembly pathways can be distinguished in single-molecule fluorescence correlation spectroscopy or bulk time-resolved small-angle X-ray scattering experiments.

Coarse-grained versus atomistic simulations: realistic interaction free energies for real proteins

To assess whether two proteins will interact under physiological conditions, information on the interaction free energy is needed. Statistical learning techniques and docking methods for predicting protein-protein interactions cannot quantitatively estimate binding free energies. Full atomistic molecular simulation methods do have this potential, but are completely unfeasible for large-scale applications in terms of computational cost required. Here we investigate whether applying coarse-grained (CG) molecular dynamics simulations is a viable alternative for complexes of known structure. We calculate the free energy barrier with respect to the bound state based on molecular dynamics simulations using both a full atomistic and a CG force field for the TCR-pMHC complex and the MP1-p14 scaffolding complex. We find that the free energy barriers from the CG simulations are of similar accuracy as those from the full atomistic ones, while achieving a speedup of >500-fold. We also observe that extensive sampling is extremely important to obtain accurate free energy barriers, which is only within reach for the CG models. Finally, we show that the CG model preserves biological relevance of the interactions: (i) we observe a strong correlation between evolutionary likelihood of mutations and the impact on the free energy barrier with respect to the bound state; and (ii) we confirm the dominant role of the interface core in these interactions. Therefore, our results suggest that CG molecular simulations can realistically be used for the accurate prediction of protein-protein interaction strength. The python analysis framework and data files are available for download at http://www.ibi.vu.nl/downloads/bioinformatics-2013-btt675.tgz.

Flow Cytometric Analysis of Bimolecular Fluorescence Complementation: A High Throughput Quantitative Method to Study Protein-protein Interaction

Among methods to study protein-protein interaction inside cells, Bimolecular Fluorescence Complementation (BiFC) is relatively simple and sensitive. BiFC is based on the production of fluorescence using two non-fluorescent fragments of a fluorescent protein (Venus, a Yellow Fluorescent Protein variant, is used here). Non-fluorescent Venus fragments (VN and VC) are fused to two interacting proteins (in this case, AKAP-Lbc and PDE4D3), yielding fluorescence due to VN-AKAP-Lbc-VC-PDE4D3 interaction and the formation of a functional fluorescent protein inside cells. BiFC provides information on the subcellular localization of protein complexes and the strength of protein interactions based on fluorescence intensity. However, BiFC analysis using microscopy to quantify the strength of protein-protein interaction is time-consuming and somewhat subjective due to heterogeneity in protein expression and interaction. By coupling flow cytometric analysis with BiFC methodology, the fluorescent BiFC protein-protein interaction signal can be accurately measured for a large quantity of cells in a short time. Here, we demonstrate an application of this methodology to map regions in PDE4D3 that are required for the interaction with AKAP-Lbc. This high throughput methodology can be applied to screening factors that regulate protein-protein interaction.

Real-Time Monitoring of Relative Peptide–Protein Interaction Strengths in the Yeast Two-Hybrid System

The yeast two-hybrid (Y2H) system is one of the most technically straightforward, effective, and widely used tools for the discovery of the binary peptide or protein interactions. However, its exceptional detection sensitivity poses a serious challenge for affinity ranking and hence prioritizing the resultant large number of putative interactors for follow-up analyses. To overcome this apparent bottleneck, we describe here a novel yeast growth curve-based interaction-monitoring approach that permits semiautomatic quantification, comparison, and statistically ascertained scoring of a large collection of Y2H interactions under real-time conditions. Initially, we conducted a proof-of-concept test of five literature-validated peptide-protein interactions with known affinities in the low μM range, and subsequently used the method to classify 88 novel vitamin D receptor-binding peptides derived from high-throughput screening of a highly diverse artificial peptide aptamer library. Based on our in-depth data evaluation, we conclude that real-time monitoring of clone growth as a measure of relative binding strength offers a facile, cost-effective, accurate, reproducible, and further adaptable complement to standard Y2H-derived clone management.

Plant protein interactions studied using AFM force spectroscopy: nanomechanical and adhesion properties

The present work was focused on the nanomechanical and adhesion properties of the napin (2S albumin) and cruciferin (12S globulin) rapeseed (Brassica napus L.) proteins, respectively, a low and high molecular weight seed protein. Using chemically modified AFM tips, force spectroscopy experiments demonstrated notable differences in the tip-protein interaction strength with regard to the nature of the protein and pH of the aqueous environment. The results clearly underline the role of residence time and electrostatic interactions in the protein-protein adhesion force. Although the nanomechanical experiments concerned more than a single molecule, unfolding length and force characteristics of the rapeseed proteins have been statistically found to be sensitive to the structural properties of the protein. This study provides insight into the characterization of rapeseed proteins and then a better knowledge of their interaction and assembling at the nanoscale range.

Kinetically Controlled Nanostructure Formation in Self-Assembled Globular Protein–Polymer Diblock Copolymers

Aqueous processing of globular protein-polymer diblock copolymers into solid-state materials and subsequent solvent annealing enables kinetic and thermodynamic control of nanostructure formation to produce block copolymer morphologies that maintain a high degree of protein fold and function. When model diblock copolymers composed of mCherry-b-poly(N-isopropylacrylamide) are used, orthogonal control over solubility of the protein block through changes in pH and the polymer block through changes in temperature is demonstrated during casting and solvent annealing. Hexagonal cylinders, perforated lamellae, lamellae, or hexagonal and disordered micellar phases are observed, depending on the coil fraction of the block copolymer and the kinetic pathway used for self-assembly. Good solvents for the polymer block produce ordered structures reminiscent of coil-coil diblock copolymers, while an unfavorable solvent results in kinetically trapped micellar structures. Decreasing solvent quality for the protein improves long-range ordering, suggesting that the strength of protein interactions influences nanostructure formation. Subsequent solvent annealing results in evolution of the nanostructures, with the best ordering and the highest protein function observed when annealing in a good solvent for both blocks. While protein secondary structure was found to be almost entirely preserved for all processing pathways, UV-vis spectroscopy of solid-state films indicates that using a good solvent for the protein block enables up to 70% of the protein to be retained in its functional form.

One-step synthesis of antibody-stabilized aqueous colloids of noble metal nanoparticles

A one-step synthetic method has been successfully applied to prepare IgG-capped aqueous colloids of noble metal (Au, Ag and Pt) nanoparticles (NPs), in which IgG is used as the solo stabilizing regent. The produced NPs were characterized by UV–vis spectroscopy, transmission electron microscopy (TEM) and dynamic light scattering (DLS) measurements. Both Au and Ag NPs prepared from this message show relatively narrow size distribution and form stable suspension in aqueous solution. The IgG-capped Au NPs exhibit much higher stability against high salt concentration than that prepared by conventional ligand-exchange method. The IgG molecules on the Au and Ag NPs can retain their bioactivity, which enables the NPs to bind with specific antigen. The IgG-capped Pt NPs were found to be less stable than the Au and Ag NPs, revealing different metal–protein interaction strength.

Extracting Protein Interaction Information from FRET Data: A Study with Bayesian Inference and Simulations

Advances in fluorescence microscopy have made it possible to collect increasingly sophisticated experimental data, but equally important is developing analysis techniques to extract information from this data. One experimental tool with potential to yield further useful quantitative information is Forster resonance energy transfer (FRET), which is frequently used to probe interactions between proteins. We hypothesized that applying Bayesian inference to analyze FRET data would allow us to create a flexible method, capable of estimating both the dissociation constant (Kd) and the FRET efficiency (Efr) by producing the distribution of values that is statistically consistent with a given dataset. The goal of this work is to develop such a method.We designed an algorithm that uses a Markov Chain Monte Carlo method to explore possible values of Kd, Efr and other parameters, seeking those that make experimental observations be consistent with predictions from a mathematical model of FRET. The algorithm can incorporate uncertainty in experimental measurements and calibration, allowing us to systematically explore how measurement errors, numbers of repeated measurements and fluorophore concentrations impact the quality of the estimate. It also has the flexibility to incorporate additional knowledge about the system that may improve the estimate, such as fluorescence lifetime data.Applying our method to data from computer simulations, we demonstrate how variations in concentrations of fluorophores and the quality and quantity of the data determine how much can be inferred about the underlying parameters of interest. Overall, our work has applications for extracting additional quantitative information from FRET data and for optimally designing FRET experiments to measure protein interaction strength.