Dynamic Protein-protein Interactions Research Articles

FAHFA promotes intracellular calcium signaling via activating the fat taste receptor, CD36 and Src protein kinases in mice taste bud cells

Two lipid sensors, CD36 and GPR120, are crucial for the orosensory detection of fat taste and for mediating fat preference. However, the mechanism by which endogenous lipid (FAHFA) binds to CD36 to initiate intracellular signaling remains unexplained. Hence, the primary objective of this study is to investigate the binding mechanism of FAHFA to CD36 and its role in isolated mouse taste bud cells (mTBCs). The Schrodinger platform was used to assess the molecular dynamics of protein and ligand interactions, and an in vitro experiment was used to validate the findings. Based on the docking score of the ligand, the molecular mechanistic activities of the targeted complexes, CD36–5-POHSA (−8.2 kcal/mol), were investigated using the dynamic simulation. In comparison to linoleic acid (LA), POHSA rapidly increased [Ca2+]i via acting on CD36, and 5-POHSA treatment in mTBCs activated src-kinase at 20 μM. CD36 siRNA transfection in TBCs downregulate the CD36 protein expression as well as [Ca2+]i flux. This study suggests that 5-POHSA may help combat taste abnormalities and the adverse effects of obesity by binding to the lingual CD36 receptor and activating the tongue-brain axis.

µMap proximity labeling in living cells reveals stress granule disassembly mechanisms.

Phase-separated condensates are membrane-less intracellular structures comprising dynamic protein interactions that organize essential biological processes. Understanding the composition and dynamics of these organelles advances our knowledge of cellular behaviors and disease pathologies related to granule dysregulation. In this study, we apply microenvironment mapping with a HaloTag-based platform (HaloMap) to characterize intracellular stress granule dynamics in living cells. After validating the robustness and sensitivity of this approach, we then profile the stress granule proteome throughout the formation and disassembly and under pharmacological perturbation. These experiments reveal several ubiquitin-related modulators, including the HECT (homologous to E6AP C terminus) E3 ligases ITCH and NEDD4L, as well as the ubiquitin receptor toll-interacting protein TOLLIP, as key mediators of granule disassembly. In addition, we identify an autophagy-related pathway that promotes granule clearance. Collectively, this work establishes a general photoproximity labeling approach for unraveling intracellular protein interactomes and uncovers previously unexplored regulatory mechanisms of stress granule dynamics.

Detection of TurboID fusion proteins by fluorescent streptavidin outcompetes antibody signals and visualises targets not accessible to antibodies.

Immunofluorescence localises proteins via fluorophore-labelled antibodies. However, some proteins evade detection due to antibody-accessibility issues or because they are naturally low abundant or antigen density is reduced by the imaging method. Here, we show that the fusion of the target protein to the biotin ligase TurboID and subsequent detection of biotinylation by fluorescent streptavidin offers an 'all in one' solution to these restrictions. For all proteins tested, the streptavidin signal was significantly stronger than an antibody signal, markedly improving the sensitivity of expansion microscopy and correlative light and electron microscopy. Importantly, proteins within phase-separated regions, such as the central channel of the nuclear pores, the nucleolus, or RNA granules, were readily detected with streptavidin, while most antibodies failed. When TurboID is used in tandem with an HA epitope tag, co-probing with streptavidin and anti-HA can map antibody-accessibility and we created such a map for the trypanosome nuclear pore. Lastly, we show that streptavidin imaging resolves dynamic, temporally, and spatially distinct sub-complexes and, in specific cases, reveals a history of dynamic protein interaction. In conclusion, streptavidin imaging has major advantages for the detection of lowly abundant or inaccessible proteins and in addition, provides information on protein interactions and biophysical environment.

Label free tracking to quantify nanoparticle diffusion through biological media

Nanotechnology is a rapidly evolving field and has been extensively studied in biological applications. An understanding of the factors that influence nanoparticle diffusion in biofluids can aid in the development of diverse technologies. The development of real-time, label-free tracking technologies would allow the expansion of current knowledge of the diffusion and activity of nanoparticles. Fluorescence-based microscopy is one of the most widespread tools to monitor and track nanoparticle dynamics; however, the influence of fluorescent tags on diffusion and biological activity is still unclear. In this study, we experimentally determined the diffusion coefficient of gold nanoparticles using a label-free, optical tracking technique and evaluated the influence of protein concentration, charge and diameter on nanoparticle diffusion through biological media. We dispersed positively- and negatively-charged nanoparticles with diameters varying from 10 to 100 nm in a common cell culture media with different concentrations of serum proteins. Our results show that dynamic protein interactions influence nanoparticle diffusion in the range of serum concentrations tested. Experimental regimes to obtain quantitative information on the factors that influence the dynamics of nanoparticles in biological media have been developed.

Time-resolved interactome profiling deconvolutes secretory protein quality control dynamics

Many cellular processes are governed by protein–protein interactions that require tight spatial and temporal regulation. Accordingly, it is necessary to understand the dynamics of these interactions to fully comprehend and elucidate cellular processes and pathological disease states. To map de novo protein–protein interactions with time resolution at an organelle-wide scale, we developed a quantitative mass spectrometry method, time-resolved interactome profiling (TRIP). We apply TRIP to elucidate aberrant protein interaction dynamics that lead to the protein misfolding disease congenital hypothyroidism. We deconvolute altered temporal interactions of the thyroid hormone precursor thyroglobulin with pathways implicated in hypothyroidism pathophysiology, such as Hsp70-/90-assisted folding, disulfide/redox processing, and N-glycosylation. Functional siRNA screening identified VCP and TEX264 as key protein degradation components whose inhibition selectively rescues mutant prohormone secretion. Ultimately, our results provide novel insight into the temporal coordination of protein homeostasis, and our TRIP method should find broad applications in investigating protein-folding diseases and cellular processes.

Dynamic protein-protein interactions of KCNQ1 and KCNE1 measured by EPR line shape analysis

KCNQ1, also known as Kv7.1, is a voltage gated potassium channel that associates with the KCNE protein family. Mutations in this protein has been found to cause a variety of diseases including Long QT syndrome, a type of cardiac arrhythmia where the QT interval observed on an electrocardiogram is longer than normal. This condition is often aggravated during strenuous exercise and can cause fainting spells or sudden death. KCNE1 is an ancillary protein that interacts with KCNQ1 in the membrane at varying molar ratios. This interaction allows for the flow of potassium ions to be modulated to facilitate repolarization of the heart. The interaction between these two proteins has been studied previously with cysteine crosslinking and electrophysiology. In this study, electron paramagnetic resonance (EPR) spectroscopy line shape analysis in tandem with site directed spin labeling (SDSL) was used to observe changes in side chain dynamics as KCNE1 interacts with KCNQ1. KCNE1 was labeled at different sites that were found to interact with KCNQ1 based on previous literature, along with sites outside of that range as a control. Once labeled KCNE1 was incorporated into vesicles, KCNQ1 (helices S1-S6) was titrated into the vesicles. The line shape differences observed upon addition of KCNQ1 are indicative of an interaction between the two proteins. This method provides a first look at the interactions between KCNE1 and KCNQ1 from a dynamics perspective using the full transmembrane portion of KCNQ1.

Reconstruction of dynamic protein-protein interaction network via graph convolutional network

Traditional studies are mostly based on static protein network models. The highly averaged and idealized static protein network does not represent well this dynamic nature that encompasses different time, place and reaction conditions. Therefore, we propose PreDPPI, a method for convolving dynamic protein-protein interaction networks (PINs) using knowledge graph reconstruction of relational graphs, which achieves better performance in terms of dynamics compared to other methods. We found that the longer the length of timestamps used in reconstructing dynamic PINs, the better the prediction. In addition, we identified three hub genes (NUSAP1, SCG3, and CKAP2L) in dynamic PINs that were shown to be significantly associated with glioma prognosis. In conclusion, this work reconstructed a web-based knowledge graph convolving dynamic PINs and identified three hub genes in the GEO glioma dataset that were confirmed to be highly associated with glioma prognosis.

Benchmarking and Automating the Biotinylation Proteomics Workflow.

Protein biotinylation has been widely used in biotechnology with various labeling and enrichment strategies. However, different enrichment strategies have not been systematically evaluated due to the lack of a benchmarking model for fair comparison. Most biotinylation proteomics workflows suffer from lengthy experimental steps, non-specific bindings, limited throughput, and experimental variability. To address these challenges, we designed a two-proteome model, where biotinylated yeast proteins were spiked in unlabeled human proteins, allowing us to distinguish true enrichment from non-specific bindings. Using this benchmarking model, we compared common biotinylation proteomics methods and provided practical selection guidelines. We significantly optimized and shortened sample preparation from 3 days to 9 hours, enabling fully-automated 96-well plate sample processing. Next, we applied this optimized and automated workflow for proximity labeling to investigate the intricate interplay between mitochondria and lysosomes in living cells under both healthy state and mitochondrial damage. Our results suggested a time-dependent proteome remodeling and dynamic translocation within mitochondria and between mitochondria and lysosomes upon mitochondrial damage. This newly established benchmarking model and the fully-automated 9-hour workflow can be readily applied to the broad fields of protein biotinylation to study protein interaction and organelle dynamics.

Interrogation of RNA-protein interaction dynamics in bacterial growth

Characterising RNA–protein interaction dynamics is fundamental to understand how bacteria respond to their environment. In this study, we have analysed the dynamics of 91% of the Escherichia coli expressed proteome and the RNA-interaction properties of 271 RNA-binding proteins (RBPs) at different growth phases. We find that 68% of RBPs differentially bind RNA across growth phases and characterise 17 previously unannotated proteins as bacterial RBPs including YfiF, a ncRNA-binding protein. While these new RBPs are mostly present in Proteobacteria, two of them are orthologs of human mitochondrial proteins associated with rare metabolic disorders. Moreover, we reveal novel RBP functions for proteins such as the chaperone HtpG, a new stationary phase tRNA-binding protein. For the first time, the dynamics of the bacterial RBPome have been interrogated, showcasing how this approach can reveal the function of uncharacterised proteins and identify critical RNA–protein interactions for cell growth which could inform new antimicrobial therapies.

Disrupted protein interaction dynamics in a genetic neurodevelopmental disorder revealed by structural bioinformatics and genetic code expansion.

Deciphering the structural effects of gene variants is essential for understanding the pathophysiological mechanisms of genetic diseases. Using a neurodevelopmental disorder called Bosch-Boonstra-Schaaf Optic Atrophy Syndrome (BBSOAS) as a genetic disease model, we applied structural bioinformatics and Genetic Code Expansion (GCE) strategies to assess the pathogenic impact of human NR2F1 variants and their binding with known and novel partners. While the computational analyses of the NR2F1 structure delineated the molecular basis of the impact of several variants on the isolated and complexed structures, the GCE enabled covalent and site-specific capture of transient supramolecular interactions in living cells. This revealed the variable quaternary conformations of NR2F1 variants and highlighted the disrupted interplay with dimeric partners and the newly identified co-factor, CRABP2. The disclosed consequence of the pathogenic mutations on the conformation, supramolecular interplay, and alterations in the cell cycle, viability, and sub-cellular localization of the different variants reflect the heterogeneous disease spectrum of BBSOAS and set up novel foundation for unveiling the complexity of neurodevelopmental diseases.

In Vivo Detection of Metabolic Fluctuations in Real Time Using the NanoBiT Technology Based on PII Signalling Protein Interactions.

New protein-fragment complementation assays (PCA) have successfully been developed to characterize protein-protein interactions in vitro and in vivo. Notably, the NanoBiT technology, employing fragment complementation of NanoLuc luciferase, stands out for its high sensitivity, wide dynamic range, and straightforward read out. Previously, we explored the in vitro protein interaction dynamics of the PII signalling protein using NanoBiT, revealing significant modulation of luminescence signals generated by the interaction between PII and its receptor protein NAGK by 2-oxoglutarate levels. In the current work, we investigated this technology in vivo, to find out whether recombinantly expressed NanoBiT constructs using the NanoLuc large fragment fused to PII and PII-interaction partners NAGK or PipX-fused to the NanoLuc Small BiT are capable of detecting the metabolic fluctuations in Escherichia coli. Therefore, we devised an assay capable of capturing the metabolic responses of E. coli cells, demonstrating real-time metabolic perturbation upon nitrogen upshift or depletion treatments. In particular, the PII-NAGK NanoBitT sensor pair reported these changes in a highly sensitive manner.

A transient in planta editing assay identifies specific binding of the splicing regulator PTB as a prerequisite for cassette exon inclusion

The dynamic interaction of RNA-binding proteins (RBPs) with their target RNAs contributes to the diversity of ribonucleoprotein (RNP) complexes that are involved in a myriad of biological processes. Identifying the RNP components at high resolution and defining their interactions are key to understanding their regulation and function. Expressing fusions between an RBP of interest and an RNA editing enzyme can result in nucleobase changes in target RNAs, representing a recent addition to experimental approaches for profiling RBP/RNA interactions. Here, we have used the MS2 protein/RNA interaction to test four RNA editing proteins for their suitability to detect target RNAs of RBPs in planta. We have established a transient test system for fast and simple quantification of editing events and identified the hyperactive version of the catalytic domain of an adenosine deaminase (hADARcd) as the most suitable editing enzyme. Examining fusions between homologs of polypyrimidine tract binding proteins (PTBs) from Arabidopsis thaliana and hADARcd allowed determining target RNAs with high sensitivity and specificity. Moreover, almost complete editing of a splicing intermediate provided insight into the order of splicing reactions and PTB dependency of this particular splicing event. Addition of sequences for nuclear localisation of the fusion protein increased the editing efficiency, highlighting this approach’s potential to identify RBP targets in a compartment-specific manner. Our studies have established the editing-based analysis of interactions between RBPs and their RNA targets in a fast and straightforward assay, offering a new system to study the intricate composition and functions of plant RNPs in vivo.

SRC family kinase inhibition rescues molecular and behavioral phenotypes, but not protein interaction network dynamics, in a mouse model of Fragile X syndrome.

Glutamatergic synapses encode information from extracellular inputs using dynamic protein interaction networks (PINs) that undergo widespread reorganization following synaptic activity, allowing cells to distinguish between signaling inputs and generate coordinated cellular responses. Here, we investigate how Fragile X Messenger Ribonucleoprotein (FMRP) deficiency disrupts signal transduction through a glutamatergic synapse PIN downstream of NMDA receptor or metabotropic glutamate receptor (mGluR) stimulation. In cultured cortical neurons or acute cortical slices from P7, P17 and P60 FMR1-/y mice, the unstimulated protein interaction network state resembled that of wildtype littermates stimulated with mGluR agonists, demonstrating resting state pre-activation of mGluR signaling networks. In contrast, interactions downstream of NMDAR stimulation were similar to WT. We identified the Src family kinase (SFK) Fyn as a network hub, because many interactions involving Fyn were pre-activated in FMR1-/y animals. We tested whether targeting SFKs in FMR1-/y mice could modify disease phenotypes, and found that Saracatinib (SCB), an SFK inhibitor, normalized elevated basal protein synthesis, novel object recognition memory and social behavior in FMR1-/y mice. However, SCB treatment did not normalize the PIN to a wild-type-like state in vitro or in vivo, but rather induced extensive changes to protein complexes containing Shank3, NMDARs and Fyn. We conclude that targeting abnormal nodes of a PIN can identify potential disease-modifying drugs, but behavioral rescue does not correlate with PIN normalization.

Deep learning based CETSA feature prediction cross multiple cell lines with latent space representation

Mass spectrometry-coupled cellular thermal shift assay (MS-CETSA), a biophysical principle-based technique that measures the thermal stability of proteins at the proteome level inside the cell, has contributed significantly to the understanding of drug mechanisms of action and the dissection of protein interaction dynamics in different cellular states. One of the barriers to the wide applications of MS-CETSA is that MS-CETSA experiments must be performed on the specific cell lines of interest, which is typically time-consuming and costly in terms of labeling reagents and mass spectrometry time. In this study, we aim to predict CETSA features in various cell lines by introducing a computational framework called CycleDNN based on deep neural network technology. For a given set of n cell lines, CycleDNN comprises n auto-encoders. Each auto-encoder includes an encoder to convert CETSA features from one cell line into latent features in a latent space Z\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathbb {Z}$$\\end{document}. It also features a decoder that transforms the latent features back into CETSA features for another cell line. In such a way, the proposed CycleDNN creates a cyclic prediction of CETSA features across different cell lines. The prediction loss, cycle-consistency loss, and latent space regularization loss are used to guide the model training. Experimental results on a public CETSA dataset demonstrate the effectiveness of our proposed approach. Furthermore, we confirm the validity of the predicted MS-CETSA data from our proposed CycleDNN through validation in protein–protein interaction prediction.

Single-Molecule Measurement of Protein Interaction Dynamics within Biomolecular Condensates.

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) have been considered critical in cellular organization and an increasing number of cellular functions. Characterizing LLPS in live cells is also important because aberrant condensation has been linked to numerous diseases, including cancers and neurodegenerative disorders. LLPS is often driven by selective, transient, and multivalent interactions between intrinsically disordered proteins. Of great interest are the interaction dynamics of proteins participating in LLPS, which are well-summarized by measurements of their binding residence time (RT), that is, the amount of time they spend bound within condensates. Here, we present a method based on live-cell single-molecule imaging that allows us to measure the mean RT of a specific protein within condensates. We simultaneously visualize individual protein molecules and the condensates with which they associate, use single-particle tracking (SPT) to plot single-molecule trajectories, and then fit the trajectories to a model of protein-droplet binding to extract the mean RT of the protein. Finally, we show representative results where this single-molecule imaging method was applied to compare the mean RTs of a protein at its LLPS condensates when fused and unfused to an oligomerizing domain. This protocol is broadly applicable to measuring the interaction dynamics of any protein that participates in LLPS.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses.

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

Mammalian circadian clock proteins form dynamic interacting microbodies distinct from phase separation.

Liquid-liquid phase separation (LLPS) underlies diverse biological processes. Because most LLPS studies were performed in vitro using recombinant proteins or in cells that overexpress protein, the physiological relevance of LLPS for endogenous protein is often unclear. PERIOD, the intrinsically disordered domain-rich proteins, are central mammalian circadian clock components and interact with other clock proteins in the core circadian negative feedback loop. Different core clock proteins were previously shown to form large complexes. Circadian clock studies often rely on experiments that overexpress clock proteins. Here, we show that when Per2 transgene was stably expressed in cells, PER2 protein formed nuclear phosphorylation-dependent slow-moving LLPS condensates that recruited other clock proteins. Super-resolution microscopy of endogenous PER2, however, revealed formation of circadian-controlled, rapidly diffusing nuclear microbodies that were resistant to protein concentration changes, hexanediol treatment, and loss of phosphorylation, indicating that they are distinct from the LLPS condensates caused by protein overexpression. Surprisingly, only a small fraction of endogenous PER2 microbodies transiently interact with endogenous BMAL1 and CRY1, a conclusion that was confirmed in cells and in mice tissues, suggesting an enzyme-like mechanism in the circadian negative feedback process. Together, these results demonstrate that the dynamic interactions of core clock proteins are a key feature of mammalian circadian clock mechanism and the importance of examining endogenous proteins in LLPS and circadian clock studies.

Interaction networks of Escherichia coli replication proteins under different bacterial growth conditions

In this work we analyzed protein-protein interactions (PPIs) formed by E. coli replication proteins under three disparate bacterial growth conditions. The chosen conditions corresponded to fast exponential growth, slow exponential growth and growth cessation at the stationary phase. We performed affinity purification coupled with mass spectrometry (AP-MS) of chromosomally expressed proteins (DnaA, DnaB, Hda, SeqA, DiaA, DnaG, HolD, NrdB), tagged with sequential peptide affinity (SPA) tag. Composition of protein complexes was characterized using MaxQuant software. To filter out unspecific interactions, we employed double negative control system and we proposed qualitative and quantitative data analysis strategies that can facilitate hits identification in other AP-MS datasets. Our motivation to undertake this task was still insufficient understanding of molecular mechanisms coupling DNA replication to cellular growth. Previous works suggested that such control mechanisms could involve physical interactions of replication factors with metabolic or cell envelope proteins. However, the dynamic replication protein interaction network (PIN) obtained in this study can be used to characterize links between DNA replication and various cellular processes in other contexts.

Synthesis, spectroscopic analysis, DFT, docking, MD and antioxidant activity of tetrahydrocurcumin

In recent years, numerous researchers have made local chemical modifications to the structure of curcumin while its basic structure remains unchanged, thus, producing curcumin derivatives. In this article, tetrahydrocurcumin was obtained by hydrogenation of curcumin, DFT calculation and characterization at the theoretical level of B3LYP/6 -311++G(d,p) were carried out. The observed IR and Raman spectra are in good agreement with the theoretical spectra. The FMO and ESP of tetrahydrocurcumin are predicted. The interaction in the system is shown graphically and analyzed by IGMH. Compared with curcumin, tetrahydrocurcumin lacks the unsaturated C = C bond, which makes it more stable and more bioavailable. Molecular docking with antioxidant targets elucidated the ligand–protein interaction and molecular dynamics simulation showed the antioxidant activity of tetrahydrocurcumin. The antioxidant activity of tetrahydrocurcumin was proved by DPPH• and •OH radical scavenging experiments. In essence, these derivatives exhibit enhanced physiological activity in certain aspects compared to the original curcumin. Moreover, the computational pharmacology techniques lay a theoretical groundwork for the development and modification of high-efficiency, low-toxicity drugs that interface with various targets of curcumin in the future. Communicated by Ramaswamy H. Sarma

Experimental traumatic brain injury increases epichaperome formation

Under normal conditions, heat shock proteins work in unison through dynamic protein interactions collectively referred to as the “chaperome.” Recent work revealed that during cellular stress, the functional interactions of the chaperome are modified to form the “epichaperome,” which results in improper protein folding, degradation, aggregation, and transport. This study is the first to investigate this novel mechanism of protein dishomeostasis in traumatic brain injury (TBI). Male and female adult, Sprague-Dawley rats received a lateral controlled cortical impact (CCI) and the ipsilateral hippocampus was collected 24 h 1, 2, and 4 weeks after injury. The epichaperome complex was visualized by measuring HSP90, HSC70 and HOP expression in native-PAGE and normalized to monomeric protein expression. A two-way ANOVA examined the effect of injury and sex at each time-point. Native HSP90, HSC70 and HOP protein expression showed a significant effect of injury effect across all time-points. Additionally, HSC70 and HOP showed significant sex effects at 24 h and 4 weeks. Altogether, controlled cortical impact significantly increased formation of the epichaperome across all proteins measured. Further investigation of this pathological mechanism can lead to a greater understanding of the link between TBI and increased risk of neurodegenerative disease and targeting the epichaperome for therapeutics.