scholarly journals An RNA tagging approach for system-wide RNA-binding proteome profiling and dynamics investigation upon transcription inhibition

2021 ◽  
Author(s):  
Zheng Zhang ◽  
Tong Liu ◽  
Hangyan Dong ◽  
Jian Li ◽  
Haofan Sun ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Decay Rates ◽  
Mass Spectrometry Analysis ◽  
Transcription Inhibition ◽  
Distribution Profile ◽  
Proteome Profiling ◽  
Spectrometry Analysis

Abstract RNA-protein interactions play key roles in epigenetic, transcriptional and posttranscriptional regulation. To reveal the regulatory mechanisms of these interactions, global investigation of RNA-binding proteins (RBPs) and monitor their changes under various physiological conditions are needed. Herein, we developed a psoralen probe (PP)-based method for RNA tagging and ribonucleic-protein complex (RNP) enrichment. Isolation of both coding and noncoding RNAs and mapping of 2986 RBPs including 782 unknown candidate RBPs from HeLa cells was achieved by PP enrichment, RNA-sequencing and mass spectrometry analysis. The dynamics study of RNPs by PP enrichment after the inhibition of RNA synthesis provides the first large-scale distribution profile of RBPs bound to RNAs with different decay rates. Furthermore, the remarkably greater decreases in the abundance of the RBPs obtained by PP-enrichment than by global proteome profiling suggest that PP enrichment after transcription inhibition offers a valuable way for large-scale evaluation of the candidate RBPs.

scholarly journals A New Drug Discovery Approach Based on Thermal Proteome Profiling to Develop More Effective Drugs

2021 ◽  
Vol 8 (2) ◽  
Author(s):  
Elham Gholizadeh ◽  
Mostafa Rezaei-Tavirani ◽  
Alireza Emadi ◽  
Reza Karbalaei ◽  
Ali Khaleghian
Keyword(s):  
Mass Spectrometry ◽  
Protein Interactions ◽  
Large Scale ◽  
Protein Extraction ◽  
Mass Spectrometry Analysis ◽  
Structural Stabilization ◽  
Proteome Profiling ◽  
Spectrometry Analysis ◽  
Drug Concentrations ◽  
Low Efficiency

: The search for disease-related targets and studying drug-protein and protein-protein interactions are central issues that would accelerate the clinical approval of a drug. Also, by developing an accurate method in this regard, time and resource consumption will significantly decrease. The low efficiency of some drugs in humans is a grave issue leading to a low rate of FDA approval after spending billions of dollars and decades of research. Several strategies and methods have been expanded to fill this gap, such as drug affinity responsive target stability (DARTS), stability of proteins from rates of oxidation (SPROX), cellular thermal shift assay (CETSA), and finally, thermal proteome profiling (TPP). The TPP is based on the combination of CETSA and quantitative mass spectrometry. Among recently introduced proteomics technologies, TPP demonstrates the ability to offer detailed proteomic profiles for the large-scale analysis of protein-ligand interactions, including endogenous ligands and proteins like cofactors and metabolites. TPP facilitates the identification of the markers governing drug efficacy and toxicity and provides an unbiased measure for estimating the rate of drug-target engagement. At a glance at TPP steps, after protein extraction, the molecule is exposed to different temperatures and drug concentrations. After discarding solubilized and stabilized proteins, the protein’s identity is investigated by mass spectrometry analysis. As a result of the protein’s structural stabilization after binding to its substrate, TTP helps to accurately identify target proteins with high throughput. In this study, we aimed to introduce the basics of this method and review most recent studies on this technique.

scholarly journals iSUMO - integrative prediction of functionally relevant SUMOylation events

2016 ◽  
Author(s):  
Xiaotong Yao ◽  
Shuvadeep Maity ◽  
Shashank Gandhi ◽  
Marcin Imielenski ◽  
Christine Vogel
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
False Positive Rate ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Positive Rate ◽  
Protein Nucleic Acid ◽  
Scale Experiment

AbstractPost-translational modifications by the Small Ubiquitin-like Modifier (SUMO) are essential for diverse cellular functions. Large-scale experiment and sequence-based predictions have identified thousands of SUMOylated proteins. However, the overlap between the datasets is small, suggesting many false positives with low functional relevance. Therefore, we integrated ~800 sequence features and protein characteristics such as cellular function and protein-protein interactions in a machine learning approach to score likely functional SUMOylation events (iSUMO). iSUMO is trained on a total of 24 large-scale datasets, and it predicts 2,291 and 706 SUMO targets in human and yeast, respectively. These estimates are five times higher than what existing sequence-based tools predict at the same 5% false positive rate. Protein-protein and protein-nucleic acid interactions are highly predictive of protein SUMOylation, supporting a role of the modification in protein complex formation. We note the marked prevalence of SUMOylation amongst RNA-binding proteins. We validate iSUMO predictions by experimental or other evidence. iSUMO therefore represents a comprehensive tool to identify high-confidence, functional SUMOylation events for human and yeast.

Prediction and collection of protein–metabolite interactions

2021 ◽  
Author(s):  
Tianyi Zhao ◽  
Jinxin Liu ◽  
Xi Zeng ◽  
Wei Wang ◽  
Sheng Li ◽  
...  
Keyword(s):  
Small Molecule ◽  
Protein Interactions ◽  
Large Scale ◽  
Rapid Development ◽  
Area Under The Curve ◽  
Membrane Receptors ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Cellular Processes ◽  
The Rich

Abstract Interactions between proteins and small molecule metabolites play vital roles in regulating protein functions and controlling various cellular processes. The activities of metabolic enzymes, transcription factors, transporters and membrane receptors can all be mediated through protein–metabolite interactions (PMIs). Compared with the rich knowledge of protein–protein interactions, little is known about PMIs. To the best of our knowledge, no existing database has been developed for collecting PMIs. The recent rapid development of large-scale mass spectrometry analysis of biomolecules has led to the discovery of large amounts of PMIs. Therefore, we developed the PMI-DB to provide a comprehensive and accurate resource of PMIs. A total of 49 785 entries were manually collected in the PMI-DB, corresponding to 23 small molecule metabolites, 9631 proteins and 4 species. Unlike other databases that only provide positive samples, the PMI-DB provides non-interaction between proteins and metabolites, which not only reduces the experimental cost for biological experimenters but also facilitates the construction of more accurate algorithms for researchers using machine learning. To show the convenience of the PMI-DB, we developed a deep learning-based method to predict PMIs in the PMI-DB and compared it with several methods. The experimental results show that the area under the curve and area under the precision-recall curve of our method are 0.88 and 0.95, respectively. Overall, the PMI-DB provides a user-friendly interface for browsing the biological functions of metabolites/proteins of interest, and experimental techniques for identifying PMIs in different species, which provides important support for furthering the understanding of cellular processes. The PMI-DB is freely accessible at http://easybioai.com/PMIDB.

scholarly journals De novo prediction of RNA-protein interactions with Graph Neural Networks

2021 ◽  
Author(s):  
Viplove Arora ◽  
Guido Sanguinetti
Keyword(s):  
Neural Networks ◽  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
De Novo ◽  
Rna Binding Proteins ◽  
Large Data ◽  
Data Sets ◽  
Graph Neural Networks ◽  
Post Transcriptional Regulation

RNA-binding proteins (RBPs) are key co- and post-transcriptional regulators of gene expression, playing a crucial role in many biological processes. Experimental methods like CLIP-seq have enabled the identification of transcriptome-wide RNA-protein interactions for select proteins, however the time and resource intensive nature of these technologies call for the development of computational methods to complement their predictions. Here we leverage recent, large-scale CLIP-seq experiments to construct a de novo predictor of RNA-protein interactions based on graph neural networks (GNN). We show that the GNN method allows not only to predict missing links in a RNA-protein network, but to predict the entire complement of targets of previously unassayed proteins, and even to reconstruct the entire network of RNA-protein interactions in different conditions based on minimal information. Our results demonstrate the potential of machine learning methods to extract useful information on post-transcriptional regulation from large data sets.

scholarly journals RACK1 associates with RNA-binding proteins Vigilin and SERBP1 to control dengue virus replication

2021 ◽  
Author(s):  
Alexis Brugier ◽  
Mohamed-Lamine Hafirassou ◽  
Marie Pourcelot ◽  
Morgane Baldaccini ◽  
Laurine Couture ◽  
...  
Keyword(s):  
Dengue Virus ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Ribosomal Subunit ◽  
Denv Infection ◽  
Mass Spectrometry Analysis ◽  
Macromolecular Complex ◽  
Loss Of Function ◽  
Spectrometry Analysis

Dengue virus (DENV), a re-emerging virus transmitted by Aedes mosquitoes, causes severe pathogenesis in humans. No effective treatment is available against this virus. We recently identified the scaffold protein RACK1 as a component of the DENV replication complex, a macromolecular complex essential for viral genome amplification. Here, we show that RACK1 is important for DENV infection. RACK1 mediates DENV replication through binding to the 40S ribosomal subunit. Mass spectrometry analysis of RACK1 partners coupled to a loss-of-function screen identified the RNA binding proteins Vigilin and SERBP1 as DENV host dependency factors. Vigilin and SERBP1 interact with DENV viral RNA (vRNA), forming a ternary complex with RACK1 to mediate viral replication. Overall, our results indicate that RACK1 recruits Vigilin and SERBP1, linking the DENV vRNA to the translation machinery for optimal translation and replication.

scholarly journals Revealing the Proteome of Motor Cortex Derived Extracellular Vesicles Isolated from Amyotrophic Lateral Sclerosis Human Postmortem Tissues

Cells ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1709
Author(s):  
Natasha Vassileff ◽  
Laura J. Vella ◽  
Harinda Rajapaksha ◽  
Mitch Shambrook ◽  
Amirmohammad Nasiri Kenari ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Motor Cortex ◽  
Extracellular Vesicles ◽  
Motor Neurons ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Mass Spectrometry Analysis ◽  
Spectrometry Analysis ◽  
Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by the deposition of misfolded proteins in the motor cortex and motor neurons. Although a multitude of ALS-associated mutated proteins have been identified, several have been linked to small extracellular vesicles such as exosomes involved in cell−cell communication. This study aims to determine the proteome of extracellular vesicles isolated from the motor cortex of ALS subjects and to identify novel ALS-associated deregulated proteins. Motor cortex extracellular vesicles (MCEVs) were isolated from human postmortem ALS (n = 10) and neurological control (NC, n = 5) motor cortex brain tissues and the MCEVs protein content subsequently underwent mass spectrometry analysis, allowing for a panel of ALS-associated proteins to be identified. This panel consists of 16 statistically significant differentially packaged proteins identified in the ALS MCEVs. This includes several upregulated RNA-binding proteins which were determined through pathway analysis to be associated with stress granule dynamics. The identification of these RNA-binding proteins in the ALS MCEVs suggests there may be a relationship between ALS-associated stress granules and ALS MCEV packaging, highlighting a potential role for small extracellular vesicles such as exosomes in the pathogenesis of ALS and as potential peripheral biomarkers for ALS.

Niclosamide’s potential direct targets in ovarian cancer

2021 ◽  
Author(s):  
Nikola Sekulovski ◽  
James A MacLean ◽  
Sambasiva R Bheemireddy ◽  
Zhifeng Yu ◽  
Hiroshi Okuda ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cell Viability ◽  
Cancer Cells ◽  
High Throughput Screening ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Affinity Purification ◽  
Mass Spectrometry Analysis ◽  
Ovarian Cancer Cells ◽  
Spectrometry Analysis

Abstract Recent evidence indicates that niclosamide is an anti-cancer compound that is able to inhibit several signaling pathways. While niclosamide has previously been identified by high-throughput screening platforms as a potential effective compound against several cancer types, no direct binding interactions with distinct biological molecule(s) has been established. The present study identifies key signal transduction mechanisms altered by niclosamide in ovarian cancer. Using affinity purification with a biotin-modified niclosamide derivative and mass spectrometry analysis, several RNA binding proteins were identified. We chose two, FXR1 and IGF2BP2, for further analysis. A significant correlation exists in which high-expression of FXR1 or IGF2BP2 is associated with reduced survival of ovarian cancer patients. Knockdown of FXR1 or IGF2BP2 in ovarian cancer cells resulted in significantly reduced cell viability, adhesion, and migration. Furthermore, FXR1 or IGF2BP2 deficient ovarian cancer cells exhibited reduced response to most doses of niclosamide showing greater cell viability than those with intact RBPs. These results suggest that FXR1 and IGF2BP2 are direct targets of niclosamide and could have critical activities that drive multiple oncogenic pathways in ovarian cancer.

scholarly journals Recent advances in proximity-based labeling methods for interactome mapping

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 135 ◽  
Author(s):  
Laura Trinkle-Mulcahy
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Complexes ◽  
Affinity Purification ◽  
Small Scale ◽  
Protein Protein Interactions ◽  
Large Scale Network ◽  
Complementary Approach

Proximity-based labeling has emerged as a powerful complementary approach to classic affinity purification of multiprotein complexes in the mapping of protein–protein interactions. Ongoing optimization of enzyme tags and delivery methods has improved both temporal and spatial resolution, and the technique has been successfully employed in numerous small-scale (single complex mapping) and large-scale (network mapping) initiatives. When paired with quantitative proteomic approaches, the ability of these assays to provide snapshots of stable and transient interactions over time greatly facilitates the mapping of dynamic interactomes. Furthermore, recent innovations have extended biotin-based proximity labeling techniques such as BioID and APEX beyond classic protein-centric assays (tag a protein to label neighboring proteins) to include RNA-centric (tag an RNA species to label RNA-binding proteins) and DNA-centric (tag a gene locus to label associated protein complexes) assays.

scholarly journals Identification of novel proteins and mRNAs differentially bound to the Leishmania Poly(A) Binding Proteins reveals a direct association between PABP1, the RNA-binding protein RBP23 and mRNAs encoding ribosomal proteins

2021 ◽  
Vol 15 (10) ◽  
pp. e0009899
Author(s):  
Ludmila A. Assis ◽  
Moezio V. C. Santos Filho ◽  
Joao R. da Cruz Silva ◽  
Maria J. R. Bezerra ◽  
Irassandra R. P. U. C. de Aquino ◽  
...  
Keyword(s):  
Ribosomal Proteins ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Mrna Translation ◽  
Mass Spectrometry Analysis ◽  
Sequencing Analysis ◽  
Direct Role ◽  
Spectrometry Analysis

Poly(A) Binding Proteins (PABPs) are major eukaryotic RNA-binding proteins (RBPs) with multiple roles associated with mRNA stability and translation and characterized mainly from multicellular organisms and yeasts. A variable number of PABP homologues are seen in different organisms however the biological reasons for multiple PABPs are generally not well understood. In the unicellular Leishmania, dependent on post-transcriptional mechanisms for the control of its gene expression, three distinct PABPs are found, with yet undefined functional distinctions. Here, using RNA-immunoprecipitation sequencing analysis we show that the Leishmania PABP1 preferentially associates with mRNAs encoding ribosomal proteins, while PABP2 and PABP3 bind to an overlapping set of mRNAs distinct to those enriched in PABP1. Immunoprecipitation studies combined to mass-spectrometry analysis identified RBPs differentially associated with PABP1 or PABP2, including RBP23 and DRBD2, respectively, that were investigated further. Both RBP23 and DRBD2 bind directly to the three PABPs in vitro, but reciprocal experiments confirmed preferential co-immunoprecipitation of PABP1, as well as the EIF4E4/EIF4G3 based translation initiation complex, with RBP23. Other RBP23 binding partners also imply a direct role in translation. DRBD2, in contrast, co-immunoprecipitated with PABP2, PABP3 and with RBPs unrelated to translation. Over 90% of the RBP23-bound mRNAs code for ribosomal proteins, mainly absent from the transcripts co-precipitated with DRBD2. These experiments suggest a novel and specific route for translation of the ribosomal protein mRNAs, mediated by RBP23, PABP1 and the associated EIF4E4/EIF4G3 complex. They also highlight the unique roles that different PABP homologues may have in eukaryotic cells associated with mRNA translation.

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