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.

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 Compendium of Methods to Uncover RNA-Protein Interactions In Vivo

2021 ◽  
Vol 4 (1) ◽  
pp. 22
Author(s):  
Mrinmoyee Majumder ◽  
Viswanathan Palanisamy
Keyword(s):  
Gene Expression ◽  
Protein Interactions ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Expression Patterns ◽  
Control Of Gene Expression ◽  
Regulatory Pathways ◽  
Advantages And Disadvantages ◽  
Protein Nucleic Acid

Control of gene expression is critical in shaping the pro-and eukaryotic organisms’ genotype and phenotype. The gene expression regulatory pathways solely rely on protein–protein and protein–nucleic acid interactions, which determine the fate of the nucleic acids. RNA–protein interactions play a significant role in co- and post-transcriptional regulation to control gene expression. RNA-binding proteins (RBPs) are a diverse group of macromolecules that bind to RNA and play an essential role in RNA biology by regulating pre-mRNA processing, maturation, nuclear transport, stability, and translation. Hence, the studies aimed at investigating RNA–protein interactions are essential to advance our knowledge in gene expression patterns associated with health and disease. Here we discuss the long-established and current technologies that are widely used to study RNA–protein interactions in vivo. We also present the advantages and disadvantages of each method discussed in the review.

scholarly journals Robust and accurate prediction of protein–protein interactions by exploiting evolutionary information

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yang Li ◽  
Zheng Wang ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Wen-Zhun Huang ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Protein Sequence ◽  
Large Scale ◽  
False Positive Rate ◽  
Computational Method ◽  
Evolutionary Information ◽  
Local Alignment ◽  
Protein Interaction Data ◽  
Sequence Information ◽  
Protein Protein Interactions

AbstractVarious biochemical functions of organisms are performed by protein–protein interactions (PPIs). Therefore, recognition of protein–protein interactions is very important for understanding most life activities, such as DNA replication and transcription, protein synthesis and secretion, signal transduction and metabolism. Although high-throughput technology makes it possible to generate large-scale PPIs data, it requires expensive cost of both time and labor, and leave a risk of high false positive rate. In order to formulate a more ingenious solution, biology community is looking for computational methods to quickly and efficiently discover massive protein interaction data. In this paper, we propose a computational method for predicting PPIs based on a fresh idea of combining orthogonal locality preserving projections (OLPP) and rotation forest (RoF) models, using protein sequence information. Specifically, the protein sequence is first converted into position-specific scoring matrices (PSSMs) containing protein evolutionary information by using the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST). Then we characterize a protein as a fixed length feature vector by applying OLPP to PSSMs. Finally, we train an RoF classifier for the purpose of identifying non-interacting and interacting protein pairs. The proposed method yielded a significantly better results than existing methods, with 90.07% and 96.09% prediction accuracy on Yeast and Human datasets. Our experiment show the proposed method can serve as a useful tool to accelerate the process of solving key problems in proteomics.

scholarly journals Targeting RNA Binding Proteins Involved in Neurodegeneration

2013 ◽  
Vol 18 (9) ◽  
pp. 967-983 ◽  
Author(s):  
Maurizio Romano ◽  
Emanuele Buratti
Keyword(s):  
Protein Interactions ◽  
Rna Processing ◽  
Noncoding Rna ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Correct Identification ◽  
Protein Protein Interactions ◽  
Aggregation Processes ◽  
Cellular Pathways ◽  
Screening Approaches

Dysfunctions at the level of RNA processing have recently been shown to play a fundamental role in the pathogenesis of many neurodegenerative diseases. Several proteins responsible for these dysfunctions (TDP-43, FUS/TLS, and hnRNP A/Bs) belong to the nuclear class of heterogeneous ribonucleoproteins (hnRNPs) that predominantly function as general regulators of both coding and noncoding RNA metabolism. The discovery of the importance of these factors in mediating neuronal death has represented a major paradigmatic shift in our understanding of neurodegenerative processes. As a result, these discoveries have also opened the way toward novel biomolecular screening approaches in our search for therapeutic options. One of the major hurdles in this search is represented by the correct identification of the most promising targets to be prioritized. These may include aberrant aggregation processes, protein-protein interactions, RNA-protein interactions, or specific cellular pathways altered by disease. In this review, we discuss these four major options together with their various advantages and drawbacks.

scholarly journals Recent advances in large-scale protein interactome mapping

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 782 ◽  
Author(s):  
Virja Mehta ◽  
Laura Trinkle-Mulcahy
Keyword(s):  
Protein Interactions ◽  
Large Scale ◽  
Protein Complexes ◽  
Specific Protein ◽  
Quality Data ◽  
Protein Protein Interactions ◽  
Cellular Functions ◽  
Biological Studies ◽  
Protein Interactome ◽  
Literature Curation

Protein-protein interactions (PPIs) underlie most, if not all, cellular functions. The comprehensive mapping of these complex networks of stable and transient associations thus remains a key goal, both for systems biology-based initiatives (where it can be combined with other ‘omics’ data to gain a better understanding of functional pathways and networks) and for focused biological studies. Despite the significant challenges of such an undertaking, major strides have been made over the past few years. They include improvements in the computation prediction of PPIs and the literature curation of low-throughput studies of specific protein complexes, but also an increase in the deposition of high-quality data from non-biased high-throughput experimental PPI mapping strategies into publicly available databases.

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 rec-Y2H matrix screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins

2020 ◽  
Author(s):  
Benjamin Lang ◽  
Jae-Seong Yang ◽  
Mireia Garriga-Canut ◽  
Silvia Speroni ◽  
Maria Gili ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Interaction Networks ◽  
Sequence Motifs ◽  
Protein Protein Interactions ◽  
Binding Interaction ◽  
Transcriptional Gene Regulation

AbstractRNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H screening to detect direct interactions between RBPs, sampling > 600 K interactions. We discovered 1994 new interactions and demonstrate that interacting RBPs bind RNAs adjacently in vivo. We further find that the mRNA binding region and motif preferences of RBPs can deviate, depending on their adjacently binding interaction partners. Finally, we reveal novel RBP interaction networks among major RNA processing steps and show that splicing impairing RBP mutations observed in cancer rewire spliceosomal interaction networks.Graphical abstract

scholarly journals Interactions between the double-stranded RNA-binding proteins TRBP and PACT define the Medipal domain that mediates protein-protein interactions

RNA Biology ◽  
2008 ◽  
Vol 5 (2) ◽  
pp. 92-103 ◽  
Author(s):  
Ghislaine Laraki ◽  
Guerline Clerzius ◽  
Aïcha Daher ◽  
Carlos Melendez-Peña ◽  
Sylvanne Daniels ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Binding Proteins ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Protein Protein Interactions ◽  
Double Stranded Rna

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 In-cell identification and measurement of RNA-protein interactions

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Antoine Graindorge ◽  
Inês Pinheiro ◽  
Anna Nawrocka ◽  
Allison C. Mallory ◽  
Peter Tsvetkov ◽  
...  
Keyword(s):  
Protein Interactions ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Cell Protein ◽  
Cellular Functions ◽  
Functional Regions ◽  
Rna Purification ◽  
High Throughput Method ◽  
Regulatory Rnas ◽  
Rna Interaction

AbstractRegulatory RNAs exert their cellular functions through RNA-binding proteins (RBPs). Identifying RNA-protein interactions is therefore key for a molecular understanding of regulatory RNAs. To date, RNA-bound proteins have been identified primarily through RNA purification followed by mass spectrometry. Here, we develop incPRINT (in cell protein-RNA interaction), a high-throughput method to identify in-cell RNA-protein interactions revealed by quantifiable luminescence. Applying incPRINT to long noncoding RNAs (lncRNAs), we identify RBPs specifically interacting with the lncRNA Firre and three functionally distinct regions of the lncRNA Xist. incPRINT confirms previously known lncRNA-protein interactions and identifies additional interactions that had evaded detection with other approaches. Importantly, the majority of the incPRINT-defined interactions are specific to individual functional regions of the large Xist transcript. Thus, we present an RNA-centric method that enables reliable identification of RNA-region-specific RBPs and is applicable to any RNA of interest.

Sign in / Sign up

Export Citation Format

Share Document