The functional impact of alternative splicing in cancer

SummaryAlternative splicing changes are frequently observed in cancer and are starting to be recognized as important signatures for tumor progression and therapy. However, their functional impact and relevance to tumorigenesis remains mostly unknown. We carried out a systematic analysis to characterize the potential functional consequences of alternative splicing changes in thousands of tumor samples. This analysis revealed that a subset of alternative splicing changes affect protein domain families that are frequently mutated in tumors and potentially disrupt protein protein interactions in cancer-related pathways. Moreover, there was a negative correlation between the number of these alternative splicing changes in a sample and the number of somatic mutations in drivers. We propose that a subset of the alternative splicing changes observed in tumors may represent independent oncogenic processes that could be relevant to explain the functional transformations in cancer and some of them could potentially be considered alternative splicing drivers (AS-drivers).

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Integrative Visual Analysis of the Effects of Alternative Splicing on Protein Domain Interaction Networks

Journal of Integrative Bioinformatics ◽

10.1515/jib-2008-101 ◽

2008 ◽

Vol 5 (2) ◽

Cited By ~ 1

Author(s):

Dorothea Emig ◽

Melissa S. Cline ◽

Karsten Klein ◽

Anne Kunert ◽

Petra Mutzel ◽

...

Keyword(s):

Alternative Splicing ◽

Protein Interactions ◽

Visual Analysis ◽

Protein Isoforms ◽

Interaction Networks ◽

Protein Domain ◽

Domain Interaction ◽

Exon Arrays ◽

Exon Expression ◽

Splicing Patterns

SummaryProteins and their interactions are essential for the functioning of all organisms and for understanding biological processes. Alternative splicing is an important molecular mechanism for increasing the protein diversity in eukaryotic cells. Splicing events that alter the protein structure and the domain composition can be responsible for the regulation of protein interactions and the functional diversity of different tissues. Discovering the occurrence of splicing events and studying protein isoforms have become feasible using Affymetrix Exon Arrays. Therefore, we have developed the versatile Cytoscape plugin DomainGraph that allows for the visual analysis of protein domain interaction networks and their integration with exon expression data. Protein domains affected by alternative splicing are highlighted and splicing patterns can be compared.

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Porcine Amelogenin: Alternative Splicing, Proteolytic Processing, Protein-Protein Interactions, and Possible Functions

Journal of Oral Biosciences ◽

10.1016/s1349-0079(11)80011-3 ◽

2011 ◽

Vol 53 (3) ◽

pp. 275-283 ◽

Cited By ~ 11

Author(s):

Yasuo Yamakoshi

Keyword(s):

Alternative Splicing ◽

Protein Interactions ◽

Proteolytic Processing ◽

Protein Protein Interactions

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Mining Protein Interactome Networks to Measure Interaction Reliability and Select Hub Proteins

Computational Knowledge Discovery for Bioinformatics Research ◽

10.4018/978-1-4666-1785-8.ch013 ◽

2013 ◽

pp. 222-238

Author(s):

Young-Rae Cho ◽

Aidong Zhang

Keyword(s):

Protein Interactions ◽

Large Scale ◽

Functional Characterization ◽

Flow Simulation ◽

Protein Protein Interactions ◽

Systematic Analysis ◽

Graph Theoretic ◽

Interactome Network ◽

Protein Interactome ◽

Hub Proteins

High-throughput techniques involve large-scale detection of protein-protein interactions. This interaction data set from the genome-scale perspective is structured into an interactome network. Since the interaction evidence represents functional linkage, various graph-theoretic computational approaches have been applied to the interactome networks for functional characterization. However, this data is generally unreliable, and the typical genome-wide interactome networks have a complex connectivity. In this paper, the authors explore systematic analysis of protein interactome networks, and propose a $k$-round signal flow simulation algorithm to measure interaction reliability from connection patterns of the interactome networks. This algorithm quantitatively characterizes functional links between proteins by simulating the propagation of information signals through complex connections. In this regard, the algorithm efficiently estimates the strength of alternative paths for each interaction. The authors also present an algorithm for mining the complex interactome network structure. The algorithm restructures the network by hierarchical ordering of nodes, and this structure re-formatting process reveals hub proteins in the interactome networks. This paper demonstrates that two rounds of simulation accurately scores interaction reliability in terms of ontological correlation and functional consistency. Finally, the authors validate that the selected structural hubs represent functional core proteins.

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Intrinsic Disorder in Tetratricopeptide Repeat Proteins

International Journal of Molecular Sciences ◽

10.3390/ijms21103709 ◽

2020 ◽

Vol 21 (10) ◽

pp. 3709 ◽

Cited By ~ 1

Author(s):

Nathan W. Van Bibber ◽

Cornelia Haerle ◽

Roy Khalife ◽

Bin Xue ◽

Vladimir N. Uversky

Keyword(s):

Protein Interactions ◽

Posttranslational Modifications ◽

Tandem Repeats ◽

Intrinsically Disordered Protein ◽

Intrinsic Disorder ◽

Protein Protein Interactions ◽

Systematic Analysis ◽

Tetratricopeptide Repeats ◽

Intrinsically Disordered ◽

Tetratricopeptide Repeat Proteins

Among the realm of repeat containing proteins that commonly serve as “scaffolds” promoting protein-protein interactions, there is a family of proteins containing between 2 and 20 tetratricopeptide repeats (TPRs), which are functional motifs consisting of 34 amino acids. The most distinguishing feature of TPR domains is their ability to stack continuously one upon the other, with these stacked repeats being able to affect interaction with binding partners either sequentially or in combination. It is known that many repeat-containing proteins are characterized by high levels of intrinsic disorder, and that many protein tandem repeats can be intrinsically disordered. Furthermore, it seems that TPR-containing proteins share many characteristics with hybrid proteins containing ordered domains and intrinsically disordered protein regions. However, there has not been a systematic analysis of the intrinsic disorder status of TPR proteins. To fill this gap, we analyzed 166 human TPR proteins to determine the degree to which proteins containing TPR motifs are affected by intrinsic disorder. Our analysis revealed that these proteins are characterized by different levels of intrinsic disorder and contain functional disordered regions that are utilized for protein-protein interactions and often serve as targets of various posttranslational modifications.

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The 14-3-3 Proteins as Important Allosteric Regulators of Protein Kinases

International Journal of Molecular Sciences ◽

10.3390/ijms21228824 ◽

2020 ◽

Vol 21 (22) ◽

pp. 8824

Author(s):

Veronika Obsilova ◽

Tomas Obsil

Keyword(s):

Protein Interactions ◽

Cell Cycle Progression ◽

State Of The Art ◽

Review Article ◽

Protein Protein Interactions ◽

Cycle Progression ◽

Kinase Regulation ◽

Functional Consequences ◽

Allosteric Regulators ◽

Structural Aspects

Phosphorylation by kinases governs many key cellular and extracellular processes, such as transcription, cell cycle progression, differentiation, secretion and apoptosis. Unsurprisingly, tight and precise kinase regulation is a prerequisite for normal cell functioning, whereas kinase dysregulation often leads to disease. Moreover, the functions of many kinases are regulated through protein–protein interactions, which in turn are mediated by phosphorylated motifs and often involve associations with the scaffolding and chaperon protein 14-3-3. Therefore, the aim of this review article is to provide an overview of the state of the art on 14-3-3-mediated kinase regulation, focusing on the most recent mechanistic insights into these important protein–protein interactions and discussing in detail both their structural aspects and functional consequences.

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Analysis of Protein Phosphorylation and Its Functional Impact on Protein–Protein Interactions via Text Mining of the Scientific Literature

Protein Bioinformatics - Methods in Molecular Biology ◽

10.1007/978-1-4939-6783-4_10 ◽

2017 ◽

pp. 213-232 ◽

Cited By ~ 3

Author(s):

Qinghua Wang ◽

Karen E. Ross ◽

Hongzhan Huang ◽

Jia Ren ◽

Gang Li ◽

...

Keyword(s):

Text Mining ◽

Protein Phosphorylation ◽

Protein Interactions ◽

Scientific Literature ◽

Protein Protein Interactions ◽

Functional Impact

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Prediction of Protein-Protein Interaction Strength Using Domain Features with Supervised Regression

The Scientific World JOURNAL ◽

10.1155/2014/240673 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 4

Author(s):

Mayumi Kamada ◽

Yusuke Sakuma ◽

Morihiro Hayashida ◽

Tatsuya Akutsu

Keyword(s):

Protein Interactions ◽

Interaction Strength ◽

Feature Space ◽

Relevance Vector Machine ◽

Protein Domain ◽

Support Vector ◽

Protein Protein Interactions ◽

Protein Protein Interaction ◽

Living Organisms ◽

Domain Information

Proteins in living organisms express various important functions by interacting with other proteins and molecules. Therefore, many efforts have been made to investigate and predict protein-protein interactions (PPIs). Analysis of strengths of PPIs is also important because such strengths are involved in functionality of proteins. In this paper, we propose several feature space mappings from protein pairs using protein domain information to predict strengths of PPIs. Moreover, we perform computational experiments employing two machine learning methods, support vector regression (SVR) and relevance vector machine (RVM), for dataset obtained from biological experiments. The prediction results showed that both SVR and RVM with our proposed features outperformed the best existing method.

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Subcellular micropatterning for visual immunoprecipitation reveals differences in cytosolic protein complexes downstream the EGFR

10.1101/2021.05.25.445547 ◽

2021 ◽

Author(s):

Roland Hager ◽

Ulrike Mueller ◽

Nicole Ollinger ◽

Julian Weghuber ◽

Peter Lanzerstorfer

Keyword(s):

Protein Interactions ◽

Protein Complexes ◽

Growth Factor Receptor ◽

Spatial Arrangement ◽

Spatiotemporal Analysis ◽

Protein Domain ◽

Protein Protein Interactions ◽

Cytosolic Protein ◽

Bait Construct ◽

Biochemical Analyses

Analysis of protein-protein interactions in living cells by protein micropatterning is currently limited to the spatial arrangement of transmembrane proteins and their corresponding downstream molecules. Here we present a robust method for visual immunoprecipitation of cytosolic protein complexes by use of an artificial transmembrane bait construct in combination with micropatterned antibody arrays on cyclic olefin polymer (COP) substrates. The method was used to characterize Grb2-mediated signalling pathways downstream the epidermal growth factor receptor (EGFR). Ternary protein complexes (Shc1:Grb2:SOS1 and Grb2:Gab1:PI3K) were identified and we found that EGFR downstream signalling is based on constitutively bound (Grb2:SOS1 and Grb2:Gab1) as well as on agonist-dependent protein associations with transient interaction properties (Grb2:Shc1 and Grb2:PI3K). Spatiotemporal analysis further revealed significant differences in stability and exchange kinetics of protein interactions. Furthermore, we could show that this approach is well suited to study the efficacy and specificity of SH2 and SH3 protein domain inhibitors in a live cell context. Altogether, this method represents a significant enhancement of quantitative subcellular micropatterning approaches as an alternative to standard biochemical analyses.

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