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

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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The Bacterial Phosphoenolpyruvate:Carbohydrate Phosphotransferase System: Regulation by Protein Phosphorylation and Phosphorylation-Dependent Protein-Protein Interactions

Microbiology and Molecular Biology Reviews ◽

10.1128/mmbr.00001-14 ◽

2014 ◽

Vol 78 (2) ◽

pp. 231-256 ◽

Cited By ~ 180

Author(s):

Josef Deutscher ◽

Francine Moussan Désirée Aké ◽

Meriem Derkaoui ◽

Arthur Constant Zébré ◽

Thanh Nguyen Cao ◽

...

Keyword(s):

Protein Phosphorylation ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Phosphotransferase System ◽

Dependent Protein

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Building and analysis of protein-protein interactions related to diabetes mellitus using support vector machine, biomedical text mining and network analysis

Computational Biology and Chemistry ◽

10.1016/j.compbiolchem.2016.09.011 ◽

2016 ◽

Vol 65 ◽

pp. 37-44 ◽

Cited By ~ 11

Author(s):

Renu Vyas ◽

Sanket Bapat ◽

Esha Jain ◽

Muthukumarasamy Karthikeyan ◽

Sanjeev Tambe ◽

...

Keyword(s):

Diabetes Mellitus ◽

Support Vector Machine ◽

Network Analysis ◽

Text Mining ◽

Protein Interactions ◽

Support Vector ◽

Biomedical Text ◽

Biomedical Text Mining ◽

Protein Protein Interactions

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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Nucleic Acids Research ◽

10.1093/nar/gkaa1074 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D605-D612 ◽

Cited By ~ 2

Author(s):

Damian Szklarczyk ◽

Annika L Gable ◽

Katerina C Nastou ◽

David Lyon ◽

Rebecca Kirsch ◽

...

Keyword(s):

Text Mining ◽

Protein Interactions ◽

Functional Characterization ◽

Protein Networks ◽

Protein Protein Interactions ◽

Genomic Context ◽

Mining System ◽

String Database ◽

System A ◽

Physical Interactions

Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.

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How does Protein Phosphorylation Control Protein-Protein Interactions in the Photosynthetic Membrane?

Current Research in Photosynthesis ◽

10.1007/978-94-009-0511-5_431 ◽

1990 ◽

pp. 1875-1878

Author(s):

John F. Allen

Keyword(s):

Protein Phosphorylation ◽

Protein Interactions ◽

Protein Protein Interactions ◽

Photosynthetic Membrane ◽

Control Protein

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Protein Interaction Networks

Computational Text Analysis ◽

10.1093/oso/9780198567400.003.0017 ◽

2006 ◽

Author(s):

Soumya Raychaudhuri

Keyword(s):

Gene Expression ◽

Protein Interactions ◽

Large Scale ◽

Scientific Literature ◽

Genetic Interaction ◽

Genetic Interactions ◽

Protein Protein Interactions ◽

Expression Array ◽

Biological Responses ◽

Altered Gene

Genes and proteins interact with each other in many complicated ways. For example, proteins can interact directly with each other to form complexes or to modify each other so that their function is altered. Gene expression can be repressed or induced by transcription factor proteins. In addition there are countless other types of interactions. They constitute the key physiological steps in regulating or initiating biological responses. For example the binding of transcription factors to DNA triggers the assembly of the RNA assembly machinery that transcribes the mRNA that then is used as the template for protein production. Interactions such as these have been carefully elucidated and have been described in great detail in the scientific literature. Modern assays such as yeast-2-hybrid screens offer rapid means to ascertain many of the potential protein–protein interactions in an organism in a large-scale approach. In addition, other experimental modalities such as gene-expression array assays offer indirect clues about possible genetic interactions. One area that has been greatly explored in the bioinformatics literature is the possibility of learning genetic or protein networks, both from the scientific literature and from large-scale experimental data. Indeed, as we get to know more and more genes, it will become increasingly important to appreciate their interactions with each other. An understanding of the interactions between genes and proteins in a network allows for a meaningful global view of the organism and its physiology and is necessary to better understand biology. In this chapter we will explore methods to either (1) mine the scientific literature to identify documented genetic interactions and build networks of genes or (2) to confirm protein interactions that have been proposed experimentally. Our focus here is on direct physical protein–protein interactions, though the techniques described could be extended to any type of biological interaction between genes or proteins. There are multiple steps that must be addressed in identifying genetic interaction information contained within the text. After compiling the necessary documents and text, the first step is to identify gene and protein names in the text.

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