News Release

New tool mines scientific texts for fusion protein facts

Computational approach could aid efforts to develop personalized cancer treatments

Peer-Reviewed Publication


New Tool Mines Scientific Texts for Fusion Protein Facts

image: ProtFus finds protein interactions of fusions using text mining. view more 

Credit: Tagore et al, pcbi.1007239.

A new computational tool called ProtFus screens scientific literature to validate predictions about the activity of fusion proteins--proteins encoded by the joining of two genes that previously encoded two separate proteins. Somnath Tagore in the Frenkel-Morgenstern Lab at Bar-Ilan University, Israel, and colleagues present ProtFus in PLOS Computational Biology.

Different kinds of fusion proteins can arise naturally in the human body, sometimes leading to cancer. Understanding interactions between fusion proteins and other proteins can help improve personalized cancer treatment. However, the number of scientific papers discussing these interactions is growing rapidly, and there is no standard format for presenting this information. Thus, organizing and keeping abreast of this knowledge poses a major challenge.

ProtFus addresses this challenge by using computational strategies--such as text mining and machine learning--to analyze scientific literature from the online search engine PubMed. It is able to identify fusion proteins that may go by multiple names, and it can identify experimentally verified interactions between fusion proteins and other proteins. When applied to a test set of 1,817 fusion proteins, ProtFus identified 2,908 interactions across 18 cancer types that had been published in scientific texts in PubMed.

ProtFus also builds on a tool previously developed by the researchers in order to predict a given fusion protein's interactions based on the known properties of its two parental proteins. ProtFus takes a fusion protein of interest, uses the previously developed tool (Chimeric protein-Protein-Interactions, or ChiPPI) to predict its interactions, and then validates these interactions by means of a PubMed search.

"Our findings demonstrate the potential for text mining of large-scale scientific articles using a novel big-data infrastructure, with real-time updating from articles published daily," says Dr. Milana Frenkel-Morgenstern, corresponding author of the study. "ProtFus can promote studying alterations of protein networks for individual cancer patients in a fully personalized manner," highlights Tagore, the first author and previous postdoc in the lab (currently a postdoc at Columbia University, New York).


In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology:

Citation: Tagore S, Gorohovski A, Jensen LJ, Frenkel-Morgenstern M (2019) ProtFus: A Comprehensive Method Characterizing Protein-Protein Interactions of Fusion Proteins. PLoS Comput Biol 15(9): e1007239.

Funding: This work was supported by the PBC (VATAT) Fellowship for outstanding Post-Docs from China and India for MFM & ST 2015-2018 (22351, 20027), Israel Cancer Association grant for MFM for 2016-2017 (24562-01), for 2017-2018 (24562-02) and Danish-Israel collaboration grant for MFM & LJJ (0396010400).

Competing Interests: The authors have declared that no competing interests exist.

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