Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2

Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading Pub...

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Published in:	Proceedings of the National Academy of Sciences - PNAS Vol. 115; no. 42; pp. 10666 - 10671
Main Authors:	Choi, Byung-Kwon, Dayaram, Tajhal, Parikh, Neha, Wilkins, Angela D., Nagarajan, Meena, Novikov, Ilya B., Bachman, Benjamin J., Jung, Sung Yun, Haas, Peter J., Labrie, Jacques L., Pickering, Curtis R., Adikesavan, Anbu K., Regenbogen, Sam, Kato, Linda, Lelescu, Ana, Buchovecky, Christie M., Zhang, Houyin, Bao, Sheng Hua, Boyer, Stephen, Weber, Griff, Scott, Kenneth L., Chen, Ying, Spangler, Scott, Donehower, Lawrence A., Lichtarge, Olivier
Format:	Journal Article
Language:	English
Published:	United States National Academy of Sciences 16-10-2018
Subjects:	Abstracting and Indexing as Topic Automated reasoning Biological Sciences Biomedical materials Cell division Computer applications Domains HCT116 Cells HEK293 Cells Humans Hypotheses Kinases Mitosis Molecular biology Natural Language Processing NIMA-Related Kinases - genetics NIMA-Related Kinases - metabolism p53 Protein Pharmaceuticals Phosphorylation PubMed Scientific papers Tumor suppressor genes Tumor Suppressor Protein p53 - antagonists & inhibitors Tumor Suppressor Protein p53 - genetics Tumor Suppressor Protein p53 - metabolism Tumors literature text mining p53 inhibition protein–protein interaction automated hypothesis generation kinase
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Summary:	Scientific progress depends on formulating testable hypotheses informed by the literature. In many domains, however, this model is strained because the number of research papers exceeds human readability. Here, we developed computational assistance to analyze the biomedical literature by reading PubMed abstracts to suggest new hypotheses. The approach was tested experimentally on the tumor suppressor p53 by ranking its most likely kinases, based on all available abstracts. Many of the best-ranked kinases were found to bind and phosphorylate p53 (P value = 0.005), suggesting six likely p53 kinases so far. One of these, NEK2, was studied in detail. A known mitosis promoter, NEK2 was shown to phosphorylate p53 at Ser315 in vitro and in vivo and to functionally inhibit p53. These bona fide validations of text-based predictions of p53 phosphorylation, and the discovery of an inhibitory p53 kinase of pharmaceutical interest, suggest that automated reasoning using a large body of literature can generate valuable molecular hypotheses and has the potential to accelerate scientific discovery.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 1B.-K.C., T.D., N.P., and A.D.W. contributed equally to this work. Edited by Carol Prives, Columbia University, New York, NY, and approved August 28, 2018 (received for review April 29, 2018) 2Present address: Department of Diagnostic and Biomedical Sciences, School of Dentistry at Houston, The University of Texas Health Science Center, Houston, TX 77054. Author contributions: B.-K.C., T.D., N.P., A.D.W., S.B., G.W., K.L.S., Y.C., S.S., L.A.D., and O.L. designed research; B.-K.C., T.D., N.P., A.D.W., M.N., I.B.N., B.J.B., S.Y.J., P.J.H., J.L.L., C.R.P., A.K.A., S.R., L.K., A.L., C.M.B., H.Z., and S.H.B. performed research; B.-K.C., T.D., N.P., A.D.W., M.N., I.B.N., B.J.B., S.Y.J., P.J.H., J.L.L., C.R.P., A.K.A., S.R., L.K., A.L., C.M.B., H.Z., S.H.B., and S.S. analyzed data; and B.-K.C., T.D., N.P., A.D.W., S.S., L.A.D., and O.L. wrote the paper.
ISSN:	0027-8424 1091-6490
DOI:	10.1073/pnas.1806643115