TY - JOUR
T1 - Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2
AU - Choi, Byung Kwon
AU - Dayaram, Tajhal
AU - Parikh, Neha
AU - Wilkins, Angela D.
AU - Nagarajan, Meena
AU - Novikov, Ilya B.
AU - Bachman, Benjamin J.
AU - Jung, Sung Yun
AU - Haas, Peter J.
AU - Labrie, Jacques L.
AU - Pickering, Curtis R.
AU - Adikesavan, Anbu K.
AU - Regenbogen, Sam
AU - Kato, Linda
AU - Lelescu, Ana
AU - Buchovecky, Christie M.
AU - Zhang, Houyin
AU - Bao, Sheng Hua
AU - Boyer, Stephen
AU - Weber, Griff
AU - Scott, Kenneth L.
AU - Chen, Ying
AU - Spangler, Scott
AU - Donehower, Lawrence A.
AU - Lichtarge, Olivier
N1 - Funding Information:
ACKNOWLEDGMENTS. We thank Carl Anderson for a careful reading of the manuscript and Jack Manquen and Danielle Jayanty for technical assistance. This work was supported in part by a gift from the Robert and Janice McNair Medical Foundation to the Computational and Integrative Biomedical Research Center at Baylor College of Medicine; by Defense Advanced Research Projects Agency (DARPA) Contract Grants N66001-14-1-4027 and N66001-15-C-4042; by IBM Research and the IBM Accelerated Discover Lab; and by NIH Grants GM079656 and NSF DBI 1356569 (to O.L.). Imaging was supported by the Integrated Microscopy Core, and mass spectrometry was supported by the Mass Spectrometry Proteomics Core at Baylor College of Medicine. Funding was also provided by the NIH (HD007495, DK56338, and CA125123); the Dan L. Duncan Cancer Center; and the John S. Dunn Gulf Coast Consortium for Chemical Genomics.
Publisher Copyright:
© 2018 National Academy of Sciences. All Rights Reserved.
PY - 2018/10/16
Y1 - 2018/10/16
N2 - 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.
AB - 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.
KW - Automated hypothesis generation
KW - Kinase
KW - Literature text mining
KW - Protein-protein interaction
KW - p53 inhibition
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U2 - 10.1073/pnas.1806643115
DO - 10.1073/pnas.1806643115
M3 - Article
C2 - 30266789
AN - SCOPUS:85054995671
SN - 0027-8424
VL - 115
SP - 10666
EP - 10671
JO - Proceedings of the National Academy of Sciences of the United States of America
JF - Proceedings of the National Academy of Sciences of the United States of America
IS - 42
ER -