Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2
| Autor: | Ilya B. Novikov, Tajhal Dayaram, Neha Parikh, Griff Weber, Angela D. Wilkins, Scott Spangler, Sam Regenbogen, Ying Chen, Linda Kato, Ana Lelescu, Shenghua Bao, Benjamin J. Bachman, Byung-Kwon Choi, Houyin Zhang, Anbu Karani Adikesavan, Curtis R. Pickering, Lawrence A. Donehower, Meena Nagarajan, Christie M. Buchovecky, Kenneth L. Scott, Jacques L. Labrie, Olivier Lichtarge, Sung Yun Jung, Peter J. Haas, Stephen K. Boyer |
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| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
p53 inhibition PubMed kinase Abstracting and Indexing Computational biology P53 phosphorylation Biology automated hypothesis generation Protein–protein interaction law.invention 03 medical and health sciences law Humans NIMA-Related Kinases Automated reasoning Phosphorylation Natural Language Processing Multidisciplinary Kinase HEK 293 cells Biological Sciences HCT116 Cells 3. Good health Biophysics and Computational Biology protein–protein interaction HEK293 Cells 030104 developmental biology Suppressor Tumor Suppressor Protein p53 literature text mining |
| Zdroj: | Proceedings of the National Academy of Sciences of the United States of America |
| ISSN: | 1091-6490 0027-8424 |
| DOI: | 10.1073/pnas.1806643115 |
| Popis: | Significance We adapted natural language processing to the biological literature and demonstrated end-to-end automated knowledge discovery by exploring subtle word connections. General text mining scanned 21 million publication abstracts and selected a reliable 130,000 from which hypothesis generation algorithms predicted kinases not known to phosphorylate p53, but likely to do so. Six of these p53 kinase candidates passed experimental validation. Among them NEK2 was examined in depth and shown to repress p53 and promote cell division. This work demonstrates the possibility of integrating a vast corpora of written knowledge to compute valuable hypotheses that will often test true and fuel discovery. 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. |
| Databáze: | OpenAIRE |
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