Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.
Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2.
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DOI：
10.1073/pnas.1806643115
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发表时间：
2018-10-16
影响因子：
11.1
通讯作者：
Lichtarge O
中科院分区：
文献类型：
--
作者：
Choi BK;Dayaram T;Parikh N;Wilkins AD;Nagarajan M;Novikov IB;Bachman BJ;Jung SY;Haas PJ;Labrie JL;Pickering CR;Adikesavan AK;Regenbogen S;Kato L;Lelescu A;Buchovecky CM;Zhang H;Bao SH;Boyer S;Weber G;Scott KL;Chen Y;Spangler S;Donehower LA;Lichtarge O
我们将自然语言处理应用于生物文献，并通过探索微妙的单词连接演示了端到端的自动化知识发现。通用文本挖掘扫描了2100万份出版物摘要，并从中选择了可靠的13万份，其中假设生成算法预测了未知但可能磷酸化p53的激酶。这些p53激酶候选物中有6个通过了实验验证。其中对NEK2进行了深入研究，发现其抑制p53并促进细胞分裂。这项工作证明了整合大量书面知识来计算有价值的假设的可能性，这些假设通常会检验真实并推动发现。科学进步依赖于根据文献提出可检验的假设。然而，在许多领域，这个模型是紧张的，因为研究论文的数量超过了人类的可读性。在这里，我们开发了计算辅助，通过阅读PubMed摘要来分析生物医学文献，以提出新的假设。该方法在肿瘤抑制因子p53上进行了实验测试，根据所有可用的摘要，对其最可能的激酶进行了排序。许多排名靠前的激酶被发现与p53结合并磷酸化（P值= 0.005），迄今为止有六种可能的p53激酶。研究人员对其中的NEK2进行了详细研究。NEK2是一种已知的有丝分裂启动子，在体外和体内均可使p53的315位丝氨酸磷酸化，并在功能上抑制p53。这些基于文本的p53磷酸化预测的真实验证，以及对药物感兴趣的抑制p53激酶的发现，表明使用大量文献的自动推理可以产生有价值的分子假设，并有可能加速科学发现。
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.
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影响因子：
8
作者：
通讯作者：
--
DOI：
10.2741/4212
发表时间：
2014-01-01
期刊：
Frontiers in bioscience (Landmark edition)
影响因子：
--
作者：
Marina M;Saavedra HI
通讯作者：
Saavedra HI
影响因子：
4
作者：
Brognard, John;Hunter, Tony
通讯作者：
Hunter, Tony
Predose：使用社交媒体流行病学的语义网络平台。
影响因子：
4.5
作者：
Cameron, Delroy;Smith, Gary A.;Daniulaityte, Raminta;Sheth, Amit P.;Dave, Drashti;Chen, Lu;Anand, Gaurish;Carlson, Robert;Watkins, Kera Z.;Falck, Russel
通讯作者：
Falck, Russel
DOI：
10.1136/amiajnl-2012-001173
发表时间：
2013-09
期刊：
Journal of the American Medical Informatics Association : JAMIA
影响因子：
--
作者：
Kang N;Singh B;Afzal Z;van Mulligen EM;Kors JA
通讯作者：
Kors JA