Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.
Bioinformatics. 2022 Jun 24;38(Suppl 1):i68-i76. doi: 10.1093/bioinformatics/btac240.
A critical element of drug development is the identification of therapeutic targets for diseases. However, the depletion of therapeutic targets is a serious problem.
In this study, we propose the novel concept of target repositioning, an extension of the concept of drug repositioning, to predict new therapeutic targets for various diseases. Predictions were performed by a trans-disease analysis which integrated genetically perturbed transcriptomic signatures (knockdown of 4345 genes and overexpression of 3114 genes) and disease-specific gene transcriptomic signatures of 79 diseases. The trans-disease method, which takes into account similarities among diseases, enabled us to distinguish the inhibitory from activatory targets and to predict the therapeutic targetability of not only proteins with known target-disease associations but also orphan proteins without known associations. Our proposed method is expected to be useful for understanding the commonality of mechanisms among diseases and for therapeutic target identification in drug discovery.
Supplemental information and software are available at the following website [http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/].
Supplementary data are available at Bioinformatics online.
药物开发的一个关键要素是确定疾病的治疗靶点。然而，治疗靶点的枯竭是一个严重的问题。
在这项研究中，我们提出了目标重定位的新概念，这是药物重定位概念的延伸，以预测各种疾病的新治疗靶点。通过跨疾病分析进行预测，该分析整合了遗传扰动的转录组特征（敲低 4345 个基因和过表达 3114 个基因）和 79 种疾病的特定疾病基因转录组特征。跨疾病方法考虑了疾病之间的相似性，使我们能够区分抑制性和激活性靶点，并预测不仅具有已知靶-疾病关联的蛋白质，而且还预测了没有已知关联的孤儿蛋白质的治疗靶标性。我们提出的方法有望用于理解疾病之间机制的共性，并有助于药物发现中的治疗靶点识别。
补充信息和软件可在以下网站获得[http://labo.bio.kyutech.ac.jp/~yamani/target_repositioning/]。
补充数据可在生物信息学在线获得。
