TDR Targets: a chemogenomics resource for neglected diseases.

TDR Targets: a chemogenomics resource for neglected diseases.
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TDR靶标:用于被忽视疾病的化学基因组学资源。

DOI:
10.1093/nar/gkr1053
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发表时间:
2012-01
影响因子:
14.9
通讯作者:
Agüero F
Agüero F
中科院分区:
生物学2区
文献类型:
--
作者:
Magariños MP;Carmona SJ;Crowther GJ;Ralph SA;Roos DS;Shanmugam D;Van Voorhis WC;Agüero F

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TDR目标数据库(http://tdrtargets.org)是作为一种在线资源设计和开发的,目的是促进药物开发的分子目标的快速识别和优先排序,重点是导致被忽视的人类疾病的病原体。该数据库将病原体特异性基因组信息与从各种来源(包括文献整理)收集的基因的功能数据(例如表达、系统发育、必要性)集成在一起。这些信息可以通过一个具有组合、保存、导出和共享查询结果功能的广泛的web界面来浏览和查询。可以使用分配给用于查询的标准的数值权重对目标基因进行排序和优先级排序。在本报告中,我们描述了最近对TDR目标数据库的更新,包括增加新的基因组(特别是蠕虫),整合生物配体、药物和抑制剂的化学结构、性质和生物活性信息,以及用于查询和可视化这些化学数据的化学信息学工具。这些变化极大地促进了对基因和小分子之间联系的探索(无论是已知的还是预测的),从而深入了解特定蛋白质是否可以药物化,从而有效地在基因组学背景下导航化学空间。
The TDR Targets Database (http://tdrtargets.org) has been designed and developed as an online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases. The database integrates pathogen specific genomic information with functional data (e.g. expression, phylogeny, essentiality) for genes collected from various sources, including literature curation. This information can be browsed and queried using an extensive web interface with functionalities for combining, saving, exporting and sharing the query results. Target genes can be ranked and prioritized using numerical weights assigned to the criteria used for querying. In this report we describe recent updates to the TDR Targets database, including the addition of new genomes (specifically helminths), and integration of chemical structure, property and bioactivity information for biological ligands, drugs and inhibitors and cheminformatic tools for querying and visualizing these chemical data. These changes greatly facilitate exploration of linkages (both known and predicted) between genes and small molecules, yielding insight into whether particular proteins may be druggable, effectively allowing the navigation of chemical space in a genomics context.
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