Chan Wallace K B, Zhang Hongjiu, Yang Jianyi, Brender Jeffrey R, Hur Junguk, Özgür Arzucan, Zhang Yang
Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.
Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey Department of Biological Chemistry, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA, Department of Basic Sciences, University of North Dakota, School of Medicine and Health Sciences, Grand Forks, ND 58203, USA and Department of Computer Engineering, Bogazici University, Istanbul, Turkey.
Bioinformatics. 2015 Sep 15;31(18):3035-42. doi: 10.1093/bioinformatics/btv302. Epub 2015 May 13.
G protein-coupled receptors (GPCRs) are probably the most attractive drug target membrane proteins, which constitute nearly half of drug targets in the contemporary drug discovery industry. While the majority of drug discovery studies employ existing GPCR and ligand interactions to identify new compounds, there remains a shortage of specific databases with precisely annotated GPCR-ligand associations.
We have developed a new database, GLASS, which aims to provide a comprehensive, manually curated resource for experimentally validated GPCR-ligand associations. A new text-mining algorithm was proposed to collect GPCR-ligand interactions from the biomedical literature, which is then crosschecked with five primary pharmacological datasets, to enhance the coverage and accuracy of GPCR-ligand association data identifications. A special architecture has been designed to allow users for making homologous ligand search with flexible bioactivity parameters. The current database contains ∼500 000 unique entries, of which the vast majority stems from ligand associations with rhodopsin- and secretin-like receptors. The GLASS database should find its most useful application in various in silico GPCR screening and functional annotation studies.
The website of GLASS database is freely available at http://zhanglab.ccmb.med.umich.edu/GLASS/.
Supplementary data are available at Bioinformatics online.
G蛋白偶联受体(GPCRs)可能是最具吸引力的药物靶点膜蛋白,在当代药物研发行业中,它们构成了近一半的药物靶点。虽然大多数药物研发研究利用现有的GPCR与配体相互作用来鉴定新化合物,但仍缺乏具有精确注释的GPCR-配体关联的特定数据库。
我们开发了一个新的数据库GLASS,旨在为经过实验验证的GPCR-配体关联提供一个全面的、人工整理的资源。提出了一种新的文本挖掘算法,用于从生物医学文献中收集GPCR-配体相互作用,然后与五个主要药理学数据集进行交叉核对,以提高GPCR-配体关联数据识别的覆盖范围和准确性。设计了一种特殊的架构,允许用户使用灵活的生物活性参数进行同源配体搜索。当前数据库包含约500,000个独特条目,其中绝大多数来自与视紫红质样和促胰液素样受体的配体关联。GLASS数据库应在各种计算机模拟GPCR筛选和功能注释研究中找到其最有用的应用。
GLASS数据库的网站可在http://zhanglab.ccmb.med.umich.edu/GLASS/免费获取。
补充数据可在《生物信息学》在线获取。