Zhihai Liu1, Yan Li1, Li Han1, Jie Li1, Jie Liu1, Zhixiong Zhao1, Wei Nie1, Yuchen Liu1, Renxiao Wang2. 1. State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032 and State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People's Republic of China. 2. State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032 and State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People's Republic of China State Key Laboratory of Bioorganic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Lingling Road, Shanghai 200032 and State Key Laboratory of Quality Research in Chinese Medicine, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People's Republic of China.
Abstract
MOTIVATION: Molecular recognition between biological macromolecules and organic small molecules plays an important role in various life processes. Both structural information and binding data of biomolecular complexes are indispensable for depicting the underlying mechanism in such an event. The PDBbind database was created to collect experimentally measured binding data for the biomolecular complexes throughout the Protein Data Bank (PDB). It thus provides the linkage between structural information and energetic properties of biomolecular complexes, which is especially desirable for computational studies or statistical analyses. RESULTS: Since its first public release in 2004, the PDBbind database has been updated on an annual basis. The latest release (version 2013) provides experimental binding affinity data for 10,776 biomolecular complexes in PDB, including 8302 protein-ligand complexes and 2474 other types of complexes. In this article, we will describe the current methods used for compiling PDBbind and the updated status of this database. We will also review some typical applications of PDBbind published in the scientific literature. AVAILABILITY AND IMPLEMENTATION: All contents of this database are freely accessible at the PDBbind-CN Web server at http://www.pdbbind-cn.org/. CONTACT: wangrx@mail.sioc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Molecular recognition between biological macromolecules and organic small molecules plays an important role in various life processes. Both structural information and binding data of biomolecular complexes are indispensable for depicting the underlying mechanism in such an event. The PDBbind database was created to collect experimentally measured binding data for the biomolecular complexes throughout the Protein Data Bank (PDB). It thus provides the linkage between structural information and energetic properties of biomolecular complexes, which is especially desirable for computational studies or statistical analyses. RESULTS: Since its first public release in 2004, the PDBbind database has been updated on an annual basis. The latest release (version 2013) provides experimental binding affinity data for 10,776 biomolecular complexes in PDB, including 8302 protein-ligand complexes and 2474 other types of complexes. In this article, we will describe the current methods used for compiling PDBbind and the updated status of this database. We will also review some typical applications of PDBbind published in the scientific literature. AVAILABILITY AND IMPLEMENTATION: All contents of this database are freely accessible at the PDBbind-CN Web server at http://www.pdbbind-cn.org/. CONTACT: wangrx@mail.sioc.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Mei Qian Yau; Abigail L Emtage; Nathaniel J Y Chan; Stephen W Doughty; Jason S E Loo Journal: J Comput Aided Mol Des Date: 2019-04-15 Impact factor: 3.686
Authors: Richard D Smith; Jordan J Clark; Aqeel Ahmed; Zachary J Orban; James B Dunbar; Heather A Carlson Journal: J Mol Biol Date: 2019-05-22 Impact factor: 5.469