Literature DB >> 15943484

The PDBbind database: methodologies and updates.

Renxiao Wang1, Xueliang Fang, Yipin Lu, Chao-Yie Yang, Shaomeng Wang.   

Abstract

We have developed the PDBbind database to provide a comprehensive collection of binding affinities for the protein-ligand complexes in the Protein Data Bank (PDB). This paper gives a full description of the latest version, i.e., version 2003, which is an update to our recently reported work. Out of 23 790 entries in the PDB release No.107 (January 2004), 5897 entries were identified as protein-ligand complexes that meet our definition. Experimentally determined binding affinities (K(d), K(i), and IC(50)) for 1622 of these were retrieved from the references associated with these complexes. A total of 900 complexes were selected to form a "refined set", which is of particular value as a standard data set for docking and scoring studies. All of the final data, including binding affinity data, reference citations, and processed structural files, have been incorporated into the PDBbind database accessible on-line at http:// www.pdbbind.org/.

Mesh:

Substances:

Year:  2005        PMID: 15943484     DOI: 10.1021/jm048957q

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  161 in total

1.  Robust scoring functions for protein-ligand interactions with quantum chemical charge models.

Authors:  Jui-Chih Wang; Jung-Hsin Lin; Chung-Ming Chen; Alex L Perryman; Arthur J Olson
Journal:  J Chem Inf Model       Date:  2011-10-07       Impact factor: 4.956

2.  Improving molecular docking through eHiTS' tunable scoring function.

Authors:  Orr Ravitz; Zsolt Zsoldos; Aniko Simon
Journal:  J Comput Aided Mol Des       Date:  2011-11-11       Impact factor: 3.686

3.  Computational fragment-based screening using RosettaLigand: the SAMPL3 challenge.

Authors:  Ashutosh Kumar; Kam Y J Zhang
Journal:  J Comput Aided Mol Des       Date:  2012-01-15       Impact factor: 3.686

4.  A collaborative environment for developing and validating predictive tools for protein biophysical characteristics.

Authors:  Michael A Johnston; Damien Farrell; Jens Erik Nielsen
Journal:  J Comput Aided Mol Des       Date:  2012-04-04       Impact factor: 3.686

5.  Rapid prediction of solvation free energy. 3. Application to the SAMPL2 challenge.

Authors:  Enrico O Purisima; Christopher R Corbeil; Traian Sulea
Journal:  J Comput Aided Mol Des       Date:  2010-04-06       Impact factor: 3.686

6.  PDB ligand conformational energies calculated quantum-mechanically.

Authors:  Markus Sitzmann; Iwona E Weidlich; Igor V Filippov; Chenzhong Liao; Megan L Peach; Wolf-Dietrich Ihlenfeldt; Rajeshri G Karki; Yulia V Borodina; Raul E Cachau; Marc C Nicklaus
Journal:  J Chem Inf Model       Date:  2012-02-21       Impact factor: 4.956

7.  Scoring and lessons learned with the CSAR benchmark using an improved iterative knowledge-based scoring function.

Authors:  Sheng-You Huang; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2011-08-31       Impact factor: 4.956

8.  Incorporating specificity into optimization: evaluation of SPA using CSAR 2014 and CASF 2013 benchmarks.

Authors:  Zhiqiang Yan; Jin Wang
Journal:  J Comput Aided Mol Des       Date:  2016-02-15       Impact factor: 3.686

9.  A quantum mechanics-based halogen bonding scoring function for protein-ligand interactions.

Authors:  Zhuo Yang; Yingtao Liu; Zhaoqiang Chen; Zhijian Xu; Jiye Shi; Kaixian Chen; Weiliang Zhu
Journal:  J Mol Model       Date:  2015-05-10       Impact factor: 1.810

10.  Iterative Knowledge-Based Scoring Functions Derived from Rigid and Flexible Decoy Structures: Evaluation with the 2013 and 2014 CSAR Benchmarks.

Authors:  Chengfei Yan; Sam Z Grinter; Benjamin Ryan Merideth; Zhiwei Ma; Xiaoqin Zou
Journal:  J Chem Inf Model       Date:  2015-10-01       Impact factor: 4.956

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.