To search this database, you have only to type a keyword related to the system of your research on the field "Search our systems..." indicated below. Once found the dataset, click on the ZIP icon (on the right) to download the structures and the binding affinity data. Each zipped folder has the binding/thermodynamic data and the crystallographic structures (lig.pdbqtandreceptor.pdbqt) for the PDBs in the dataset. The binding affinity (e.g. Ki) or thermodynamic (e.g. DeltaG) data are available in thechklig.infile (last column) found in the unzipped folder. The second column ofchklig.infile indicates the PDB access code, the following columns indicate the data related to the active ligand bound to the structure.
In this dataset, we have 30 structures used to obtain the regression weights for the AutoDock 3 scoring function (Morris et al., 1998).
These structures might be employed as benchmark to test the predictive performance of new scoring functions. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Automated docking; binding affinity; drug design; scoring function; AutoDock
References
Morris GM, Goodsell DS, Halliday RS, Huey R, Hart WE, Belew RK, Olson AJ. Automated Docking Using a Lamarckian Genetic Algorithm and an Empirical Binding Free Energy Funtion. J Comput Chem. 1998; 19(14):1639-1662.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
Beta Galactosidase with Inhibition Constant (Ki) Data
In this dataset, we have 24 crystallographic structures of beta galactosidase (EC 3.2.1.23) with inhibition constant (Ki) data.
These structures can be applied to develop targeted-scoring functions for beta galactosidase. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
beta galactosidase; binding affinity; drug design; scoring function; inhibition constant
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
Cyclin-Dependent Kinase with Half Maximal Inhibitory Concentration (IC50) Data
In this dataset, we have 176 crystallographic structures of cyclin-dependent kinase (CDK) (EC 2.7.11.22) with half maximal inhibitory concentration (IC50) data.
These structures can be applied to develop targeted-scoring functions for CDK. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 167 crystallographic structures of cyclin-dependent kinase 2 (CDK2) (EC 2.7.11.22) with half maximal inhibitory concentration (IC50) data.
These structures can be applied to develop targeted-scoring functions for CDK2. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
Cyclin-Dependent Kinase (except CDK2) with Half Maximal Inhibitory Concentration (IC50) Data
1UNG,1UNH,3BLR,3LQ5,3O0G,3RGF,3TN8,4AU8,4AUA
In this dataset, we have 9 crystallographic structures of cyclin-dependent kinase (CDK) (EC 2.7.11.22) with half maximal inhibitory concentration (IC50) data.
This dataset doesn't bring CDK2 structures.
These structures can be applied to develop targeted-scoring functions for non-CDK2 structure. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 133 crystallographic structures of cyclin-dependent kinase 2 (CDK2) (without cyclin partner) (EC 2.7.11.22) with half maximal inhibitory concentration (IC50) data.
These structures can be applied to develop targeted-scoring functions for CDK2. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 34 crystallographic structures of cyclin-dependent kinase 2 (CDK2) (with cyclin partner) (EC 2.7.11.22) with half maximal inhibitory concentration (IC50) data.
These structures can be applied to develop targeted-scoring functions for CDK2 in complex with cyclin. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 22 crystallographic structures of 3-dehydroquinate dehydratase (DHQD) (EC 4.2.1.10) with inhibition constant (Ki) data.
These structures can be applied to develop targeted-scoring functions for 3-dehydroquinate dehydratase. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 28 crystallographic structures of enoyl-[acyl-carrier-protein] reductase (EC 1.3.1.9) with inhibition constant (Ki) data.
These structures can be applied to develop targeted-scoring functions for enoyl-[acyl-carrier-protein] reductase. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 32 crystallographic structures of estrogen receptor alpha with half maximal inhibitory concentration (IC50) data.
These structures can be applied to develop targeted-scoring functions for estrogen receptor alpha. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 31 crystallographic structures of HIV1-protease with DeltaG (ΔG) data.
These structures can be applied to develop targeted-scoring functions for estrogen receptor alpha. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
HIV1-protease; HIV; Aids; binding affinity; drug design; scoring function; Gibbs free energy
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
In this dataset, we have 68 crystallographic structures of HIV1-protease with inhibition constant (Ki) data.
These structures can be applied to develop targeted-scoring functions for estrogen receptor alpha. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
In this dataset, we have 22 high-resolution structures (Oxidoreductases)(EC 1. -. -.-) with inhibition constant (Ki) data.
These structures can be applied to develop novel scoring functions specific for oxydoreductases. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Drug design; oxidoreductases; scoring function; protein-ligand interactions; inhibition constant (Ki)
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
High-Resolution Structures (Transferases) with Inhibition Constant (Ki) Data
In this dataset, we have 32 high-resolution structures (Transferases)(EC 2. -. -.-) with inhibition constant (Ki) data.
These structures can be applied to develop novel scoring functions specific for oxydoreductases. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Drug design; transferases; scoring function; protein-ligand interactions; inhibition constant (Ki)
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
High-Resolution Structures (Hydrolases) with Inhibition Constant (Ki) Data
In this dataset, we have 155 high-resolution structures (Hydrolases)(EC 3. -. -.-) with inhibition constant (Ki) data.
These structures can be applied to develop novel scoring functions specific for oxydoreductases. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Drug design; hydrolases; scoring function; protein-ligand interactions; inhibition constant (Ki)
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
High-Resolution Structures (Lyases) with Inhibition Constant (Ki) Data
In this dataset, we have 38 high-resolution structures (Lyases)(EC 4. -. -.-) with inhibition constant (Ki) data.
These structures can be applied to develop novel scoring functions specific for oxydoreductases. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Drug design; lyases; scoring function; protein-ligand interactions; inhibition constant (Ki)
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
High-Resolution Structures (Isomerases) with Inhibition Constant (Ki) Data
1GYX,1GYY,1O8B,4DRI,4JFI,4JFJ,4JFM,4TW6,4TW7,4TX0
In this dataset, we have 10 high-resolution structures (Isomerases)(EC 5. -. -.-) with inhibition constant (Ki) data.
These structures can be applied to develop novel scoring functions specific for oxydoreductases. We generated PDBQT files
using AutoDockTool4 (Morris et al., 2009).
Keywords
Drug design; isomerases; scoring function; protein-ligand interactions; inhibition constant (Ki)
Reference
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem. 2009; 30(16):2785-2791.
Datasets
The major application of the data available here is the development of targeted-scoring functions making use of machine-learning techniques such as the ones available in the programs SAnDReS (Xavieret al., 2016) and Taba (da Silvaet al., 2020). We expect to use this structural and binding information to explore the scoring function space (Hecket al., 2017;Bitencourt-Ferreira & de Azevedo, 2019) and design scoring functions targeted to each of the datasets available here.
Weused these datasets to evaluate the predictive performance of the Taba program (da Silvaet al., 2020). Taba is an acronym forTool to Analyze the Binding Affinity. This program uses a physical mass-spring model to generate machine-learning models to predict binding affinity based on the atomic coordinates of protein-ligand complexes. We applied it to a dataset of the cyclin-dependent kinase (EC 2.7.11.22) with inhibition constant (Ki) data. This dataset is available below (Cyclin-Dependent Kinase with Inhibition Constant (Ki) Data).
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Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE. The Protein Data Bank. Nucleic Acids Res., 2000; 28(1): 235–242. PubMed
Bitencourt-Ferreira G, de Azevedo WF Jr. Exploring the Scoring Function Space. Methods Mol Biol. 2019; 2053: 275–281. PubMed
Chen X, Liu M, Gilson MK. BindingDB: a web-accessible molecular recognition database.Comb Chem High Throughput Screen. 2001; 4(8): 719–725. PubMed
da Silva AD, Bitencourt-Ferreira G, de Azevedo WF Jr. Taba: A Tool to Analyze the Binding Affinity. J Comput Chem. 2020; 41(1): 69–73. PubMed
Heck GS, Pintro VO, Pereira RR, de Ávila MB, Levin NMB, de Azevedo WF. Supervised Machine Learning Methods Applied to Predict Ligand-Binding Affinity. Curr Med Chem. 2017; 24(23): 2459–2470. PubMed
Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. 2009; J Comput Chem 30: 2785–2791. PubMed
Seifert MH. Targeted scoring functions for virtual screening. Drug Discov Today. 2009;14(11-12):562–569. PubMed
Wang R, Fang X, Lu Y, Wang S. The PDBbind database: collection of binding affinities for protein-ligand complexes with known three-dimensional structures. J Med Chem. 2004; 47(12): 2977–2980. PubMed
Xavier MM, Heck GS, de Avila MB, Levin NM, Pintro VO, Carvalho NL, Azevedo WF Jr. SAnDReS a Computational Tool for Statistical Analysis of Docking Results and Development of Scoring Functions. Comb. Chem. High Throughput Screen. 2016; 19(10): 801–12. PubMedPDFGitHub