Database NoteDevelopment of KiBank, a database supporting structure-based drug design
Introduction
Since last decade, structure-based drug design (SBDD) has become a mature discipline of medicinal chemistry (Anderson, 2003, Böhm, 1996, Klebe, 2000, Nakata, 2002), and development of computer technologies to calculate molecular properties, of combinatorial chemistry and abundant data on target proteins coming from human genome research have opened new opportunities and feasible approaches for drug discovery (Bailey and Brown, 2001, Kirkpatrick et al., 1999).
Estimation of the binding affinity of novel chemicals to target proteins is a critical procedure in computational approaches to drug design including SBDD. The strategies that can be applied for this purpose fall in to two major categories: a target-based approach and a ligand-based approach. Recently, some researchers have combined both of these approaches in an automated unbiased procedure (Dean et al., 2004, Loew et al., 1993, Sippl, 2002b). The former can be used if the 3D structure of the binding site is known as is the case of SBDD. In practice, in silico screening of chemical databases is widely applied to find lead candidates for target proteins. Each of the reported methods has two steps, docking and scoring (Ewing et al., 2001, Goodsell et al., 1996, Jones et al., 1997, Rarey et al., 1996). Although several scoring methods for estimating binding affinity have been documented, it is not yet clear which docking/scoring combinations will provide the best accuracy. Therefore, before beginning to screen the entire chemical database, it is necessary to test docking/scoring combinations and program parameter settings by a test screening of a reduced database including known ligands. Experimental binding affinity values are also needed for the calibration of most scoring functions (Bissantz et al., 2000; Schneider and Böhm, 2002). On the other hand, traditional quantitative structure–activity relationship (QSAR) and modern 3D QSAR techniques are widely used in the ligand-based approach when the target structure is unknown (Akamatsu, 2002, Loew et al., 1993, Yoo et al., 2000). In the past few years, QSAR techniques have also been used in combination with structure-based methods (Lozano et al., 2000, Sippl, 2002b, Vaz et al., 1998). The QSAR techniques are based on experimental structure–activity relationships, and thus require large amount of experimental data.
Generally, to perform SBDD, 3D structures of target proteins are definitely needed, while experimental data is indispensable for accurate estimation of the binding affinities of newly designed chemicals. Although a database including such data will exactly facilitate this drug discovery approach, to our knowledge, it has been nonexistent up to now. Therefore, we developed KiBank providing Ki values and 3D structure files of chemicals and proteins ready for use in SBDD.
Section snippets
Creating KiBank
KiBank is a PostgreSQL database consisting of three knowledge areas — binding affinity data, chemical data and protein data (Aizawa et al., 2004) (Fig. 1).
As the binding affinity data, inhibition constant (Ki) values were accumulated from peer-reviewed literature searched via PubMed by using the name of a target protein (e.g., androgen receptor) and “Ki” as search terms. Articles published from 1985, with the majority from around the year 2000, were then selected for input into KiBank through
Results and discussion
As of August 2004, KiBank contains 166 proteins covering the subtypes of receptors and enzymes, over 6000 chemicals and 8000 Ki values.
A drug is effective when it binds more specifically and tightly to the target protein against natural ligands in a competitive fashion (McIlwain, 1986). Thus consideration of chemicals’ binding competitiveness is important for the computational approaches to drug design. Experimental binding affinities are reported as the inhibition constant (Ki), relative
Acknowledgements
This research was done under the “Frontier Simulation Software for Industrial Science (FSIS)” project supported by the IT program of the Japanese Ministry of Education, Culture, Sports, Science and Technology (MEXT), and was partly supported by the Toxico-proteomics project fund from the Japanese Ministry of Health, Labour and Welfare.
References (43)
The process of structure-based drug design
Chem. Biol.
(2003)- et al.
High-throughput chemistry and structure-based design: survival of the smartest
Drug Discov. Today
(2001) Computational tools for structure-based ligand design
Prog. Biophys. Mol. Biol.
(1996)- et al.
Development and validation of a genetic algorithm for flexible docking
J. Mol. Biol.
(1997) Drug-like properties and the causes of poor solubility and poor permeability
J. Pharmacol. Toxicol. Meth.
(2000)- et al.
Fragment molecular orbital method: application to polypeptides
Chem. Phys. Lett.
(2000) - et al.
Fragment molecular orbital method: use of approximate electrostatic potential
Chem. Phys. Lett.
(2002) - et al.
A fast flexible docking method using an incremental construction algorithm
J. Mol. Biol.
(1996) - et al.
Virtual screening and fast automated docking methods
Drug Discov. Today
(2002) Binding affinity prediction of novel estrogen receptor ligands using receptor-based 3D QSAR methods
Bioorg. Med. Chem.
(2002)
The conformation and activity relationship of benzofuran type of angiotensin II receptor antagonists
Bioorg. Med. Chem.
KiBank: a database for computer-aided drug design based on protein–chemical interaction analysis
Yakugaku Zasshi
Current state and perspectives of 3D-QSAR
Curr. Top. Med. Chem.
Complete protein structure determination using backbone residual dipolar couplings and side chain rotamer prediction
J. Struct. Funct. Genom.
GenBank
Nucl. Acids Res.
The protein data bank
Nucl. Acids Res.
Protein-based virtual screening of chemical databases. 1. Evaluation of different docking/scoring combinations
J. Med. Chem.
The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003
TTD: therapeutic target database
Nucl. Acids Res.
Future vision of the GDB human genome database
Human Mutat.
Cited by (46)
Concepts and Experimental Protocols of Modelling and Informatics in Drug Design
2020, Concepts and Experimental Protocols of Modelling and Informatics in Drug DesignChapter 11 SAR Knowledge Bases in Drug Discovery
2008, Annual Reports in Computational ChemistryCitation Excerpt :The data comprise those generated within the host laboratory at the University of North Carolina (sponsored by the National Institute of Mental Health) together with data extracted from the literature. The inhibition constant (Ki) values in KiBank [27,28] (http://kibank.iis.u-tokyo.ac.jp) have been extracted from scientific journals (from 1985 onwards) via PubMed searches. KiBank was originally constructed with a structural emphasis and it does include information on 3D protein structure where applicable.
Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins
2007, Journal of Pharmaceutical SciencesCitation Excerpt :Mining of the compounds known to have a particular property and those do not have that property from the literature117 and other sources118, 119 is a key to more extensive exploration of ML methods. Databases such as PDSP Ki database,120 KiBank,121 PubChem,122 and CLiBE123 that provide compound property and activity data are useful resources for serving this purpose, and more such databases are desired. In the datasets of some of the reported studies, there appears to be a significant imbalance between the numbers of compounds interacting with a therapeutic or ADMET related protein and those not interacting with that protein.
MLDB: Macromolecule ligand database
2010, Journal of Applied CrystallographyData mining and machine learning models for predicting drug likeness and their disease or organ category
2018, Frontiers in ChemistryZika virus serine protease complex (NS2B-NS3) inhibition by 2-amino-5-{[(1Z) -amino ({[(z) -benzoyl] imino}) methyl] amino} -n-(5-amino-7-{[carbamoyl (phenyl) methyl] amino} -6-oxoheptyl) pentanamide, in silico studies
2017, Asian Journal of Pharmaceutical and Clinical Research