NLDB About

About

NLDB (capitals denote Natural Ligand DataBase; URL: http://nldb.hgc.jp) is a database of automatically corrected and predicted 3D protein-ligand interactions in the enzymatic reactions of various metabolic pathways. Information about such non-covalent interactions is important, not only for studying the molecular functions of specific proteins but also for enzyme-targeted drug discovery, and thus it will be valuable to complement the structural information about the reactions obtained by computational approaches. Therefore, we predict 3D protein-ligand interactions using reliable, state-of-the-art software programs if their complex structures are unknown, and then construct a database of the 3D interactions in various enzymatic reactions.

NLDB produces three different types of data resources, natural, analog, and ab initio complex structures. The natural complexes are experimentally determined protein-ligand complex structures in the PDB, the analog complexes are predicted based on known protein structures in a complex with a similar ligand (analog) by transforming a target ligand to the analog using the fkcombu program in KCOMBU [1], and the ab initio complexes are predicted by docking a ligand to predicted or high confidence ligand-binding sites of a protein using AutoDock VINA [2]. In addition, the ligand-binding sites of protein are predicted using BUMBLE [3] and the high confidential binding sites of a protein are obtained by mapping ligand-binding sites among homologous proteins. Furthermore, the database has a flexible search function, based on various types of relevant keywords, and an enrichment analysis function based on a set of KEGG compound IDs.

NLDB will be a valuable resource for experimental biologists studying protein-ligand interactions in specific chemical reactions, and for theoretical researchers wishing to undertake more precise simulations of interactions for drug discovery purposes. NLDB is freely accessible at http://nldb.hgc.jp, and will be regularly updated every three months

Reference:
  1. Kawabata T. and Nakamura H., (2014), 3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity, J.Chem.Infor.Model, vol.54, pp.1850-1868.
  2. Trott O. and Olson A J., (2010), AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, J.Comp.Chem, vol.31, pp.455-461.
  3. Kasahara K., Kinoshita K. and Takagi T., (2010), Ligand-binding site prediction of proteins based on known fragment-fragment interactions, Bioinformatics, vol.15, issue.12, pp.1493-1499.