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Metligdb: a Web-Based Database for the Identification of Chemical Groups

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Metligdb: a Web-Based Database for the Identification of Chemical Groups computer programs Journal of Applied MetLigDB: a web-based database for the Crystallography identification of chemical groups to design ISSN 0021-8898 metalloprotein inhibitors Received 12 April 2011 Accepted 10 June 2011 Hwanho Choi,‡ Hongsuk Kang‡ and Hwangseo Park* Department of Bioscience and Biotechnology, Sejong University, 98 Kunja-dong, Kwangjin-ku, Seoul 143-747, Korea. Correspondence e-mail: [email protected] MetLigDB (http://silver.sejong.ac.kr/MetLigDB) is a publicly accessible web- based database through which the interactions between a variety of chelating groups and various central metal ions in the active site of metalloproteins can be explored in detail. Additional information can also be retrieved, including protein and inhibitor names, the amino acid residues coordinated to the central metal ion, and the binding affinity of the inhibitor for the target metalloprotein. Although many metalloproteins have been considered promising targets for drug discovery, it is difficult to discover new inhibitors because of the difficulty in designing a suitable chelating moiety to impair the catalytic activity of the central metal ion. Because both common and specific chelating groups can be identified for varying metal ions and the associated coordination environments, # 2011 International Union of Crystallography MetLigDB is expected to give users insight into designing new inhibitors of Printed in Singapore – all rights reserved metalloproteins for drug discovery. 1. Introduction 2008), the coordination geometry around the metal ion (Castagnetto The importance of metal ions in biological systems is increasingly et al., 2002; Andreini, Bertini, Cavallaro, Holliday & Thornton, 2009), drawing attention, as indicated by the recent emergence of terms such catalytic mechanisms (Andreini, Bertini, Cavallaro, Najmanovich & as metallome and metallomics (Shi & Chance, 2008). This is not Thornton, 2009) and interactions in protein structures (Hemavathi et surprising because about 40% of proteins require metal ions in their al., 2010). Therefore, we have constructed MetLigDB to provide active sites for biological activity (Andreini et al., 2008). Throughout further insight when designing new inhibitors of metalloproteins. To this paper we will use the term ‘active site’ for all kinds of proteins, this end, users are enabled to examine the interactions between a although it is normally referred to enzymes. Most metalloproteins known inhibitor and the active-site metal ion cluster of a metallo- play a key role in biological processes and therefore have been protein of interest in the Protein Data Bank (PDB; Berman et al., considered to be promising targets for drug discovery. Metal ions in 2000). This allows the identification of the chemical moieties appro- the active site can participate in the enzymatic reaction as a receptor priate for the chelative inactivation of a specific metal ion with a given for lone-pair electrons, which has the effect of weakening the coordination environment in the metalloprotein. The coordination chemical bonds in the substrate (Armstrong, 2000). Metal ions may patterns of a variety of chemical moieties can be found and visualized also play the role of a Lewis acid catalyst, stabilizing the transition in MetLigDB. In order for the relative binding affinities of the inhi- state or an intermediate in which the negative charge is developed as bitors to be estimated, their Ki or IC50 (binding affinity and 50% a consequence of chemical reaction (Kleifeld et al., 2003). The central inhibitory concentration) values against known metalloproteins are metal ion is thus indispensable for the biological activity of metallo- also provided. This paper summarizes the data content and the basic proteins. technical aspects employed in the construction of MetLigDB. In order for a small-molecule inhibitor to regulate the activity of a metalloprotein, the inhibitor must contain a chemical group that can bind to the central metal ion. In the case of virtual screening for 2. Methodology inhibitors, the covalent-like interactions between metal centers and their ligands, coupled with their large electrostatic potentials, make MetLigDB is a publicly accessible web-based database designed by metalloproteins a challenge for many docking scoring functions taking advantage of well known web technologies such as a stable (Irwin et al., 2005). Despite such difficulties, a number of chemical web server (Apache), a fast database engine (MySQL) and a groups that chelate and inactivate metal ions at the active site of powerful web scripting language (PHP). By virtue of these open- metalloproteins have been reported, including carboxylate, phos- source tools, we have been able to construct interactive web query phorate, hydroxamate, -keto acid and diol moieties. interfaces and functions to allow remote viewing and searching. Although some databases and web servers for metalloproteins Structural information about the metalloprotein–inhibitor interac- have already been reported, all the biochemical information has been tions was collected from the PDB, which contains a total of 71 635 limited to the identification of metal-binding residues (Hsin et al., experimentally determined structures made public before 8 March 2011. In this study, the metalloprotein and the inhibitor refer to a protein that has at least one metal ion in its physiological site and a ‡ These authors contributed equally to this work. small molecule that has been shown to impair the biological activity 878 doi:10.1107/S0021889811022503 J. Appl. Cryst. (2011). 44, 878–881 computer programs of a protein, respectively. Starting from these structures, the data Table 1 content of MetLigDB for metalloprotein–inhibitor interaction was Number of metal ions in the simplified model for metalloprotein–inhibitor complexes in MetLigDB. obtained in a few steps of data filtration and simplification, as depicted in Fig. 1. The first step involved the identification of the Metal ion Number of protein–ligand complexes Number of metalloproteins structures in the PDB that contain at least one metal ion and Zn2+ 436 142 simultaneously a small-molecule inhibitor in the active site. Among Mn2+ 188 86 2+ 3+ the 3045 structures that satisfied this criterion, those in which the Fe /Fe 61 28 Ni2+ 21 8 central metal ion forms at least one coordination bond with the Mg2+ 14 12 inhibitor were selected for further processing. As the distance limit Cu2+ 88 ˚ Co2+ 22 for defining a coordination bond, we used R0 + 0.5 A as suggested by Mo2+ 21 Shi et al. (2007), where R0 represents the average interatomic distance Total 732 287 between the central metal ion and a ligand atom as given in the PDB. The structural data file in PDB format was then prepared to show the detailed coordination pattern by selecting the central metal ion, the With respect to the ligand-binding affinities, data from multiple small-molecule inhibitor, and the amino acid residues and the research groups were collected to obtain the average values of Ki or structural water molecules coordinated to the metal ion. The other IC50 of the inhibitor for the target protein. The journals from which parts of the original PDB file of the protein–inhibitor complex were the binding affinity data were drawn include Journal of Medicinal removed for simplicity and visual clarity. On the basis of this proce- Chemistry, Journal of Biological Chemistry, Bioorganic and Medic- dure, 732 simplified structural models for metalloprotein–inhibitor inal Chemistry Letters, and Biochemistry. Despite an extensive search complexes were obtained for 250 proteins and eight types of metal for binding affinity data, Ki or IC50 values for only 41% of the ion. metalloprotein–inhibitor complexes contained in MetLigDB were available in the literature. This is in contrast to the case of nonmetal enzymes, for which the binding affinity data can be found for most of the inhibitors present in the PDB (Liu et al., 2007). The majority of the binding affinity data in MetLigDB were obtained from enzyme inhibition studies, while a small portion of data was obtained from the more informative methods such as isothermal titration calorimetry (Pierce et al., 1999). 3. Contents of database The web page of MetLigDB is organized in a hierarchical fashion. As can be seen in Fig. 2, users can select the kind of metal ion or protein on the menu bar. In MetLigDB, the numbers of metalloproteins that belong to the categories oxidoreductase, transferase, hydrolase, lyase, isomerase and ligase are 87, 108, 306, 110, 27 and 8, respectively. There are also 86 metalloproteins in the database whose enzyme Figure 1 The flowchart used for the identification of a metalloprotein–inhibitor complex commission (EC) numbers have not been assigned. Each entry involving a direct metal–ligand coordination in the PDB. contains the PDB code, the protein name, the EC number, the protein classification, the kind of metal ion in the active site, the IUPAC name of the small-molecule inhibitor, the binding affi- nity and the binding mode of the inhibitor to the active-site metal ion cluster. The coordination pattern for a chelating group of the inhibitor with the central metal ion can be displayed with Jmol (http:// www.jmol.org), an open-source Java viewer for chemical structures in three dimensions. Users may be enabled to detect a chemical moiety suitable for the inactivation of the catalytic action of the metalloprotein of interest by visual inspection of the simpli- fied structural model for metalloprotein– inhibitor complexes. Table 1 lists the number of entries in MetLigDB for a given metal ion. The zinc– ligand complexes appear to be predomi- nant, occupying 59.6% of the total data Figure 2 A screenshot of the main window of MetLigDB. In total, seven interface windows are presented to users, each of content. Such an abundance of zinc metallo- which can be shown by clicking on the tabs at the top of the main window. proteins is consistent with the fact that zinc J. Appl. Cryst. (2011). 44, 878–881 Choi, Kang and Park MetLigDB 879 computer programs Table 2 Representative ligand moieties for the central metal ions in metalloproteins.
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