Creation of a free, Internet-accessible database: the Multiple Target Ligand Database
© Chen et al.; licensee Springer. 2016
Received: 28 May 2014
Accepted: 30 March 2015
Published: 15 April 2015
Polypharmacology plays an important part in drug discovery, and remains a major challenge in drug development. Identification of the underlying polypharmacology of a drug, as well as development of polypharmacological drugs, have become important issues in the pharmaceutical industry and academia.
Herein, through data mining of the Protein Data Bank (PDB), a free, Internet-accessible database called the Multiple Target Ligand Database (MTLD; www.mtdcadd.com) was constructed. The MTLD contains 1,732 multiple-target ligands (MTLs) which bind to 14,996 binding sites extracted from 12,759 PDB structures. Among MTLs, 222 entries are approved drugs and 1,334 entries are drug-like compounds. The MTLD could be an extremely useful tool in the development of polypharmacological drugs. It also sheds light on the side effects of drugs through anticipation of their multiple functions and similarities in the binding sites of multiple targets. The entire database is free for online searching, browsing, and downloading.
As a crucial expansion of the PDB, increasing numbers of MTLs will be included in the MTLD. Eventually, it will become an efficient platform to obtain useful information on MTLs and their underlying polypharmacology.
KeywordsPolypharmacology Multiple-target ligands Database Drug discovery
“Polypharmacology” (also termed “drug promiscuity”) refers to the action of a single drug on multiple targets through a single pathway or multiple pathways. This phenomenon has been regarded to be the main cause for the severe adverse effects or toxicities of several drugs approved since the 1990s [1-3]. Based on the exponential growth of molecular data and rapid advances in drug development, evidence suggests that polypharmacology is also important for drug efficacy. For instance, clozapine is the “gold standard” anti-psychotic drug exhibiting beneficial effects via complicated interactions with multiple target networks . Several highly efficacious drugs such as salicylate, metformin, or imatinib exhibit enhanced therapeutic efficacy through interactions with multiple targets simultaneously.
In general, it is accepted that the activity towards a single target is not sufficient for a complex disease involving multiple pathogenic factors (e.g., cancer, diabetes mellitus, neurodegenerative syndrome, cardiovascular diseases). Importantly, some of the undesired side effects are due to drugs not hitting their targets, which can confer potential repurposing opportunities for these drugs and provide novel strategies in drug design.
Taken together, polypharmacology plays an important part in drug discovery and remains one of the major challenges in drug development. It opens avenues for rational design of new agents that are more efficient and less toxic than their predecessors [5-10]. Drug discovery using a polypharmacology approach has become a hot topic in the pharmaceutical industry and in academia [5-7,11].
There are hundreds of publicly available databases on drug discovery Protein Data Bank (PDB) , DrugBank , Kyoto Encyclopedia of Genes and Genomes (KEGG) , ZINC , Chemical database of European Molecular Biology Laboratory , and Therapeutic Target Database . Such databases are key resources that integrate diverse information such as molecular pathways, crystal structures, binding experiments, side effects, and drug targets. Such information is also very useful in prospective drug design using a polypharmacology approach. However, finding information on polypharmacological agents is difficult because of the swathes of information contained in such databases. Thus, development of a novel data-mining method archiving polypharmacological information is needed.
The PDB is a repository of detailed three-dimensional (3D) structural information of proteins and other molecules, including information on the binding between ligands and proteins. It is extremely helpful in the elucidation of ligand promiscuity. Recently, based on the information obtained from the PDB, evidence indicates that similarities in binding sites among multiple proteins and the molecular complexity of a ligand could be reasons for the polypharmacology of drugs [18,19]. As a result, several datasets of multiple-target ligands (MTLs) derived from the PDB have been built by comparing the similarities of binding sites (e.g., Kahraman, Extended Kahraman, Huang) [20,21]. However, the overall entries of MTLs in these datasets are ≤100. Through analyses of ligand promiscuity based on the PDB, an additional two datasets have been generated, containing 164 and 247 entries, respectively [18,19]. However, these datasets do not include all of the potential MTLs in the PDB, and the information in these datasets is not sufficient. Hence, a database containing all of the potential MTLs in the PDB is needed.
Herein, a database termed the Multiple Target Ligand Database (MTLD, www.mtdcadd.com) based on 3D structural data extracted from the PDB has been constructed. The MTLD contains all of the ligands binding to MTLs and sheds light on the side effects through anticipation of their multiple functions as well as the similarities in the binding site of multiple targets. The entire database is free for online searching, browsing, and downloading. Collectively, the MTLD is extremely useful for the development of polypharmacological drugs, and provides various potential candidates for further optimization.
Construction and content
Atomic structures with a resolution of ≥3.0 Å could be inferred. Hence, X-ray protein structures with a resolution <3.0 Å (67,793 entries) from the PDB were selected for the extraction of ligands and their binding sites. To avoid selection of solvent molecules, ligands containing >8 heavy atoms were extracted from selected PDB files. As a result, 62,423 ligand coordinate files were obtained. Binding sites were defined as all of the protein residues within a radius of 6.0 Å of each atom in binding ligands. Binding sites with >5 residues were outputted, and 54,936 binding sites were extracted, which could bind with 12,138 ligands. Among these ligands, 3,371 ligands were found to bind to more than one PDB structure, and these ligands were chosen for the next filtration. To remove the redundancy of crystal structural entries bound to the same ligand, the sequence identity between protein pairs was restricted to <35%. Eventually, 1,732 MTLs were extracted from the PDB and archived in the MTLD.
Each ligand entry contains five pieces of information. First, the 3D structures of the ligand extracted directly from the known crystal structures are provided. Second, the two-dimensional structure of the ligand that had been converted into “SMILES” format is given. Third, the structures of the binding site that were outputted according to the coordinates of the ligand are detailed. Fourth, the original crystal structures from the PDB to which the ligand binds is given. Last, information on the sequence of the involved proteins downloaded from the PDB or Universal Protein Resource (UniProt; www.uniprot.org) is provided.
Comparison of the MTLD with other datasets of multiple-target ligands
PDB MTL I
PDB MTL II
Statistical analyses for the MTLD
Among 1,732 MTLs, ≈45.9% ligands (795 entries) were bound to two distinct proteins (Figure 2E), which was lower than the result (65%) reported by Noé Sturm et al. . This result was probably caused by using a different source of datasets when they adopted the sc-PDB (a database derived from the PDB). Notably, 222 ligands were bound to >10 proteins, including approved drugs such as isotretinoin, spermidine, and salicylic acid. The promiscuity of a ligand is related to its conformational flexibility . Hence, analyses of the conformational complexity of the extracted PDB structures for each ligand were conducted through structural alignment using Multiscale Modeling Tools for Structural Biology (Scripps Research Institute, San Diego, CA, USA). The root-mean-square deviation (RMSD) of structure pairs was calculated after the alignment. The maximal RMSD value of the structure pairs was taken to be a criterion of conformational change. Computed RMSD values of most MTLs (1,270 entries, ≈73.3%) were <2.0 Å (Figure 2F), indicating that most of the MTLs could bind to different proteins by adopting a similar conformation. However, further comprehensive analyses are needed to identify the effects of other parameters such as the: molecular size and flexibility of MTLs; number of potential targets; mode of interaction between MTLs and targets.
Internet interface of the MTLD
The MTLD is an easily usable and fully searchable database with many built-in tools. On the MTLD homepage and “About” webpage, a brief introduction of the MTLD is given. The “Download” webpage provides the download option for all data, including approved drugs, KEGG ligands, and some kinase inhibitors. All can be downloaded conveniently. On the “Statistics” webpage, the results of statistical analyses are provided (as mentioned above) and more statistical results will be revealed on this page in the future.
Multiple targets of selected drugs and natural products in the MTLD
Mast/stem cell growth factor (3G0E)
Phosphorylase b kinase gamma (2Y7J)
Cyclin-dependent kinase 2 (3TI1)
Tyrosine-protein kinase ITK/TSK (3MIY)
Homo sapiens v-kit Hardy–Zuckerman (1 T46)
Ribosyldihydronicotinamide dehydrogenase (3FW1)
Tyrosine-protein kinase SYK (1XBB)
Mitogen-activated protein kinase 14 (3HEC)
B-Raf proto-oncogene serine/threonine-protein (1UWH)
Vascular endothelial growth factor (4ASD)
Cyclin-dependent kinase 8 (3RGF)
Mitogen-activated protein kinase 14 (3GCS)
Sex hormone-binding globulin (1D2S)
Estradiol 17-beta-dehydrogenase 1 (3KLM)
Androgen receptor (1I37)
Short chain 3-hydroxyacyl-CoA dehydrogenase (1E6W)
Sex hormone-binding globulin (1LHU)
Estrogen sulfotransferase (1AQU)
Estrogen receptor beta (2J7X)
Thyroid receptor alpha (THRA) protein (2H79)
Androgen receptor (2PIV)
Proliferating cell nuclear antigen (3VKX)
Sulfotransferase family cytosolic 1B (3CKL)
Myosin-2 heavy chain (3MNQ)
Leukotriene A-4 hydrolase (3FTS)
NRH dehydrogenase [quinone] 2 (1SG0)
Protein (chalcone synthase) (1CGZ)
Methionine adenosyltransferase 2 subunit (2YDX)
Epigallocatechin gallate (EGCG; KDH)
Polymerase subunit PA (4AWM)
Peptidyl-prolyl cis-trans isomerase NIMA-inte (3OOB)
Apolipoprotein A-I-binding protein
Peroxisome proliferator-activated receptor ga
Amine oxidase [flavin-containing] b
A crystal structure with a bound ligand does not necessarily mean firm binding. The binding affinity or binding energy of the ligand are important parameters to show if the interaction between the ligand and protein is specific, which can help to judge the “true” target of the ligand. Thus, a link to BindingDB was added on the ligand webpage, and one can find the reported data on binding affinity on the linking page. Moreover, most data on the binding affinity of complexes were not available. Hence, evaluation of the binding free energy using the X-Score method according to the complex coordinate  was undertaken, and the value of the binding free energy of the complex shown on the webpage. Furthermore, the crystal structures of some target classes (e.g., kinases) can be obtained much more readily than those of other target classes (e.g., G protein-coupled receptors). In such cases, a bias may be added into the MTLD because the target will not be shown in the database if a crystal structure for that target has not been solved. Further enhancement is needed to try to include such targets into the MTLD.
Most of the entries in the MTLD are based on drugs. Hence, the MTLD should be useful for developing polypharmacological agents, and may provide potential candidates for further optimization. For example, estrogen receptor-alpha is a drug target for treating breast cancer, and 17-hydroxysteroid dehydrogenase (17HSD1) is a putative target for endocrine therapy of hormone-dependent breast cancer . By searching the MTLD, a ligand, estrogen (ligand-ID: EST) binding to 17HSD1 (PDB-ID: 1FDS) and estrogen receptor (PDB-ID: 3Q95) was found. Thus, polypharmacological drugs that act on both targets could be designed based on the structure of estrogen. Conversely, software called Binding Site Match Maker was developed, which could align binding sites according to their similar physiochemical properties and generate common sites. Binding Site Match Maker could be another option for the design of polypharmacological drugs.
The MTLD can shed light on the multiple mechanisms of action of drugs or natural products. For example, imatinib is an efficacious drug for the treatment of chronic myeloid leukaemia (CML). Imatinib prevents Bcr-Abl protein from exerting its actions in the oncogenic pathway in CML . By searching the MTLD, apart from Bcr-Abl, imatinib was found to bind to mitogen-activated protein kinase 14, ribosyldihydronicotinamide dehydrogenase, tyrosine-protein kinase Syk, and c-Kit kinase. The natural product resveratrol (which is present in red wine) exhibits considerable chemical diversity and biological activities . In the MTLD, resveratrol was found to bind with seven targets. More examples of drugs and natural products are listed in Table 2.
Information obtained from the MTLD can be used to address the mechanism of action of the adverse side effects of drugs. For example, the methylxanthine derivative theophylline (used to treat the symptoms of reversible airflow obstruction) can cause headaches, agitation, and other adverse neuronal side effects. Upon searching the MTLD, pyridoxal kinase was found to be one of the targets of theophylline, which could be a possible underlying mechanism of the neurotoxic effects of theophylline . Likewise, pioglitazone (used for treating type-2 diabetes mellitus) exhibits neuronal side effects, including central nervous system (CNS) depression. Upon searching the MTLD, it was found that pioglitazone can bind to monoamine oxidase B, which may be responsible for CNS depression .
Similarities in the binding site have important roles in polypharmacology . Several methodologies have been developed to evaluate similarities in binding sites, such as SiteComp  and MultiBind . These methodologies provide useful tools to predict the binding-site similarity of proteins but because of their incompleteness, need to be improved . In such circumstances, the MTLD could provide various binding sites as the training sets or testing sets for the development of novel methodologies for comparisons of binding sites.
Here we describe development of a comprehensive, Internet-accessible database called the MTLD based on datasets extracted from the PDB. To date, the MTLD comprises 1,732 MTLs that bind to 14,996 binding sites extracted from 12,759 PDB structures. In the MTLD, the 222 entries are approved drugs and 1,334 entries are drug-like compounds. Thus, the MTLD could be extremely helpful for developing polypharmacological drugs and could provide potential candidates for further optimization. Moreover, the MTLD may shed light on the: side effects of drugs; multiple functions of small biological molecules; similarities in binding site of target proteins. As a crucial expansion of the PDB, increasing numbers of MTLs will be included in the MTLD, which will become an efficient platform to obtain useful information on MTLs.
Availability and requirements
MTLD is freely accessible at http://www.mtdcadd.com. The data in MTLD is free for search, download, and further analysis.
Protein Data Bank
Multiple Target Ligands
Multiple Target Ligand Database
Kyoto Encyclopedia of Genes and Genomes
Therapeutic target database
Root mean square deviation
Multiscale modeling tools for structural biology
Estrogen receptor α
17-hydroxysteroid dehydrogenase type 1
We thank Mr. Ruiming Li at Institute of Medicinal Biotechnology, Chinese Academy of Medical Science, Beijing, China for the kindly helps at the construction of the website. This work was in part supported by the Nature Science Foundation of China (81311120299, 81271844) and Fundamental Research Funds for the Central Universities of China (3142014100).
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