Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures
Language English Country Switzerland Media electronic
Document type Journal Article
Grant support
MSMT-5727/2018-2
Ministerstvo Školství, Mládeže a Tělovýchovy
14.587.21.0049
Ministry of Education and Science of the Russian Federation
PubMed
30486389
PubMed Central
PMC6321403
DOI
10.3390/molecules23123094
PII: molecules23123094
Knihovny.cz E-resources
- Keywords
- 3D pharmacophore hash, 3D pharmacophore signatures, ligand-based modeling, pharmacophore modeling,
- MeSH
- Adenosine A2 Receptor Antagonists chemistry MeSH
- Cholinesterase Inhibitors chemistry MeSH
- Cytochrome P-450 CYP3A Inhibitors chemistry MeSH
- Ligands MeSH
- Models, Molecular * MeSH
- Publication type
- Journal Article MeSH
- Names of Substances
- Adenosine A2 Receptor Antagonists MeSH
- Cholinesterase Inhibitors MeSH
- Cytochrome P-450 CYP3A Inhibitors MeSH
- Ligands MeSH
Pharmacophore modeling is a widely used strategy for finding new hit molecules. Since not all protein targets have available 3D structures, ligand-based approaches are still useful. Currently, there are just a few free ligand-based pharmacophore modeling tools, and these have a lot of restrictions, e.g., using a template molecule for alignment. We developed a new approach to 3D pharmacophore representation and matching which does not require pharmacophore alignment. This representation can be used to quickly find identical pharmacophores in a given set. Based on this representation, a 3D pharmacophore ligand-based modeling approach to search for pharmacophores which preferably match active compounds and do not match inactive ones was developed. The approach searches for 3D pharmacophore models starting from 2D structures of available active and inactive compounds. The implemented approach was successfully applied for several retrospective studies. The results were compared to a 2D similarity search, demonstrating some of the advantages of the developed 3D pharmacophore models. Also, the generated 3D pharmacophore models were able to match the 3D poses of known ligands from their protein-ligand complexes, confirming the validity of the models. The developed approach is available as an open-source software tool: http://www.qsar4u.com/pages/pmapper.php and https://github.com/meddwl/psearch.
A M Butlerov Institute of Chemistry Kazan Federal University Kremlevskaya Str 18 420008 Kazan Russia
See more in PubMed
Schuster D., Nashev L.G., Kirchmair J., Laggner C., Wolber G., Langer T., Odermatt A. Discovery of Nonsteroidal 17β-Hydroxysteroid Dehydrogenase 1 Inhibitors by Pharmacophore-Based Screening of Virtual Compound Libraries. J. Med. Chem. 2008;51:4188–4199. doi: 10.1021/jm800054h. PubMed DOI
Hinsberger S., Hüsecken K., Groh M., Negri M., Haupenthal J., Hartmann R.W. Discovery of Novel Bacterial RNA Polymerase Inhibitors: Pharmacophore-Based Virtual Screening and Hit Optimization. J. Med. Chem. 2013;56:8332–8338. doi: 10.1021/jm400485e. PubMed DOI
Krautscheid Y., Senning C.J.Å., Sartori S.B., Singewald N., Schuster D., Stuppner H. Pharmacophore Modeling, Virtual Screening, and in Vitro Testing Reveal Haloperidol, Eprazinone, and Fenbutrazate as Neurokinin Receptors Ligands. J. Chem. INF. 2014;54:1747–1757. doi: 10.1021/ci500106z. PubMed DOI
Polishchuk P.G., Samoylenko G.V., Khristova T.M., Krysko O.L., Kabanova T.A., Kabanov V.M., Kornylov A.Y., Klimchuk O., Langer T., Andronati S.A., et al. Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents. J. Med. Chem. 2015;58:7681–7694. doi: 10.1021/acs.jmedchem.5b00865. PubMed DOI
Vuorinen A., Schuster D. Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling. Methods. 2015;71:113–134. doi: 10.1016/j.ymeth.2014.10.013. PubMed DOI
Jones G. GAPE: An Improved Genetic Algorithm for Pharmacophore Elucidation. J. Chem. INF. 2010;50:2001–2018. doi: 10.1021/ci100194k. PubMed DOI
Korb O., Monecke P., Hessler G., Stützle T., Exner T.E. pharmACOphore: Multiple Flexible Ligand Alignment Based on Ant Colony Optimization. J. Chem. INF. 2010;50:1669–1681. doi: 10.1021/ci1000218. PubMed DOI
Patel Y., Gillet V.J., Bravi G., Leach A.R. A comparison of the pharmacophore identification programs: Catalyst, DISCO and GASP. J Comput. Aid. Mol. Des. 2002;16:653–681. doi: 10.1023/A:1021954728347. PubMed DOI
Martin Y.C., Bures M.G., Danaher E.A., DeLazzer J., Lico I., Pavlik P.A. A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists. J Comput. Aid. Mol. Des. 1993;7:83–102. doi: 10.1007/BF00141577. PubMed DOI
Wolber G., Dornhofer A.A., Langer T. Efficient overlay of small organic molecules using 3D pharmacophores. J. Comput. Aid. Mol. Des. 2006;20:773–788. doi: 10.1007/s10822-006-9078-7. PubMed DOI
Richmond N.J., Abrams C.A., Wolohan P.R.N., Abrahamian E., Willett P., Clark R.D. GALAHAD: 1. Pharmacophore identification by hypermolecular alignment of ligands in 3D. J Comput. Aid. Mol. Des. 2006;20:567–587. doi: 10.1007/s10822-006-9082-y. PubMed DOI
Schneidman-Duhovny D., Dror O., Inbar Y., Nussinov R., Wolfson H.J. PharmaGist: A webserver for ligand-based pharmacophore detection. Nucleic Acids Res. 2008;36:W223–W228. doi: 10.1093/nar/gkn187. PubMed DOI PMC
Schreyer A.M., Blundell T. USRCAT: Real-time ultrafast shape recognition with pharmacophoric constraints. J. Cheminformatics. 2012;4:27. doi: 10.1186/1758-2946-4-27. PubMed DOI PMC
Koes D.R., Camacho C.J. Pharmer: Efficient and Exact Pharmacophore Search. J. Chem. INF. 2011;51:1307–1314. doi: 10.1021/ci200097m. PubMed DOI PMC
Morgan H.L. The Generation of a Unique Machine Description for Chemical Structures-A Technique Developed at Chemical Abstracts Service. J. Chem. Documentation. 1965;5:107–113. doi: 10.1021/c160017a018. DOI
Butina D. Unsupervised Data Base Clustering Based on Daylight’s Fingerprint and Tanimoto Similarity: A Fast and Automated Way To Cluster Small and Large Data Sets. J. Chem. Inf. Comput. Sci. 1999;39:747–750. doi: 10.1021/ci9803381. DOI
Halgren T.A. Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94. J. Comput. Chem. 1996;17:490–519. doi: 10.1002/(SICI)1096-987X(199604)17:5/6<490::AID-JCC1>3.0.CO;2-P. DOI
Benchmarks for interpretation of QSAR models
Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores
Virtual Screening Using Pharmacophore Models Retrieved from Molecular Dynamic Simulations