MolMeDB: Molecules on Membranes Database
Jazyk angličtina Země Anglie, Velká Británie Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
31250015
PubMed Central
PMC6597476
DOI
10.1093/database/baz078
PII: 5523873
Knihovny.cz E-zdroje
- MeSH
- chemické databáze * MeSH
- lidé MeSH
- membrány MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Biological membranes act as barriers or reservoirs for many compounds within the human body. As such, they play an important role in pharmacokinetics and pharmacodynamics of drugs and other molecular species. Until now, most membrane/drug interactions have been inferred from simple partitioning between octanol and water phases. However, the observed variability in membrane composition and among compounds themselves stretches beyond such simplification as there are multiple drug-membrane interactions. Numerous experimental and theoretical approaches are used to determine the molecule-membrane interactions with variable accuracy, but there is no open resource for their critical comparison. For this reason, we have built Molecules on Membranes Database (MolMeDB), which gathers data about over 3600 compound-membrane interactions including partitioning, penetration and positioning. The data have been collected from scientific articles published in peer-reviewed journals and complemented by in-house calculations from high-throughput COSMOmic approach to set up a baseline for further comparison. The data in MolMeDB are fully searchable and browsable by means of name, SMILES, membrane, method or dataset and we offer the collected data openly for further reuse and we are open to further additions. MolMeDB can be a powerful tool that could help researchers better understand the role of membranes and to compare individual approaches used for the study of molecule/membrane interactions.
Zobrazit více v PubMed
Shevchenko A. and Simons K. (2010) Lipidomics: coming to grips with lipid diversity. Nat. Rev. Mol. Cell Biol., 11, 593–598. PubMed
Lomize M.A., Pogozheva I.D., Joo H. et al. (2012) OPM database and PPM web server: resources for positioning of proteins in membranes. Nucleic Acids Res., 40, D370–D376. PubMed PMC
Kozma D., Simon I. and Tusnády E.E. (2012) PDBTM: protein data Bank of transmembrane proteins after 8 years. Nucleic Acids Res., 41, D524–D529. PubMed PMC
Stansfeld P.J., Goose J.E., Caffrey M. et al. (2015) MemProtMD: automated insertion of membrane protein structures into explicit lipid membranes. Structure, 23, 1350–1361. PubMed PMC
Postic G., Ghouzam Y., Etchebest C. et al. (2017) TMPL: a database of experimental and theoretical transmembrane protein models positioned in the lipid bilayer. Database, 2017, 1–7. PubMed PMC
Sarti E., Aleksandrova A.A., Ganta S.K. et al. (2019) EncoMPASS: an online database for analyzing structure and symmetry in membrane proteins. Nucleic Acids Res., 47, D315–D321. PubMed PMC
Wishart D.S., Feunang Y.D., Guo A.C. et al. (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res., 46, D1074–D1082. PubMed PMC
Williams F.M. (2004) EDETOX. Evaluations and predictions of dermal absorption of toxic chemicals. Int. Arch. Occup. Environ. Health, 77, 150–151. PubMed
Chen L., Lian G. and Han L. (2010) Modeling transdermal permeation. Part I. predicting skin permeability of both hydrophobic and hydrophilic solutes. AIChE J., 56, 1136–1146.
Lomize A.L. and Pogozheva I.D. (2017) Prediction of passive membrane permeability and translocation pathways of biologically active molecules. Biophys. J., 112, 525a.
Paloncýová M., Fabre G., DeVane R.H. et al. (2014) Benchmarking of force fields for molecule–membrane interactions. J. Chem. Theory Comput., 10, 4143–4151. PubMed
Tse C.H., Comer J., Wang Y. et al. (2018) Link between membrane composition and permeability to drugs. J. Chem. Theory Comput., 14, 2895–2909. PubMed
Menichetti R., Kanekal K.H., Kremer K. et al. (2017) In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force. J. Chem. Phys., 147, 125101. PubMed
Klamt A., Huniar U., Spycher S. et al. (2008) COSMOmic: a mechanistic approach to the calculation of membrane−water partition coefficients and internal distributions within membranes and micelles. J. Phys. Chem. B, 112, 12148–12157. PubMed
Bennion B.J., Be N.A., McNerney M.W. et al. (2017) Predicting a drug’s membrane permeability: a computational model validated with in vitro permeability assay data. J. Phys. Chem. B, 121, 5228–5237. PubMed
Carpenter T.S., Kirshner D.A., Lau E.Y. et al. (2014) A method to predict blood-brain barrier permeability of drug-like compounds using molecular dynamics simulations. Biophys. J., 107, 630–641. PubMed PMC
Di L., Kerns E.H., Fan K. et al. (2003) High throughput artificial membrane permeability assay for blood–brain barrier. Eur. J. Med. Chem., 38, 223–232. PubMed
Dickson C.J., Hornak V., Pearlstein R.A. et al. (2017) Structure–kinetic relationships of passive membrane permeation from multiscale modeling. J. Am. Chem. Soc., 139, 442–452. PubMed
Endo S., Escher B.I. and Goss K.-U. (2011) Capacities of membrane lipids to accumulate neutral organic chemicals. Environ. Sci. Technol., 45, 5912–5921. PubMed
Eyer K., Paech F., Schuler F. et al. (2014) A liposomal fluorescence assay to study permeation kinetics of drug-like weak bases across the lipid bilayer. J. Control. Release, 173, 102–109. PubMed
Lundborg M., Wennberg C.L., Narangifard A. et al. (2018) Predicting drug permeability through skin using molecular dynamics simulation. J. Control. Release, 283, 269–279. PubMed
Yazdanian M., Glynn S.L., Wright J.L. et al. (1998) Correlating partitioning and caco-2 cell permeability of structurally diverse small molecular weight compounds. Pharm. Res., 15, 1490–1494. PubMed
Swift R.V. and Amaro R.E. (2013) Back to the future: can physical models of passive membrane permeability help reduce drug candidate attrition and move us beyond QSPR? Chem. Biol. Drug Des., 81, 61–71. PubMed PMC
White S.H. (1972) Analysis of the torus surrounding planar lipid bilayer membranes. Biophys. J., 12, 432–445. PubMed PMC
Avdeef A., Artursson P., Neuhoff S. et al. (2005) Caco-2 permeability of weakly basic drugs predicted with the double-sink PAMPA method. Eur. J. Pharm. Sci., 24, 333–349. PubMed
Gobas F.A., Lahittete J.M., Garofalo G. et al. (1988) A novel method for measuring membrane-water partition coefficients of hydrophobic organic chemicals: comparison with 1-octanol-water partitioning. J. Pharm. Sci., 77, 265–272. PubMed
Kwon J.-H., Wuethrich T., Mayer P. et al. (2009) Development of a dynamic delivery method for in vitro bioassays. Chemosphere, 76, 83–90. PubMed
Banks J.L., Beard H.S., Cao Y. et al. (2005) Integrated modeling program, applied chemical theory (IMPACT). J. Comput. Chem., 26, 1752–1780. PubMed PMC
Řezáč J. (2016) Cuby: an integrative framework for computational chemistry. J. Comput. Chem., 37, 1230–1237. PubMed
Klamt A. and Diedenhofen M. (2018) A refined cavity construction algorithm for the conductor-like screening model. J. Comput. Chem., 39, 1648–1655. PubMed
Paloncýová M., DeVane R.H., Murch B.P. et al. (2014) Rationalization of reduced penetration of drugs through ceramide gel phase membrane. Langmuir, 30, 13942–13948. PubMed
Paloncýová M., Vávrová K., Sovová Ž. et al. (2015) Structural changes in ceramide bilayers rationalize increased permeation through stratum corneum models with shorter acyl tails. J. Phys. Chem. B, 119, 9811–9819. PubMed
Steinbeck C., Han Y., Kuhn S. et al. (2003) The chemistry development kit (CDK): an open-source java library for chemo- and bioinformatics. J. Chem. Inf. Comput. Sci., 43, 493–500. PubMed PMC
Sehnal D., Deshpande M., Vařeková R.S. et al. (2017) LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data. Nat. Methods, 14, 1121–1122. PubMed
Kim S., Thiessen P.A., Bolton E.E. et al. (2016) PubChem substance and compound databases. Nucleic Acids Res., 44, D1202–D1213. PubMed PMC
Berman H.M., Westbrook J., Feng Z. et al. (2000) The protein data Bank. Nucleic Acids Res., 28, 235–242. PubMed PMC
Hastings J., Owen G., Dekker A. et al. (2016) ChEBI in 2016: improved services and an expanding collection of metabolites. Nucleic Acids Res., 44, D1214–D1219. PubMed PMC
Gaulton A., Hersey A., Nowotka M. et al. (2017) The ChEMBL database in 2017. Nucleic Acids Res., 45, D945–D954. PubMed PMC
Arnaud M.J. (2011) Pharmacokinetics and metabolism of natural methylxanthines in animal and man. Handb. Exp. Pharmacol., 33–91. PubMed
Paloncýová M., Berka K. and Otyepka M. (2013) Molecular insight into affinities of drugs and their metabolites to lipid bilayers. J. Phys. Chem. B, 117, 2403–2410. PubMed
Meta-Analysis of Permeability Literature Data Shows Possibilities and Limitations of Popular Methods