Computational models for prediction of interactions with ABC-transporters.
The polyspecific ligand recognition pattern of ATB-binding cassette (ABC)-transporters, combined with the limited knowledge on the molecular basis of their multispecificity, makes it difficult to apply traditional molecular modelling and quantitative structure-activity relationships (QSAR) methods for identification of new ligands. Recent advances relied mainly on pharmacophore modelling and machine learning methods. Structure-based design studies suffer from the lack of available protein structures at atomic resolution. The recently published protein homology models of P-glycoprotein structure, based on the high-resolution structure of the bacterial ABC-transporter of Sav1866, may open a new chapter for structure-based studies. Last, but not least, molecular dynamics simulations have already proved their high potential for structure-function modelling of ABC-transporter. Because of the recognition of several ABC-transporters as antitargets, algorithms for predicting substrate properties are of increasing interest.
Ecker
Gerhard F
GF
Emerging Field Pharmacoinformatics, Department of Medicinal Chemistry, University of Vienna, Althanstrasse 14, 1090 Wien, Austria. gerhard.f.ecker@univie.ac.at
Stockner
Thomas
T
Chiba
Peter
P
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1 / Q000737:chemistry; D018528:ATP-Binding Cassette Transporters / Q000737:chemistry* / Q000378:metabolism; D019295:Computational Biology; D015195:Drug Design; D006801:Humans; D008024:Ligands; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism; D011487:Protein Conformation; D021281:Quantitative Structure-Activity Relationship
D020168:ATP Binding Cassette Transporter, Subfamily B, Member 1; D018528:ATP-Binding Cassette Transporters; D008024:Ligands; D004364:Pharmaceutical Preparations