Computational models for prediction of interactions with ABC-transporters

Drug Discov Today. 2008 Apr;13(7-8):311-7. doi: 10.1016/j.drudis.2007.12.012. Epub 2008 Mar 14.


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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • ATP Binding Cassette Transporter, Subfamily B, Member 1 / chemistry
  • ATP-Binding Cassette Transporters / chemistry*
  • ATP-Binding Cassette Transporters / metabolism
  • Computational Biology
  • Drug Design*
  • Humans
  • Ligands
  • Models, Molecular*
  • Pharmaceutical Preparations / chemistry*
  • Pharmaceutical Preparations / metabolism
  • Protein Conformation
  • Quantitative Structure-Activity Relationship


  • ATP Binding Cassette Transporter, Subfamily B, Member 1
  • ATP-Binding Cassette Transporters
  • Ligands
  • Pharmaceutical Preparations