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.