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.