Freya Klepsch

Exhaustive sampling of docking poses reveals binding hypotheses for propafenone type inhibitors of P-glycoprotein.

Overexpression of the xenotoxin transporter P-glycoprotein (P-gp) represents one major reason for the development of multidrug resistance (MDR), leading to the failure of antibiotic and cancer therapies. Inhibitors of P-gp have thus been advocated as promising candidates for overcoming the problem of MDR. However, due to lack of a high-resolution structure the concrete mode of interaction of both substrates and inhibitors is still not known. Therefore, structure-based design studies have to rely on protein homology models. In order to identify binding hypotheses for propafenone-type P-gp inhibitors, five different propafenone derivatives with known structure-activity relationship (SAR) pattern were docked into homology models of the apo and the nucleotide-bound conformation of the transporter. To circumvent the uncertainty of scoring functions, we exhaustively sampled the pose space and analyzed the poses by combining information retrieved from SAR studies with common scaffold clustering. The results suggest propafenone binding at the transmembrane helices 5, 6, 7 and 8 in both models, with the amino acid residue Y307 playing a crucial role. The identified binding site in the non-energized state is overlapping with, but not identical to, known binding areas of cyclic P-gp inhibitors and verapamil. These findings support the idea of several small binding sites forming one large binding cavity. Furthermore, the binding hypotheses for both catalytic states were analyzed and showed only small differences in their protein-ligand interaction fingerprints, which indicates only small movements of the ligand during the catalytic cycle.

Ligand and structure-based classification models for prediction of P-glycoprotein inhibitors.

The ABC transporter P-glycoprotein (P-gp) actively transports a wide range of drugs and toxins out of cells, and is therefore related to multidrug resistance and the ADME profile of therapeutics. Thus, development of predictive in silico models for the identification of P-gp inhibitors is of great interest in the field of drug discovery and development. So far in silico P-gp inhibitor prediction was dominated by ligand-based approaches because of the lack of high-quality structural information about P-gp. The present study aims at comparing the P-gp inhibitor/noninhibitor classification performance obtained by docking into a homology model of P-gp, to supervised machine learning methods, such as Kappa nearest neighbor, support vector machine (SVM), random fores,t and binary QSAR, by using a large, structurally diverse data set. In addition, the applicability domain of the models was assessed using an algorithm based on Euclidean distance. Results show that random forest and SVM performed best for classification of P-gp inhibitors and noninhibitors, correctly predicting 73/75% of the external test set compounds. Classification based on the docking experiments using the scoring function ChemScore resulted in the correct prediction of 61% of the external test set. This demonstrates that ligand-based models currently remain the methods of choice for accurately predicting P-gp inhibitors. However, structure-based classification offers information about possible drug/protein interactions, which helps in understanding the molecular basis of ligand-transporter interaction and could therefore also support lead optimization.

Impact of the Recent Mouse P-Glycoprotein Structure for Structure-Based Ligand Design

P‐Glycoprotein (P‐gp), a transmembrane, ATP‐dependent drug efflux transporter, has attracted considerable interest both with respect to its role in tumour cell multidrug resistance and in absorption‐distribution and elimination of drugs. Although known since more than 30 years, the understanding of the molecular basis of drug/transporter interaction is still limited, which is mainly due to the lack of structural information available. However, within the past decade X‐ray structures of several bacterial homologues as well as very recently also of mouse P‐gp have become available. Within this review we give an overview on the current status of structural information available and on its impact for structure‐based drug design.

Pharmacoinformatic approaches to design natural product type ligands of ABC-transporters.

ABC-transporter have been recognized as being responsible for multiple drug resistance in tumor therapy, for decreased brain uptake and low oral bioavailability of drug candidates, and for drug-drug interactions and drug induced cholestasis. P-glycoprotein (ABCB1), the paradigm protein in the field, is mainly effluxing natural product toxins and shows very broad substrate specificity. Within this article we will highlight SAR and QSAR approaches for designing natural product type inhibitors of ABCB1 and related proteins as well as in silico strategies to predict ABCB1 substrates and inhibitors in order to design out undesirable drug/protein interaction.

Random Mutagenesis of the Prokaryotic Peptide Transporter YdgR Identifies Potential Periplasmic Gating Residues

The peptide transporter (PTR) family represents a group of proton-coupled secondary transporters responsible for bulk uptake of amino acids in the form of di- and tripeptides, an essential process employed across species ranging from bacteria to humans. To identify amino acids critical for peptide transport in a prokaryotic PTR member, we have screened a library of mutants of the Escherichia coli peptide transporter YdgR using a high-throughput substrate uptake assay. We have identified 35 single point mutations that result in a full or partial loss of transport activity. Additional analysis, including homology modeling based on the crystal structure of the Shewanella oneidensis peptide transporter PepTso, identifies Glu56 and Arg305 as potential periplasmic gating residues. In addition to providing new insights into transport by members of the PTR family, these mutants provide valuable tools for further study of the mechanism of peptide transport.

Using Structural and Mechanistic Information to Design Novel Inhibitors/Substrates of P-Glycoprotein

Design of inhibitors of P-glycoprotein still represents a challenging task for medicinal chemists. The polyspecificity of the transporter combined with the limited structural information renders rational drug design approaches rather ineffective. Within this article we will exemplify how recent insights into structure and mechanism of P-glycoprotein may aid in design of potent inhibitors.