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

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.Wehave 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 PepT so, identifies Glu 56 and Arg 305 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

Molecular basis of drug-P-glycoprotein interaction

Der membranständige ABC-Transporter P-glycoprotein (P-gp) ist für den Export einer großen Anzahl von Substanzklassen aus der Zelle verantwortlich. Einerseits wird ihm dadurch eine wichtige Schutz- und Entgiftungsfunktion zugeschrieben, andererseits wird P-gp aus diesem Grund sowohl mit veränderten ADMET Eigenschaften, als auch mit unerwünschten Arzneistoffwechselwirkungen und der Resistenzentwicklung von Tumoren und Krankheitserregern in Zusammenhang gebracht. Aus diesem Grund ist die frühzeitige Erkennung potentieller P-gp Substrate und Inhibitoren mittels verlässlicher in silico Methoden von großer Bedeutung in der Wirkstoffentwicklung. In diesem Sinne, werden in dieser Dissertation drei Studien präsentiert, die die erfolgreiche Anwendung struktur-basierter in silico Modelle zur Vorhersage von P-gp-Wirkstoff-Interaktionen darstellen. In zwei Studien wurde Protein-Ligand Docking dazu verwendet, um die Bindungsmodi für zwei Klassen von P-gp-Inhibitoren zu identifizieren. Der Augenmerk lag hierbei auf der Implementierung von externen Informationen, die die Verwendung umstrittener Fitnessfunktionen limitiert. Die daraus gewonnenen Resultate konnten erfolgreich für ein virtuelles Screening für neue P-gp Inhibitoren weiterverwendet werden. Außerdem wurden in einer dritten Studie potentielle Methoden zur Klassifizierung von P-gp Inhibitoren untersucht. Dabei wurden liganden- und struktur-basierte Techniken gegenübergestellt. Obwohl sich herausstellte, dass Erstere in puncto Treffsicherheit deutlich überlegen waren, konnten Letztere durch Einbeziehung von Ligandeninformation die Vorhersagekraft verbessern. Fitnessfunktionen, die Scoring- und Ligandendeskriptorwerte kombinieren, zeigen daher großes Potential.The ABC transporter P-glycoprotein (P-gp) is responsible for the translocation of a broad variety of different substances across the lipid bilayer. As a result, P-gp on the one hand exhibits important barrier and detoxifying functions, on the other hand it affects the ADMET properties of drugs, triggers unwanted drug-drug interactions and its overexpression is responsible for the development of multidrug-resistance (MDR), one major reason for the failure of antibiotic or anticancer therapies. Thus, reliable in silico methods for the early identification of P-gp modulators or virtual screening approaches for discovering new inhibitors is of high interest in drug discovery. This thesis outlines in three independent studies how structure-based methods can be used for tackling the problems triggered by P-gp. Although structure-based design has to be performed with precaution when dealing with membrane proteins, it is highly necessary for understanding intermolecular interactions between the ligand and its target protein. Two studies are presented, that describe the use of docking for the identification of binding modes of two classes of P-gp inhibitors. The workflows applied show how the implemention of external information is able to reduce the debatable use of scoring functions to a minimum. The results obtained provided useful information for screening for new P-gp inhibitors. Furthermore, a third study that compares the classification performance of ligand-based and structure-based in silico methods is also included in this thesis. Although the former models tend to be more accurate, the implementation of some ligand information could improve the structure-based models, emphasizing the importance of combined scoring functions (merging scoring and descriptor values) for future studies

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

Targeting a cell state common to triple‐negative breast cancers

International audienceSome mutations in cancer cells can be exploited for therapeutic intervention. However, for many cancer subtypes, including triple-negative breast cancer (TNBC), no frequently recurring aberrations could be identified to make such an approach clinically feasible. Characterized by a highly heterogeneous mutational landscape with few common features, many TNBCs cluster together based on their 'basal-like' transcriptional profiles. We therefore hypothesized that targeting TNBC cells on a systems level by exploiting the transcriptional cell state might be a viable strategy to find novel therapies for this highly aggressive disease. We performed a large-scale chemical genetic screen and identified a group of compounds related to the drug PKC412 (midostaurin). PKC412 induced apoptosis in a subset of TNBC cells enriched for the basal-like subtype and inhibited tumor growth in vivo. We employed a multi-omics approach and computational modeling to address the mechanism of action and identified spleen tyrosine kinase (SYK) as a novel and unexpected target in TNBC. Quantitative phosphoproteomics revealed that SYK inhibition abrogates signaling to STAT3, explaining the selectivity for basal-like breast cancer cells. This non-oncogene addiction suggests that chemical SYK inhibition may be beneficial for a specific subset of TNBC patients and demonstrates that targeting cell states could be a viable strategy to discover novel treatment strategies

MTHFD1 interaction with BRD4 links folate metabolism to transcriptional regulation

The histone acetyl reader bromodomain-containing protein 4 (BRD4) is an important regulator of chromatin structure and transcription, yet factors modulating its activity have remained elusive. Here we describe two complementary screens for genetic and physical interactors of BRD4, which converge on the folate pathway enzyme MTHFD1 (methylenetetrahydrofolate dehydrogenase, cyclohydrolase and formyltetrahydrofolate synthetase 1). We show that a fraction of MTHFD1 resides in the nucleus, where it is recruited to distinct genomic loci by direct interaction with BRD4. Inhibition of either BRD4 or MTHFD1 results in similar changes in nuclear metabolite composition and gene expression; pharmacological inhibitors of the two pathways synergize to impair cancer cell viability in vitro and in vivo. Our finding that MTHFD1 and other metabolic enzymes are chromatin associated suggests a direct role for nuclear metabolism in the control of gene expression

A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor

International audienceApproved drugs are invaluable tools to study biochemical pathways, and further characterization of these compounds may lead to repurposing of single drugs or combinations. Here we describe a collection of 308 small molecules representing the diversity of structures and molecular targets of all FDA-approved chemical entities. The CeMM Library of Unique Drugs (CLOUD) covers prodrugs and active forms at pharmacologically relevant concentrations and is ideally suited for combinatorial studies. We screened pairwise combinations of CLOUD drugs for impairment of cancer cell viability and discovered a synergistic interaction between flutamide and phenprocoumon (PPC). The combination of these drugs modulates the stability of the androgen receptor (AR) and resensitizes AR-mutant prostate cancer cells to flutamide. Mechanistically, we show that the AR is a substrate for γ-carboxylation, a post-translational modification inhibited by PPC. Collectively, our data suggest that PPC could be repurposed to tackle resistance to antiandrogens in prostate cancer patients