The transport pathway in the ABCG2 protein and its regulation revealed by molecular dynamics simulations

Magnolol derivatives as specific and noncytotoxic inhibitors of breast cancer resistance protein (BCRP/ABCG2)

The breast cancer resistance protein (BCRP/ABCG2) transporter mediates the efflux of numerous antineoplastic drugs, playing a central role in multidrug resistance related to cancer. The absence of successful clinical trials using specific ABCG2 inhibitors reveals the urge to identify new compounds to attend this critical demand. In this work, a series of 13 magnolol derivatives was tested as ABCG2 inhibitors. Only two compounds, derivatives 10 and 11, showed partial and complete ABCG2 inhibitory effect, respectively. This inhibition was selective toward ABCG2, since none of the 13 compounds inhibited neither P-glycoprotein nor MRP1. Both inhibitors (10 and 11) were not transported by ABCG2 and demonstrated a low cytotoxic profile even at high concentrations (up to 100 µM). 11 emerged as the most promising compound of the series, considering the ratio between cytotoxicity (IG50) and ABCG2 inhibition potency (IC50), showing a therapeutic ratio (TR) higher than observed for 10 (10.5 versus 1.6, respectively). This derivative showed a substrate-independent and a mixed type of inhibition. The effect of compound 11 on the ABCG2 ATPase activity and thermostability revealed allosteric protein changes. This compound did not affect the expression levels of ABCG2 and increased the binding of the conformational-sensitive antibody 5D3. A docking study showed that 11 did not share the same binding site with ABCG2 substrate mitoxantrone. Finally, 11 could revert the chemoresistance to SN-38 mediated by ABCG2.

Membrane-assisted tariquidar access and binding mechanisms of human ATP-binding cassette transporter P-glycoprotein

The human multidrug transporter P-glycoprotein (P-gp) is physiologically essential and of key relevance to biomedicine. Recent structural studies have shed light on the mode of inhibition of the third-generation inhibitors for human P-gp, but the molecular mechanism by which these inhibitors enter the transmembrane sites remains poorly understood. In this study, we utilized all-atom molecular dynamics (MD) simulations to characterize human P-gp dynamics under a potent inhibitor, tariquidar, bound condition, as well as the atomic-level binding pathways in an explicit membrane/water environment. Extensive unbiased simulations show that human P-gp remains relatively stable in tariquidar-free and bound states, while exhibiting a high dynamic binding mode at either the drug-binding pocket or the regulatory site. Free energy estimations by partial nudged elastic band (PNEB) simulations and Molecular Mechanics Generalized Born Surface Area (MM/GBSA) method identify two energetically favorable binding pathways originating from the cytoplasmic gate with an extended tariquidar conformation. Interestingly, free tariquidar in the lipid membrane predominantly adopts extended conformations similar to those observed at the regulatory site. These results suggest that membrane lipids may preconfigure tariquidar into an active ligand conformation for efficient binding to the regulatory site. However, due to its conformational plasticity, tariquidar ultimately moves toward the drug-binding pocket in both pathways, explaining how it acts as a substrate at low concentrations. Our molecular findings propose a membrane-assisted mechanism for the access and binding of the third-generation inhibitors to the binding sites of human P-gp, and offer deeper insights into the molecule design of more potent inhibitors against P-gp-mediated drug resistance.

Computational and artificial intelligence-based approaches for drug metabolism and transport prediction

Drug metabolism and transport, orchestrated by drug-metabolizing enzymes (DMEs) and drug transporters (DTs), are implicated in drug-drug interactions (DDIs) and adverse drug reactions (ADRs). Reliable and precise predictions of DDIs and ADRs are critical in the early stages of drug development to reduce the rate of drug candidate failure. A variety of experimental and computational technologies have been developed to predict DDIs and ADRs. Recent artificial intelligence (AI) approaches offer new opportunities for better predicting and understanding the complex processes related to drug metabolism and transport. We summarize the role of major DMEs and DTs, and provide an overview of current progress in computational approaches for the prediction of drug metabolism, transport, and DDIs, with an emphasis on AI including machine learning (ML) and deep learning (DL) modeling.

Structural and molecular characterization of lopinavir and ivermectin as breast cancer resistance protein (BCRP/ABCG2) inhibitors

A current clinical challenge in cancer is multidrug resistance (MDR) mediated by ABC transporters. Breast cancer resistance protein (BCRP) or ABCG2 transporter is one of the most important ABC transporters implicated in MDR and the use of inhibitors is a promising approach to overcome the resistance in cancer. This study aimed to characterize the molecular mechanism of ABCG2 inhibitors identified by a repurposing drug strategy using antiviral, anti-inflammatory and antiparasitic agents. Lopinavir and ivermectin can be considered as pan-inhibitors of ABC transporters, since both compounds inhibited ABCG2, P-glycoprotein and MRP1. They inhibited ABCG2 activity showing IC50 values of 25.5 and 23.4 µM, respectively. These drugs were highly cytotoxic and not transported by ABCG2. Additionally, these drugs increased the 5D3 antibody binding and did not affect the mRNA and protein expression levels. Cell-based analysis of the type of inhibition suggested a non-competitive inhibition, which was further corroborated by in silico approaches of molecular docking and molecular dynamics simulations. These results showed an overlap of the lopinavir and ivermectin binding sites on ABCG2, mainly interacting with E446 residue. However, the substrate mitoxantrone occupies a different site, binding to the F436 region, closer to the L554/L555 plug. In conclusion, these results revealed the mechanistic basis of lopinavir and ivermectin interaction with ABCG2. See also the Graphical abstract(Fig. 1).

Restriction of access to the central cavity is a major contributor to substrate selectivity in plant ABCG transporters

ABCG46 of the legume Medicago truncatula is an ABC-type transporter responsible for highly selective translocation of the phenylpropanoids, 4-coumarate, and liquiritigenin, over the plasma membrane. To investigate molecular determinants of the observed substrate selectivity, we applied a combination of phylogenetic and biochemical analyses, AlphaFold2 structure prediction, molecular dynamics simulations, and mutagenesis. We discovered an unusually narrow transient access path to the central cavity of MtABCG46 that constitutes an initial filter responsible for the selective translocation of phenylpropanoids through a lipid bilayer. Furthermore, we identified remote residue F562 as pivotal for maintaining the stability of this filter. The determination of individual amino acids that impact the selective transport of specialized metabolites may provide new opportunities associated with ABCGs being of interest, in many biological scenarios.

Differential dynamics and direct interaction of bound ligands with lipids in multidrug transporter ABCG2

ABCG2 is an ATP-binding cassette (ABC) transporter that extrudes a wide range of xenobiotics and drugs from the cell and contributes to multidrug resistance in cancer cells. Following our recent structural characterization of topotecan-bound ABCG2, here, we present cryo-EM structures of ABCG2 under turnover conditions in complex with a special modulator and slow substrate, tariquidar, in nanodiscs. The structures reveal that similar to topotecan, tariquidar induces two distinct ABCG2 conformations under turnover conditions (turnover-1 and turnover-2). μs-scale molecular dynamics simulations of drug-bound and apo ABCG2 in native-like lipid bilayers, in both topotecan- and tariquidar-bound states, characterize the ligand size as a major determinant of its binding stability. The simulations highlight direct lipid-drug interactions for the smaller topotecan, which exhibits a highly dynamic binding mode. In contrast, the larger tariquidar occupies most of the available volume in the binding pocket, thus leaving little space for lipids to enter the cavity and interact with it. Similarly, when simulating ABCG2 in the apo inward-open state, we also observe spontaneous penetration of phospholipids into the binding cavity. The captured phospholipid diffusion pathway into ABCG2 offers a putative general path to recruit any hydrophobic/amphiphilic substrates directly from the membrane. Our simulations also reveal that ABCG2 rejects cholesterol as a substrate, which is omnipresent in plasma membranes that contain ABCG2. At the same time, cholesterol is found to prohibit the penetration of phospholipids into ABCG2. These molecular findings have direct functional ramifications on ABCG2's function as a transporter.

Two Important Anticancer Mechanisms of Natural and Synthetic Chalcones

ATP-binding cassette subfamily G and tubulin pharmacological mechanisms decrease the effectiveness of anticancer drugs by modulating drug absorption and by creating tubulin assembly through polymerization. A series of natural and synthetic chalcones have been reported to have very good anticancer activity, with a half-maximal inhibitory concentration lower than 1 µM. By modulation, it is observed in case of the first mechanism that methoxy substituents on the aromatic cycle of acetophenone residue and substitution of phenyl nucleus by a heterocycle and by methoxy or hydroxyl groups have a positive impact. To inhibit tubulin, compounds bind to colchicine binding site. Presence of methoxy groups, amino groups or heterocyclic substituents increase activity.

Protein Design: From the Aspect of Water Solubility and Stability

Water solubility and structural stability are key merits for proteins defined by the primary sequence and 3D-conformation. Their manipulation represents important aspects of the protein design field that relies on the accurate placement of amino acids and molecular interactions, guided by underlying physiochemical principles. Emulated designer proteins with well-defined properties both fuel the knowledge-base for more precise computational design models and are used in various biomedical and nanotechnological applications. The continuous developments in protein science, increasing computing power, new algorithms, and characterization techniques provide sophisticated toolkits for solubility design beyond guess work. In this review, we summarize recent advances in the protein design field with respect to water solubility and structural stability. After introducing fundamental design rules, we discuss the transmembrane protein solubilization and de novo transmembrane protein design. Traditional strategies to enhance protein solubility and structural stability are introduced. The designs of stable protein complexes and high-order assemblies are covered. Computational methodologies behind these endeavors, including structure prediction programs, machine learning algorithms, and specialty software dedicated to the evaluation of protein solubility and aggregation, are discussed. The findings and opportunities for Cryo-EM are presented. This review provides an overview of significant progress and prospects in accurate protein design for solubility and stability.

Structural analysis of cholesterol binding and sterol selectivity by ABCG5/G8

The ATP-binding cassette (ABC) sterol transporters are responsible for maintaining cholesterol homeostasis in mammals by participating in reverse cholesterol transport (RCT) or transintestinal cholesterol efflux (TICE). The heterodimeric ABCG5/G8 carries out selective sterol excretion, preventing the abnormal accumulation of plant sterols in human bodies, while homodimeric ABCG1 contributes to the biogenesis and metabolism of high-density lipoproteins. A sterol-binding site on ABCG5/G8 was proposed at the interface of the transmembrane domain and the core of lipid bilayers. In this study, we have determined the crystal structure of ABCG5/G8 in a cholesterol-bound state. The structure combined with amino acid sequence analysis shows that in the proximity of the sterol-binding site, a highly conserved phenylalanine array supports functional implications for ABCG cholesterol/sterol transporters. Lastly, in silico docking analysis of cholesterol and stigmasterol (a plant sterol) suggests sterol-binding selectivity on ABCG5/G8, but not ABCG1. Together, our results provide a structural basis for cholesterol binding on ABCG5/G8 and the sterol selectivity by ABCG transporters.

Comprehensive Collection and Prediction of ABC Transmembrane Protein Structures in the AI Era of Structural Biology

The number of unique transmembrane (TM) protein structures doubled in the last four years, which can be attributed to the revolution of cryo-electron microscopy. In addition, AlphaFold2 (AF2) also provided a large number of predicted structures with high quality. However, if a specific protein family is the subject of a study, collecting the structures of the family members is highly challenging in spite of existing general and protein domain-specific databases. Here, we demonstrate this and assess the applicability and usability of automatic collection and presentation of protein structures via the ABC protein superfamily. Our pipeline identifies and classifies transmembrane ABC protein structures using the PFAM search and also aims to determine their conformational states based on special geometric measures, conftors. Since the AlphaFold database contains structure predictions only for single polypeptide chains, we performed AF2-Multimer predictions for human ABC half transporters functioning as dimers. Our AF2 predictions warn of possibly ambiguous interpretation of some biochemical data regarding interaction partners and call for further experiments and experimental structure determination. We made our predicted ABC protein structures available through a web application, and we joined the 3D-Beacons Network to reach the broader scientific community through platforms such as PDBe-KB.

ABCG2/BCRP transport mechanism revealed through kinetically excited targeted molecular dynamics simulations

ABCG2/BCRP is an ABC transporter that plays an important role in tissue protection by exporting endogenous substrates and xenobiotics. ABCG2 is of major interest due to its involvement in multidrug resistance (MDR), and understanding its complex efflux mechanism is essential to preventing MDR and drug-drug interactions (DDI). ABCG2 export is characterized by two major conformational transitions between inward- and outward-facing states, the structures of which have been resolved. Yet, the entire transport cycle has not been characterized to date. Our study bridges the gap between the two extreme conformations by studying connecting pathways. We developed an innovative approach to enhance molecular dynamics simulations, ‘kinetically excited targeted molecular dynamics’, and successfully simulated the transitions between inward- and outward-facing states in both directions and the transport of the endogenous substrate estrone 3-sulfate. We discovered an additional pocket between the two substrate-binding cavities and found that the presence of the substrate in the first cavity is essential to couple the movements between the nucleotide-binding and transmembrane domains. Our study shed new light on the complex efflux mechanism, and we provided transition pathways that can help to identify novel substrates and inhibitors of ABCG2 and probe new drug candidates for MDR and DDI.

Ins and outs of AlphaFold2 transmembrane protein structure predictions

Transmembrane (TM) proteins are major drug targets, but their structure determination, a prerequisite for rational drug design, remains challenging. Recently, the DeepMind's AlphaFold2 machine learning method greatly expanded the structural coverage of sequences with high accuracy. Since the employed algorithm did not take specific properties of TM proteins into account, the reliability of the generated TM structures should be assessed. Therefore, we quantitatively investigated the quality of structures at genome scales, at the level of ABC protein superfamily folds and for specific membrane proteins (e.g. dimer modeling and stability in molecular dynamics simulations). We tested template-free structure prediction with a challenging TM CASP14 target and several TM protein structures published after AlphaFold2 training. Our results suggest that AlphaFold2 performs well in the case of TM proteins and its neural network is not overfitted. We conclude that cautious applications of AlphaFold2 structural models will advance TM protein-associated studies at an unexpected level.