The reliability of molecular dynamics simulations of the multidrug transporter P-glycoprotein in a membrane environment

Role of CSF1R 550th-tryptophan in kusunokinin and CSF1R inhibitor binding and ligand-induced structural effect

Binding affinity is an important factor in drug design to improve drug-target selectivity and specificity. In this study, in silico techniques based on molecular docking followed by molecular dynamics (MD) simulations were utilized to identify the key residue(s) for CSF1R binding affinity among 14 pan-tyrosine kinase inhibitors and 15 CSF1R-specific inhibitors. We found tryptophan at position 550 (W550) on the CSF1R binding site interacted with the inhibitors' aromatic ring in a π-π way that made the ligands better at binding. Upon W550-Alanine substitution (W550A), the binding affinity of trans-(-)-kusunokinin and imatinib to CSF1R was significantly decreased. However, in terms of structural features, W550 did not significantly affect overall CSF1R structure, but provided destabilizing effect upon mutation. The W550A also did not either cause ligand to change its binding site or conformational changes due to ligand binding. As a result of our findings, the π-π interaction with W550's aromatic ring could be still the choice for increasing binding affinity to CSF1R. Nevertheless, our study showed that the increasing binding to W550 of the design ligand may not ensure CSF1R specificity and inhibition since W550-ligand bound state did not induce significantly conformational change into inactive state.

A new horizon in the phosphorylated sites of AGA: the structural impact of C163S mutation in aspartylglucosaminuria through molecular dynamics simulation

Aspartylglucosaminuria (AGU) is a lysosomal storage disorder caused by insufficient aspartylglucosaminidase (AGA) activity leading to chronic neurodegeneration. We utilized the PhosphoSitePlus tool to identify the AGA protein's phosphorylation sites. The phosphorylation was induced on the specific residue of the three-dimensional AGA protein, and the structural changes upon phosphorylation were studied via molecular dynamics simulation. Furthermore, the structural behaviour of C163S mutation and C163S mutation with adjacent phosphorylation was investigated. We have examined the structural impact of phosphorylated forms and C163S mutation in AGA. Molecular dynamics simulations (200 ns) exposed patterns of deviation, fluctuation, and change in compactness of Y178 phosphorylated AGA protein (Y178-p), T215 phosphorylated AGA protein (T215-p), T324 phosphorylated AGA protein (T324-p), C163S mutant AGA protein (C163S), and C163S mutation with Y178 phosphorylated AGA protein (C163S-Y178-p). Y178-p, T215-p, and C163S mutation demonstrated an increase in intramolecular hydrogen bonds, leading to greater compactness of the AGA forms. Principle component analysis (PCA) and Gibbs free energy of the phosphorylated/C163S mutation structures exhibit transition in motion/orientation than Wild type (WT). T215-p may be more dominant among these than the other studied phosphorylated forms. It might contribute to hydrolyzing L-asparagine functioning as an asparaginase, thereby regulating neurotransmitter activity. This study revealed structural insights into the phosphorylation of Y178, T215, and T324 in AGA protein. Additionally, it exposed the structural changes of the C163S mutation and C163S-Y178-p of AGA protein. This research will shed light on a better understanding of AGA's phosphorylated mechanism.Communicated by Ramaswamy H. Sarma.

A chirality-sensitive approach to predict chemical transfer across the human placental barrier

The placenta is a membrane that separates the fetus from the maternal circulation, and in addition to protecting the fetus, plays a key role in fetal growth and development. With increasing drug use in pregnancy, it is imperative that reliable models of estimating placental permeability and safety be established. In vitro methods and animal models such as rodent placenta are limited in application since the species-specific nature of the placenta prevents meaningful extrapolations to humans. In this regard, in silico approaches such as quantitative structure-property relationships (QSPRs) are useful alternatives. However, despite evidence that drug transport across the placenta is stereoselective (i.e., governed by the spatial arrangement of the atoms in a molecule), many QSPR models for placental transfer have been built using 2D descriptors that do not account for chirality and stereochemistry. In this study, we apply a chirality-sensitive and proven QSPR methodology titled "EigenValue ANalySis" (EVANS) to build QSPR models for placental transfer. We deploy EVANS along with robust machine learning algorithms to build (i) regression models on a dataset of environmental chemicals (dataset PD I) followed by (ii) classification models on a set of drug-like compounds (dataset PD II). The best models were found to achieve state-of-the-art performance, with the support vector machine algorithm returning rtrain2=0.85,rtest2=0.75 for PD I, and the logistic regression algorithm giving accuracy 0.88 and F1 score 0.93 for PD II. The best models were interpreted with the Shapley Additive Explanations paradigm, and it was found that autocorrelation descriptors are crucial for modelling placental permeability. In conclusion, we demonstrate the need of a chirality-sensitive approach for modelling placental transfer of chemicals, and present two predictive QSPR models that may reliably be used for prediction of placental transfer.

Exploring the dynamics of the ABCB1 membrane transporter P-glycoprotein in the presence of ATP and active/non-active compounds through molecular dynamics simulations

P-glycoprotein (Pgp) is a member of the ATP-binding cassette family of transporters that confers multidrug resistance to cancer cells and is actively involved in the pharmacokinetics and toxicokinetics of a big variety of drugs. Extensive studies have provided insights into the binding of many compounds, but the precise mechanism of translocation across the membrane remains unknown; in this context, the major challenge has been to understand the basis for its polyspecificity. In this study, molecular dynamics (MD) simulations of human P-gp (hP-gp) in an explicit membrane-and-water environment were performed to investigate the dynamic behavior of the transporter in the presence of different compounds (active and inactive) in the binding pocket and ATP molecules within the nucleotide binding domains (NBDs). The complexes studied involve four compounds: cyclosporin A (CSA), amiodarone (AMI), pamidronate (APD), and valproic acid (VPA). While CSA and AMI are known to interact with P-gp, APD and VPA do not. The results highlighted how the presence of ATP notably contributed to increased flexibility of key residues in NBD1 of active systems, indicating potential conformational changes activating the translocation mechanism. MD simulations reveal how these domains adapt and respond to the presence of different substrates, as well as the influence of ATP binding on their flexibility. Furthermore, distinctive behavior was observed in the presence of active and inactive compounds, particularly in the arrangement of ATP between NBDs, supporting the proposed nucleotide sandwich dimer mechanism for ATP binding. This study provides comprehensive insights into P-gp behavior with various ligands and ATP, offering implications for drug development, toxicity assessment and demonstrating the validity of the results derived from the MD simulations.

Conformational dynamics of α-1 acid glycoprotein (AGP) in cancer: A comparative study of glycosylated and unglycosylated AGP

α-1 acid glycoprotein (AGP) is one of the most abundant plasma proteins. It fulfills two important functions: immunomodulation, and binding to various drugs and receptors. These different functions are closely associated and modulated via changes in glycosylation and cancer missense mutations. From a structural point of view, glycans alter the local biophysical properties of the protein leading to a diverse ligand-binding spectrum. However, glycans can typically not be observed in the resolved X-ray crystallography structure of AGP due to their high flexibility and microheterogeneity, so limiting our understanding of AGP's conformational dynamics 70 years after its discovery. We here investigate how mutations and glycosylation interfere with AGP's conformational dynamics changing its biophysical behavior, by using molecular dynamics (MD) simulations and sequence-based dynamics predictions. The MD trajectories show that glycosylation decreases the local backbone flexibility of AGP and increases the flexibility of distant regions through allosteric effects. We observe that mutations near the glycosylation site affect glycan's conformational preferences. Thus, we conclude that mutations control glycan dynamics which modulates the protein's backbone flexibility directly affecting its accessibility. These findings may assist in the drug design targeting AGP's glycosylation and mutations in cancer.

Ligand- and Structure-based Approaches for Transmembrane Transporter Modeling

The study of transporter proteins is key to understanding the mechanism behind multidrug resistance and drug-drug interactions causing severe side effects. While ATP-binding transporters are well-studied, solute carriers illustrate an understudied family with a high number of orphan proteins. To study these transporters, in silico methods can be used to shed light on the basic molecular machinery by studying protein-ligand interactions. Nowadays, computational methods are an integral part of the drug discovery and development process. In this short review, computational approaches, such as machine learning, are discussed, which try to tackle interactions between transport proteins and certain compounds to locate target proteins. Furthermore, a few cases of selected members of the ATP binding transporter and solute carrier family are covered, which are of high interest in clinical drug interaction studies, especially for regulatory agencies. The strengths and limitations of ligand-based and structure-based methods are discussed to highlight their applicability for different studies. Furthermore, the combination of multiple approaches can improve the information obtained to find crucial amino acids that explain important interactions of protein-ligand complexes in more detail. This allows the design of drug candidates with increased activity towards a target protein, which further helps to support future synthetic efforts.

Modeling Substrate Entry into the P-Glycoprotein Efflux Pump at the Blood-Brain Barrier

We report molecular dynamics simulations of rhodamine entry into the central binding cavity of P-gp in the inward open conformation. Rhodamine can enter the inner volume via passive transport across the luminal membrane or lateral diffusion in the lipid bilayer. Entry into the inner volume is determined by the aperture angle at the apex of the protein, with a critical angle of 27° for rhodamine. The central binding cavity has an aqueous phase with a few lipids, which significantly reduces substrate diffusion. Within the central binding cavity, we identified regions with relatively weak binding, suggesting that the combination of reduced mobility and weak substrate binding confines rhodamine to enable the completion of the efflux cycle. Tariquidar, a P-gp inhibitor, aggregates at the lower arms of the P-gp, suggesting that inhibition involves steric hindrance of entry into the inner volume and/or steric hindrance of access of ATP to the nucleotide-binding domains.

An Improved Technique for Genotyping the ABCB1 Gene Variant of Exon 21

The Multidrug Resistance protein (ABCB1, MDR1) is involved in the transport of xenobiotics and antiretroviral drugs. Some variants of the ABCB1 gene are of clinical importance; among them, exon 12 (c.1236C>T, rs1128503), 21 (c.2677G>T/A, rs2032582), and 26 (c.3435C>T, rs1045642) have a high incidence in Caucasians. Several protocols have been used for genotyping the exon 21 variants, such as allele-specific PCR-RFLP using adapted primer to generate a digestion site for several enzymes and automatic sequencing to detect the SNVs, TaqMan Allele Discrimination assay and High-Resolution Melter analysis (HRMA). The aim was to describe a new approach to genotype the three variants c.2677G>T/A for the exon 21 doing only one PCR with the corresponding primers and the digestion of the PCR product with two restriction enzymes: BrsI to identify A allele and BseYI to differentiate between G or T. An improvement of this methodology was also described. The proposal technique here described is demonstrated to be very efficient, easy, fast, reproducible, and cost-effective.

Interaction of a Homologous Series of Amphiphiles with P-glycoprotein in a Membrane Environment-Contributions of Polar and Non-Polar Interactions

The transport of drugs by efflux transporters in biomembranes limits their bioavailability and is a major determinant of drug resistance development by cancer cells and pathogens. A large number of chemically dissimilar drugs are transported, and despite extensive studies, the molecular determinants of substrate specificity are still not well understood. In this work, we explore the role of polar and non-polar interactions on the interaction of a homologous series of fluorescent amphiphiles with the efflux transporter P-glycoprotein. The interaction of the amphiphiles with P-glycoprotein is evaluated through effects on ATPase activity, efficiency in inhibition of [¹²⁵I]-IAAP binding, and partition to the whole native membranes containing the transporter. The results were complemented with partition to model membranes with a representative lipid composition, and details on the interactions established were obtained from MD simulations. We show that when the total concentration of amphiphile is considered, the binding parameters obtained are apparent and do not reflect the affinity for P-gp. A new formalism is proposed that includes sequestration of the amphiphiles in the lipid bilayer and the possible binding of several molecules in P-gp's substrate-binding pocket. The intrinsic binding affinity thus obtained is essentially independent of amphiphile hydrophobicity, highlighting the importance of polar interactions. An increase in the lipophilicity and amphiphilicity led to a more efficient association with the lipid bilayer, which maintains the non-polar groups of the amphiphiles in the bilayer, while the polar groups interact with P-gp's binding pocket. The presence of several amphiphiles in this orientation is proposed as a mechanism for inhibition of P-pg function.

Fast Prediction of Binding Affinities of SARS-CoV-2 Spike Protein and Its Mutants with Antibodies through Intermolecular Interaction Modeling-Based Machine Learning

Since the introduction of the novel SARS-CoV-2 virus (COVID-19) in late 2019, various new variants have appeared with mutations that confer resistance to the vaccines and monoclonal antibodies that were developed in response to the wild-type virus. As we continue through the pandemic, an accurate and efficient methodology is needed to help predict the effects certain mutations will have on both our currently produced therapeutics and those that are in development. Using published cryo-electron microscopy and X-ray crystallography structures of the spike receptor binding domain region with currently known antibodies, in the present study, we created and cross-validated an intermolecular interaction modeling-based multi-layer perceptron machine learning approach that can accurately predict the mutation-caused shifts in the binding affinity between the spike protein (wild-type or mutant) and various antibodies. This validated artificial intelligence (AI) model was used to predict the binding affinity (Kd) of reported SARS-CoV-2 antibodies with various variants of concern, including the most recently identified "Deltamicron" (or "Deltacron") variant. This AI model may be employed in the future to predict the Kd of developed novel antibody therapeutics to overcome the challenging antibody resistance issue and develop structural bases for the effects of both current and new mutants of the spike protein. In addition, the similar AI strategy and approach based on modeling of the intermolecular interactions may be useful in development of machine learning models predicting binding affinities for other protein-protein binding systems, including other antibodies binding with their antigens.

Generalized Methodology for the Quick Prediction of Variant SARS-CoV-2 Spike Protein Binding Affinities with Human Angiotensin-Converting Enzyme II

Variants of the SARS-CoV-2 virus continue to remain a threat 2 years from the beginning of the pandemic. As more variants arise, and the B.1.1.529 (Omicron) variant threatens to create another wave of infections, a method is needed to predict the binding affinity of the spike protein quickly and accurately with human angiotensin-converting enzyme II (ACE2). We present an accurate and convenient energy minimization/molecular mechanics Poisson-Boltzmann surface area methodology previously used with engineered ACE2 therapeutics to predict the binding affinity of the Omicron variant. Without any additional data from the variants discovered after the publication of our first model, the methodology can accurately predict the binding of the spike/ACE2 variant complexes. From this methodology, we predicted that the Omicron variant spike has a Kd of ∼22.69 nM (which is very close to the experimental Kd of 20.63 nM published during the review process of the current report) and that spike protein of the new "Stealth" Omicron variant (BA.2) will display a Kd of ∼12.9 nM with the wild-type ACE2 protein. This methodology can be used with as-yet discovered variants, allowing for quick determinations regarding the variant's infectivity versus either the wild-type virus or its variants.

Effect of the Force Field on Molecular Dynamics Simulations of the Multidrug Efflux Protein P-Glycoprotein

Molecular dynamics (MD) simulations have been used extensively to study P-glycoprotein (P-gp), a flexible multidrug transporter that is a key player in the development of multidrug resistance to chemotherapeutics. A substantial body of literature has grown from simulation studies that have employed various simulation conditions and parameters, including AMBER, CHARMM, OPLS, GROMOS, and coarse-grained force fields, drawing conclusions from simulations spanning hundreds of nanoseconds. Each force field is typically parametrized and validated on different data and observables, usually of small molecules and peptides; there have been few comparisons of force field performance on large protein-membrane systems. Here we compare the conformational ensembles of P-gp embedded in a POPC/cholesterol bilayer generated over 500 ns of replicate simulation with five force fields from popular biomolecular families: AMBER 99SB-ILDN, CHARMM 36, OPLS-AA/L, GROMOS 54A7, and MARTINI. We find considerable differences among the ensembles with little conformational overlap, although they correspond to similar extents to structural data obtained from electron paramagnetic resonance and cross-linking studies. Moreover, each trajectory was still sampling new conformations at a high rate after 500 ns of simulation, suggesting the need for more sampling. This work highlights the need to consider known limitations of the force field used (e.g., biases toward certain secondary structures) and the simulation itself (e.g., whether sufficient sampling has been achieved) when interpreting accumulated results of simulation studies of P-gp and other transport proteins.

Challenges and limitations in the studies of glycoproteins: A computational chemist's perspective

Experimenters face challenges and limitations while analyzing glycoproteins due to their high flexibility, stereochemistry, anisotropic effects, and hydration phenomena. Computational studies complement experiments and have been used in characterization of the structural properties of glycoproteins. However, recent investigations revealed that computational studies face significant challenges as well. Here, we introduce and discuss some of these challenges and weaknesses in the investigations of glycoproteins. We also present requirements of future developments in computational biochemistry and computational biology areas that could be necessary for providing more accurate structural property analyses of glycoproteins using computational tools. Further theoretical strategies that need to be and can be developed are discussed herein.

The milk-derived lactoferrin inhibits V-ATPase activity by targeting its V1 domain

Lactoferrin (Lf), a bioactive milk protein, exhibits strong anticancer and antifungal activities. The search for Lf targets and mechanisms of action is of utmost importance to enhance its effective applications. A common feature among Lf-treated cancer and fungal cells is the inhibition of a proton pump called V-ATPase. Lf-driven V-ATPase inhibition leads to cytosolic acidification, ultimately causing cell death of cancer and fungal cells. Given that a detailed elucidation of how Lf and V-ATPase interact is still missing, herein we aimed to fill this gap by employing a five-stage computational approach. Molecular dynamics simulations of both proteins were performed to obtain a robust sampling of their conformational landscape, followed by clustering, which allowed retrieving representative structures, to then perform protein-protein docking. Subsequently, molecular dynamics simulations of the docked complexes and free binding energy calculations were carried out to evaluate the dynamic binding process and build a final ranking based on the binding affinities. Detailed atomist analysis of the top ranked complexes clearly indicates that Lf binds to the V₁ cytosolic domain of V-ATPase. Particularly, our data suggest that Lf binds to the interfaces between A/B subunits, where the ATP hydrolysis occurs, thus inhibiting this process. The free energy decomposition analysis further identified key binding residues that will certainly aid in the rational design of follow-up experimental studies, hence bridging computational and experimental biochemistry.

Mechanistic insights into TNFR1/MADD death domains in Alzheimer's disease through conformational molecular dynamic analysis

Proteins are tiny players involved in the activation and deactivation of multiple signaling cascades through interactions in cells. The TNFR1 and MADD interact with each other and mediate downstream protein signaling pathways which cause neuronal cell death and Alzheimer’s disease. In the current study, a molecular docking approach was employed to explore the interactive behavior of TNFR1 and MADD proteins and their role in the activation of downstream signaling pathways. The computational sequential and structural conformational results revealed that Asp400, Arg58, Arg59 were common residues of TNFR1 and MADD which are involved in the activation of downstream signaling pathways. Aspartic acid in negatively charged residues is involved in the biosynthesis of protein. However, arginine is a positively charged residue with the potential to interact with oppositely charged amino acids. Furthermore, our molecular dynamic simulation results also ensured the stability of the backbone of TNFR1 and MADD death domains (DDs) in binding interactions. This DDs interaction mediates some conformational changes in TNFR1 which leads to the activation of mediators proteins in the cellular signaling pathways. Taken together, a better understanding of TNFR1 and MADD receptors and their activated signaling cascade may help treat Alzheimer’s disease. The death domains of TNFR1 and MADD could be used as a novel pharmacological target for the treatment of Alzheimer’s disease by inhibiting the MAPK pathway.

Fast Prediction of Binding Affinities of the SARS-CoV-2 Spike Protein Mutant N501Y (UK Variant) with ACE2 and Miniprotein Drug Candidates

A recently identified variant of SARS-CoV-2 virus, known as the United Kingdom (UK) variant (lineage B.1.1.7), has an N501Y mutation on its spike protein. SARS-CoV-2 spike protein binds with angiotensin-converting enzyme 2 (ACE2), a key protein for the viral entry into the host cells. Here, we report an efficient computational approach, including the simple energy minimizations and binding free energy calculations, starting from an experimental structure of the binding complex along with experimental calibration of the calculated binding free energies, to rapidly and reliably predict the binding affinities of the N501Y mutant with human ACE2 (hACE2) and recently reported miniprotein and hACE2 decoy (CTC-445.2) drug candidates. It has been demonstrated that the N501Y mutation markedly increases the ACE2-spike protein binding affinity (Kd) from 22 to 0.44 nM, which could partially explain why the UK variant is more infectious. The miniproteins are predicted to have ∼10,000- to 100,000-fold diminished binding affinities with the N501Y mutant, creating a need for design of novel therapeutic candidates to overcome the N501Y mutation-induced drug resistance. The N501Y mutation is also predicted to decrease the binding affinity of a hACE2 decoy (CTC-445.2) binding with the spike protein by ∼200-fold. This convenient computational approach along with experimental calibration may be similarly used in the future to predict the binding affinities of potential new variants of the spike protein.

Can molecular dynamics simulations improve the structural accuracy and virtual screening performance of GPCR models?

The determination of G protein-coupled receptor (GPCR) structures at atomic resolution has improved understanding of cellular signaling and will accelerate the development of new drug candidates. However, experimental structures still remain unavailable for a majority of the GPCR family. GPCR structures and their interactions with ligands can also be modelled computationally, but such predictions have limited accuracy. In this work, we explored if molecular dynamics (MD) simulations could be used to refine the accuracy of in silico models of receptor-ligand complexes that were submitted to a community-wide assessment of GPCR structure prediction (GPCR Dock). Two simulation protocols were used to refine 30 models of the D3 dopamine receptor (D3R) in complex with an antagonist. Close to 60 μs of simulation time was generated and the resulting MD refined models were compared to a D3R crystal structure. In the MD simulations, the receptor models generally drifted further away from the crystal structure conformation. However, MD refinement was able to improve the accuracy of the ligand binding mode. The best refinement protocol improved agreement with the experimentally observed ligand binding mode for a majority of the models. Receptor structures with improved virtual screening performance, which was assessed by molecular docking of ligands and decoys, could also be identified among the MD refined models. Application of weak restraints to the transmembrane helixes in the MD simulations further improved predictions of the ligand binding mode and second extracellular loop. These results provide guidelines for application of MD refinement in prediction of GPCR-ligand complexes and directions for further method development.

Screening of Natural Compounds as P-Glycoprotein Inhibitors against Multidrug Resistance

Multidrug resistance (MDR) is a common problem when fighting cancer with chemotherapy. P-glycoprotein (P-gp, or MDR1) is an active pump responsible for the efflux of xenobiotics out of the cell, including anti-cancer drugs. It is a validated target against MDR. No crystal structure of the human P-gp is available to date, and only recently several cryo-EM structures have been solved. In this paper, we present a comprehensive computational approach that includes constructing the full-length three-dimensional structure of the human P-gp and its refinement using molecular dynamics. We assessed its flexibility and conformational diversity, compiling a dynamical ensemble that was used to dock a set of lignan compounds, previously reported as active P-gp inhibitors, and disclose their binding modes. Based on the statistical analysis of the docking results, we selected a system for performing the structure-based virtual screening of new potential P-gp inhibitors. We tested the method on a library of 87 natural flavonoids described in the literature, and 10 of those were experimentally assayed. The results reproduced the theoretical predictions only partially due to various possible factors. However, at least two of the predicted natural flavonoids were demonstrated to be effective P-gp inhibitors. They were able to increase the accumulation of doxorubicin inside the human promyelocytic leukemia HL60/MDR cells overexpressing P-gp and potentiate the antiproliferative activity of this anti-cancer drug.

Mamma Mia, P-glycoprotein binds again

The levels of amyloid peptides in the brain are regulated by a clearance pathway from neurons to the blood-brain barrier. The first step is thought to involve diffusion from the plasma membrane to the interstitium. However, amyloid peptides are hydrophobic and avidly intercalate within membranes. The ABC transporter P-glycoprotein is implicated in the clearance of amyloid peptides across the blood-brain, but its role at neurons is undetermined. We here propose that P-glycoprotein mediates 'exit' of amyloid peptides from neurons. Indeed, amyloid peptides have physicochemical similarities to substrates of P-glycoprotein, but their larger size represents a conundrum. This review probes the plausibility of a mechanism for amyloid peptide transport by P-glycoprotein exploiting evolving biochemical and structural models.

Capturing Substrate Translocation in an ABC Exporter at the Atomic Level

ATP-binding cassette (ABC) transporters chemomechanically couple ATP binding and hydrolysis to large-scale conformational changes, ultimately leading to substrate translocation across biological membranes. Despite recent progress in the structure determination of substrate-bound ABC exporters, the inherently dynamic mechanism of substrate transport remains unclear at the atomic level. In this work, we capture substrate translocation in the heterodimeric ABC exporter TM287/288 from the hyperthermophilic bacterium Thermotoga maritima using all-atom molecular dynamics (MD) simulations. Unguided multimicrosecond simulations at 375 K show how the drugs daunorubicin and verapamil, which were initially docked into the ABC transporter, get translocated through the exporter by following its large-scale alternating access conformational transitions between an inward-facing (IF) and an outward-facing (OF) conformation. Triggered by the affinity difference due to differential solvation of the binding cavity in the IF and OF conformations, the substrates unbind from the OF transporter and partition into the lipid bilayer. While daunorubicin is stably inserted into the outer leaflet of the bilayer, verapamil dynamically flip flops between the bilayer leaflets, possibly rendering its net transport futile.

In silico and in vitro identification of secoisolariciresinol as a re-sensitizer of P-glycoprotein-dependent doxorubicin-resistance NCI/ADR-RES cancer cells

P-glycoprotein (P-gp) is one of the highly expressed cancer cell efflux transporters that cause the failure of chemotherapy. To reverse P-gp induced multidrug resistance, we employed a flaxseed-derived lignan; secoisolariciresinol (SECO) that acts as an inhibitor of breast cancer resistance protein; another efflux transporter that shares some substrate/inhibitor specificity with P-gp. Molecular dynamics (MD) simulation identified SECO as a possible P-gp inhibitor. Comparing root mean square deviation (RMSD) of P-gp bound with SECO with that bound to its standard inhibitor verapamil showed that fluctuations in RMSD were lower in P-gp bound to SECO demonstrating higher stability of the complex of P-gp with SECO. In addition, the superimposition of P-gp structures after MD simulation showed that the nucleotide-binding domains of P-gp bound to SECO undertook a more central closer position compared with that bound to verapamil. Using rhodamine efflux assay on NCI/ADR-RES cancer cells, SECO was confirmed as a P-gp inhibitor, where cells treated with 25 or 50 µM of SECO showed significantly higher fluorescence intensity compared to control. Using MTT assay, SECO alone showed dose-dependent cytotoxicity, where 25 or 50 µM of SECO caused significantly less NCI/ADR-RES cellular viability compared to control. Furthermore, when 50 µM of SECO was added to doxorubicin (DOX), an anticancer drug, SECO significantly enhanced DOX-induced cytotoxicity compared to DOX alone. The combination index calculated by CompuSyn software indicated synergism between DOX and SECO. Our results suggest SECO as a novel P-gp inhibitor that can re-sensitize cancer cells during DOX chemotherapy.

Cross-linking, DEER-spectroscopy and molecular dynamics confirm the inward facing state of P-glycoprotein in a lipid membrane

The drug efflux pump P-glycoprotein (P-gp) displays a complex transport mechanism involving multiple drug binding sites and two centres for nucleotide hydrolysis. Elucidating the molecular mechanism of transport remains elusive and the availability of P-gp structures in distinct natural and ligand trapped conformations will accelerate our understanding. The present investigation sought to provide biochemical data to validate specific features of these structures; with particular focus on the transmembrane domain that provides the transport conduit. Hence our focus was on transmembrane helices six and twelve (TM6/TM12), which are believed to participate in drug binding, as they line the central transport conduit and provide a direct link to the catalytic centres. A series of P-gp mutants were generated with a single cysteine in both TM6 and TM12 to facilitate measurement of inter-helical distances using cross-linking and DEER strategies. Experimental results were compared to published structures per se and those refined by MD simulations. This analysis revealed that the refined inward-facing murine structure (4M1M) of P-gp provides a good representation of the proximity, topography and relative motions of TM6 and TM12 in reconstituted human P-gp.

Improvement of conventional anti-cancer drugs as new tools against multidrug resistant tumors

Multidrug resistance (MDR) is the dominant cause of the failure of cancer chemotherapy. The design of antitumor drugs that are able to evade MDR is rapidly evolving, showing that this area of biomedical research attracts great interest in the scientific community. The current review explores promising recent approaches that have been developed with the aim of circumventing or overcoming MDR. Encouraging results have been obtained in the investigation of the MDR-modulating properties of various classes of natural compounds and their analogues. Inhibition of P-gp or downregulation of its expression have proven to be the main mechanisms by which MDR can be surmounted. The use of hybrid molecules that are able to simultaneously interact with two or more cancer cell targets is currently being explored as a means to circumvent drug resistance. This strategy is based on the design of hybrid compounds that are obtained either by merging the structural features of separate drugs, or by conjugating two drugs or pharmacophores via cleavable/non-cleavable linkers. The approach is highly promising due to the pharmacokinetic and pharmacodynamic advantages that can be achieved over the independent administration of the two individual components. However, it should be stressed that the task of obtaining successful multivalent drugs is a very challenging one. The conjugation of anticancer agents with nitric oxide (NO) donors has recently been developed, creating a particular class of hybrid that can combat tumor drug resistance. Appropriate NO donors have been shown to reverse drug resistance via nitration of ABC transporters and by interfering with a number of metabolic enzymes and signaling pathways. In fact, hybrid compounds that are produced by covalently attaching NO-donors and antitumor drugs have been shown to elicit a synergistic cytotoxic effect in a variety of drug resistant cancer cell lines. Another strategy to circumvent MDR is based on nanocarrier-mediated transport and the controlled release of chemotherapeutic drugs and P-gp inhibitors. Their pharmacokinetics are governed by the nanoparticle or polymer carrier and make use of the enhanced permeation and retention (EPR) effect, which can increase selective delivery to cancer cells. These systems are usually internalized by cancer cells via endocytosis and accumulate in endosomes and lysosomes, thus preventing rapid efflux. Other modalities to combat MDR are described in this review, including the pharmaco-modulation of acridine, which is a well-known scaffold in the development of bioactive compounds, the use of natural compounds as means to reverse MDR, and the conjugation of anticancer drugs with carriers that target specific tumor-cell components. Finally, the outstanding potential of in silico structure-based methods as a means to evaluate the ability of antitumor drugs to interact with drug transporters is also highlighted in this review. Structure-based design methods, which utilize 3D structural data of proteins and their complexes with ligands, are the most effective of the in silico methods available, as they provide a prediction regarding the interaction between transport proteins and their substrates and inhibitors. The recently resolved X-ray structure of human P-gp can help predict the interaction sites of designed compounds, providing insight into their binding mode and directing possible rational modifications to prevent them from becoming P-gp drug substrates. In summary, although major efforts were invested in the search for new tools to combat drug resistant tumors, they all require further implementation and methodological development. Further investigation and progress in the abovementioned strategies will provide significant advances in the rational combat against cancer MDR.

Relationship between conformation shift and disease related variation sites in ATP-binding cassette transporter proteins

Transport of small molecules across the cell membrane is a crucial biological mechanism for the maintenance of the cell activity. ABC transporter family is a huge group in the transporter membrane proteins and actively transports the substrates using the energy derived from ATP hydrolysis. In humans, there are 48 distinct genes for ABC transporters. A variation of a single amino acid in the amino acid sequence of ABC transporter has been known to be linked with certain disease. The mechanism of the onset of the disease by the variation is, however, still unclear. Recent progress in the method to measure the structures of huge membrane proteins has enabled determination of the 3D structures of ABC transporters and the accumulation of coordinate data of ABC transporter has enabled us to obtain clues for the onset of the disease caused by a single variation of amino acid residue. We compared the structures of ABC transporter in apo and ATP-binding forms and found a possible conformation shift around pivot-like residues in the transmembrane domains. When this conformation change in ABC transporter and the location of pathogenic variation were compared, we found a reasonable match between the two, explaining the onset of the disease by the variation. They likely cause impairment of the pivot-like movement, weakening of ATP binding and weakening of membrane surface interactions. These findings will give a new interpretation of the variations on ABC transporter genes and pave a way to analyse the effect of variation on protein structure and function.

Structural and functional aspects of P-glycoprotein and its inhibitors

P-glycoprotein (P-gp) is a member of ATP-binding cassette (ABC) superfamily which extrudes chemotherapeutic agents out of the cell. Suppression of this efflux activity has been the subject of numerous attempts to develop P-gp inhibitors. The aim of this review is to present up-to-date information on the structural and functional aspects of P-gp and its known inhibitors. The data presented also provide some information on drug discovery approaches for candidate P-gp inhibitors. Nucleotide-binding domains (NBDs) and drug-binding domains (DBDs) have been extensively studied to gain more information about P-gp inhibition and it looks that the ATPase activity of this pump has been the most attractive target for designing inhibitors. Hydrophobic and π-π (aromatic) interactions between P-gp binding domains and inhibitors are dominant intermolecular forces that have been reported in many studies using different methods. Many synthetic and natural products have been found to possess inhibitory or modulatory effects on drug transporter proteins. Log P value is an important factor in studying these inhibitors and has a crucial role on absorption, distribution, metabolism, and excretion (ADME) properties of candidate P-gp inhibitors.

Quantitative comparison of ABC membrane protein type I exporter structures in a standardized way

An increasing number of ABC membrane protein structures are determined by cryo-electron microscopy and X-ray crystallography, consequently identifying differences between their conformations has become an arising issue. Therefore, we propose to define standardized measures for ABC Type I exporter structure characterization. We set conformational vectors, conftors, which describe the relative orientation of domains and can highlight structural differences. In addition, continuum electrostatics calculations were performed to characterize the energetics of membrane insertion illuminating functionally crucial regions. In summary, the proposed metrics contribute to deeper understanding of ABC membrane proteins' structural features, structure validation, and analysis of movements observed in a molecular dynamics trajectory. Moreover, the concept of standardized metrics can be applied not only to ABC membrane protein structures (http://conftors.hegelab.org).

The conformation and dynamics of P-glycoprotein in a lipid bilayer investigated by atomic force microscopy

The membrane-bound P-glycoprotein (Pgp) transporter plays a major role in human disease and drug disposition because of its ability to efflux a chemically diverse range of drugs through ATP hydrolysis and ligand-induced conformational changes. Deciphering these structural changes is key to understanding the molecular basis of transport and to developing molecules that can modulate efflux. Here, atomic force microscopy (AFM) is used to directly image individual Pgp transporter molecules in a lipid bilayer under physiological pH and ambient temperature. Analysis of the Pgp AFM images revealed "small" and "large" protrusions from the lipid bilayer with significant differences in protrusion height and volume. The geometry of these "small" and "large" protrusions correlated to the predicted extracellular (EC) and cytosolic (C) domains of the Pgp X-ray crystal structure, respectively. To assign these protrusions, simulated AFM images were produced from the Pgp X-ray crystal structures with membrane planes defined by three computational approaches, and a simulated 80 Å AFM cantilever tip. The theoretical AFM images of the EC and C domains had similar heights and volumes to the "small" and "large" protrusions in the experimental AFM images, respectively. The assignment of the protrusions in the AFM images to the EC and C domains was confirmed by changes in protrusion volume by Pgp-specific antibodies. The Pgp domains showed a considerable degree of conformational dynamics in time resolved AFM images. With this information, a model of Pgp conformational dynamics in a lipid bilayer is proposed within the context of the known Pgp X-ray crystal structures.