ß-Caryophyllene Induces Apoptosis and Inhibits Angiogenesis in Colorectal Cancer Models.

Beta-Caryophyllene (BCP), a naturally occurring sesquiterpene abundantly found in cloves, hops, and cannabis, is the active candidate of a relatively new group of vascular-inhibiting compounds that aim to block existing tumor blood vessels. Previously, we have reported the anti-cancer properties of BCP by utilizing a series of in-vitro anti-tumor-related assays using human colorectal carcinoma cells. The present study aimed to investigate the effects of BCP on in-vitro, ex-vivo, and in-vivo models of anti-angiogenic assays and evaluate its anti-cancer activity in xenograft tumor (both ectopic and orthotopic) mice models of human colorectal cancer. Computational structural analysis and an apoptosis antibody array were also performed to understand the molecular players underlying this effect. BCP exhibited strong anti-angiogenic activity by blocking the migration of endothelial cells, tube-like network formation, suppression of vascular endothelial growth factor (VEGF) secretion from human umbilical vein endothelial cells and sprouting of rat aorta microvessels. BCP has a probable binding at Site#0 on the surface of VEGFR2. Moreover, BCP significantly deformed the vascularization architecture compared to the negative control in a chick embryo chorioallantoic membrane assay. BCP showed a remarkable reduction in tumor size and fluorescence molecular tomography signal intensity in all the mice treated with BCP, in a dose-dependent relationship, in ectopic and orthotopic tumor xenograft models, respectively. The histological analysis of the tumor from BCP-treated mice revealed a clear reduction of the density of vascularization. In addition, BCP induced apoptosis through downregulation of HSP60, HTRA, survivin, and XIAP, along with the upregulation of p21 expressions. These results suggest that BCP acts at multiple stages of angiogenesis and could be used as a promising therapeutic candidate to halt the growth of colorectal tumor cells.

Design, synthesis, biological evaluation and dynamics simulation of indazole derivatives with antiangiogenic and antiproliferative anticancer activity.

VEGFR-2 has a pivotal role in promoting cancer angiogenesis. Herein, two series of novel indazole-based derivatives were designed, synthesized and evaluated for their in vitro inhibitory action against VEGFR-2 kinase enzyme. The second series 11a-e exhibited better potency than the first one 7a-d and 8a-f. Compounds 11b, 11c and 11e exhibited the most potent action, with IC50 of 5.4 nM, 5.6 nM and 7 nM, respectively. As a measure of cellular VEGFR-2 inhibition, compounds 11b and 11c showed strong inhibition of human umbilical vein endothelial cells (HUVEC) proliferation with 80% and 99.6% inhibition at 10 µM concentration, respectively. Attempting to interpret SAR of the synthesized compounds, and provide a basis for further optimization; a comprehensive modeling study was implemented. Molecular docking, dynamics simulation and free energy calculation of the synthesized compounds along with known VEGFR-2 inhibitors were applied. The study illustrated the effect of several factors on VEGFR-2 inhibition, such as the interaction with solvent accessible region of the enzyme, the presence of NH linker and the degree of conformational restriction. Finally, our compounds were evaluated for their in vitro anti-proliferative effect against the full NCI panel of cancer cell lines, where compounds 11a and 11c displayed mean GI% of 93 and 130%, respectively, and showed partly a better behavior than the FDA approved drug sorafenib, with respect to activity (GI50) and safety (LC50) against several cell lines. Thus, compound 11c represents a promising candidate for cancer treatment through antiangiogenic dependent and antiangiogenic independent modes of action.

Towards discovery of novel scaffold with potent antiangiogenic activity; design, synthesis of pyridazine based compounds, impact of hinge interaction, and accessibility of their bioactive conformation on VEGFR-2 activities.

Pyridazine scaffolds are considered privileged structures pertaining to its novelty, chemical stability, and synthetic feasibility. In our quest towards the development of novel scaffolds for effective vascular endothelial growth 2 (VEGFR-2) inhibition with antiangiogenic activity, four novel series of pyridazines were designed and synthesised. Five of the synthesised compounds; namely (8c, 8f, 15, 18b, and 18c) exhibited potent VEGFR-2 inhibitory potency (>80%); with IC50 values ranging from low micromolar to nanomolar range; namely compounds 8c, 8f, 15, 18c with (1.8 µM, 1.3 µM, 1.4 µM, 107 nM), respectively. Moreover, 3-[4-{(6-oxo-1,6-dihydropyridazin-3-yl)oxy}phenyl]urea derivative (18b) exhibited nanomolar potency towards VEGFR-2 (60.7 nM). In cellular assay, the above compounds showed excellent inhibition of VEGF-stimulated proliferation of human umbilical vein endothelial cells at 10 µM concentration. Finally, an extensive molecular simulation study was performed to investigate the probable interaction with VEGFR-2.

The Too Many Faces of PD-L1: A Comprehensive Conformational Analysis Study.

In the current study, we focused on the immune-checkpoints PD-1 pathway and in particular on the ligand PD-L1. We studied the conformational dynamics of PD-L1 through principal component analysis of existing crystal structures combined with classical and accelerated molecular dynamics simulations. We identified the maximum structural displacements that take place in all PD-L1 crystal structures and in the molecular dynamics trajectories. We found that these displacements are attributed to specific flexible regions in the protein. We also investigated the conformational preference for small molecule binding and highlighted a methionine residue at the binding site, which plays a key role in drug binding. The binding mechanism of PD-L1 to other binding partners is also discussed in detail from a computational perspective. We hope that the data presented here support the ongoing efforts to discover effective therapies targeting the PD-1 immune-checkpoint pathway.

Photoelectron Spectra and Electronic Structures of the Radiosensitizer Nimorazole and Related Compounds.

Soft X-ray photoelectron spectroscopy has been used to investigate the radiosensitizer nimorazole and related model compounds. We report the valence and C, N, and O 1s photoemission spectra and K-edge NEXAFS spectra of gas-phase nimorazole, 1-methyl-5-nitroimidazole, and 4(5)-nitroimidazole in combination with theoretical calculations. The valence band and core level spectra are in agreement with theory. We determine the equilibrium populations of the two tautomers in 4(5)-nitroimidazole and find a ratio of 1:0.7 at 390 K. The NEXAFS spectra of the studied nitroimidazoles show excellent agreement with spectra of compounds available in the literature that exhibit a similar chemical environment. By comparing 1-methyl-5-nitroimidazole (single tautomer) with 4(5)-nitroimidazole, we are able to disentangle the photoemission and photoabsorption spectra and identify features due to each single tautomer.

Structures of cycloserine and 2-oxazolidinone probed by X-ray photoelectron spectroscopy: theory and experiment.

The electronic structures and properties of 2-oxazolidinone and the related compound cycloserine (CS) have been investigated using theoretical calculations and core and valence photoelectron spectroscopy. Isomerization of the central oxazolidine heterocycle and the addition of an amino group yield cycloserine. Theory correctly predicts the C, N, and O 1s core spectra, and additionally, we report theoretical natural bond orbital (NBO) charges. The valence ionization energies are also in agreement with theory and previous measurements. Although the lowest binding energy part of the spectra of the two compounds shows superficial similarities, further analysis of the charge densities of the frontier orbitals indicates substantial reorganization of the wave functions as a result of isomerization. The highest occupied molecular orbital (HOMO) of CS shows leading carbonyl π character with contributions from other heavy (non-H) atoms in the molecule, while the HOMO of 2-oxazolidinone (OX2) has leading nitrogen, carbon, and oxygen pπ characters. The present study further theoretically predicts bond resonance effects of the compounds, evidence for which is provided by our experimental measurements and published crystallographic data.

Discovery of new HER2/EGFR dual kinase inhibitors based on the anilinoquinazoline scaffold as potential anti-cancer agents.

Herein, we designed and synthesized certain anilinoquinazoline derivatives bearing bulky arylpyridinyl, arylpropenoyl and arylpyrazolyl moieties at the 4' position of the anilinoquinazoline, as potential dual HER2/EGFR kinase inhibitors. A detailed molecular modeling study was performed by docking the synthesized compounds in the active site of the epidermal growth factor receptor (EGFR). The synthesized compounds were further tested for their inhibitory activity on EGFR and HER2 tyrosine kinases. The aryl 2-imino-1,2-dihydropyridine derivatives 5d and 5e displayed the most potent inhibitory activity on EGFR with IC50 equal to 2.09 and 1.94 µM, respectively, and with IC50 equal to 3.98 and 1.04 µM on HER2, respectively. Furthermore, the anti-proliferative activity of these most active compounds on MDA-MB-231 breast cancer cell lines, known to overexpress EGFR, showed an IC50 range of 2.4 and 2.5 µM, respectively.

Combining machine learning, molecular dynamics, and free energy analysis for (5HT)-2A receptor modulator classification.

The 5-Hydroxytryptamine (5HT)-2A receptor, a key target in psychoactive drug development, presents significant challenges in the design of selective compounds. Here, we describe the construction, evaluation and validation of two machine learning (ML) models for the classification of bioactivity mechanisms against the (5HT)-2A receptor. Employing neural networks and XGBoost models, we achieved an overall accuracy of around 87 %, which was further enhanced through molecular modelling (MM) (e.g. molecular dynamics simulations) and binding free energy analysis. This ML-MM integration provided insights into the mechanisms of direct modulators and prodrugs. A significant outcome of the current study is the development of a 'binding free energy fingerprint' specific to (5HT)-2A modulators, offering a novel metric for evaluating drug efficacy against this target. Our study demonstrates the prospective of employing a successful workflow combining AI with structural biology, offering a powerful tool for advancing psychoactive drug discovery.

Discovery of allosteric SHP2 inhibitors through ensemble-based consensus molecular docking, endpoint and absolute binding free energy calculations.

SHP2 (Src homology-2 domain-containing protein tyrosine phosphatase-2) is a cytoplasmic protein -tyrosine phosphatase encoded by the gene PTPN11. It plays a crucial role in regulating cell growth and differentiation. Specifically, SHP2 is an oncoprotein associated with developmental pathologies and several different cancer types, including gastric, leukemia and breast cancer and is of great therapeutic interest. Given these roles, current research efforts have focused on developing SHP2 inhibitors. Allosteric SHP2 inhibitors have been shown to be more selective and pharmacologically appealing compared to competitive catalytic inhibitors targeting SHP2. Nevertheless, there remains a need for novel allosteric inhibitor scaffolds targeting SHP2 to develop compounds with improved selectivity, cell permeability, and bioavailability. Towards this goal, this study applied various computational tools to screen over 6 million compounds against the allosteric site within SHP2. The top-ranked hits from our in-silico screening were validated using protein thermal shift and biolayer interferometry assays, revealing three potent compounds. Kinetic binding assays were employed to measure the binding affinities of the top-ranked compounds and demonstrated that they all bind to SHP2 with a nanomolar affinity. Hence the compounds and the computational workflow described herein provide an effective approach for identifying and designing a generation of improved allosteric inhibitors of SHP2.

Photoelectron spectra of some antibiotic building blocks: 2-azetidinone and thiazolidine-carboxylic acid.

X-ray photoelectron spectra of the core and valence levels of the fundamental building blocks of ß-lactam antibiotics have been investigated and compared with theoretical calculations. The spectra of the compounds 2-azetidinone and the 2- and 4-isomers of thiazolidine-carboxylic acid are interpreted in the light of theoretical calculations. The spectra of the two isomers of thiazolidine-carboxylic acid are rather similar, as expected, but show clear effects due to isomerization. Both isomers are analogues of proline, which is well-known to populate several low energy conformers in the gas phase. We have investigated the low energy conformers of thiazolidine-4-carboxylic acid theoretically in more detail and find some spectroscopic evidence that multiple conformers may be present. The measured valence levels are assigned for all three compounds, and the character of the frontier orbitals is identified and analyzed.

Leveraging structural and 2D-QSAR to investigate the role of functional group substitutions, conserved surface residues and desolvation in triggering the small molecule-induced dimerization of hPD-L1.

Small molecules are rising as a new generation of immune checkpoints' inhibitors, with compounds targeting the human Programmed death-ligand 1 (hPD-L1) protein are pioneering this area of research. Promising examples include the recently disclosed compounds from Bristol-Myers-Squibb (BMS). These molecules bind specifically to hPD-L1 through a unique mode of action. They induce dimerization between two hPD-L1 monomers through the hPD-1 binding interface in each monomer, thereby inhibiting the PD-1/PD-L1 axis. While the recently reported crystal structures of such small molecules bound to hPD-L1 reveal valuable insights regarding their molecular interactions, there is still limited information about the dynamics driving this unusual complex formation. The current study provides an in-depth computational structural analysis to study the interactions of five small molecule compounds in complex with hPD-L1. By employing a combination of molecular dynamic simulations, binding energy calculations and computational solvent mapping techniques, our analyses quantified the dynamic roles of different hydrophilic and lipophilic residues at the surface of hPD-L1 in mediating these interactions. Furthermore, ligand-based analyses, including Free-Wilson 2D-QSAR was conducted to quantify the impact of R-group substitutions at different sites of the phenoxy-methyl biphenyl core. Our results emphasize the importance of a terminal phenyl ring that must be present in any hPD-L1 small molecule inhibitor. This phenyl moiety overlaps with a very unfavorable hydration site, which can explain the ability of such small molecules to trigger hPD-L1 dimerization.

A 'deep dive' into the SARS-Cov-2 polymerase assembly: identifying novel allosteric sites and analyzing the hydrogen bond networks and correlated dynamics.

Replication of the SARS-CoV-2 genome is a fundamental step in the virus life cycle and inhibiting the SARS-CoV2 replicase machinery has been proven recently as a promising approach in combating the virus. Despite this recent success, there are still several aspects related to the structure, function and dynamics of the CoV-2 polymerase that still need to be addressed. This includes understanding the dynamicity of the various polymerase subdomains, analyzing the hydrogen bond networks at the active site and at the template entry in the presence of water, studying the binding modes of the nucleotides at the active site, highlighting positions for acceptable nucleotides' substitutions that can be tolerated at different positions within the nascent RNA strand, identifying possible allosteric sites within the polymerase structure and studying their correlated dynamics relative to the catalytic site. Here, we combined various cutting-edge modelling tools with the recently resolved SARS-CoV-2 cryo-EM polymerase structures to fill this gap in knowledge. Our findings provide a detailed analysis of the hydrogen bond networks at various parts of the polymerase structure and suggest possible nucleotides' substitutions that can be tolerated by the polymerase complex. We also report here three 'druggable' allosteric sites within the NSP12 RdRp that can be targeted by small molecule inhibitors. Our correlated motion analysis shows that the dynamics within one of the newly identified sites are linked to the active site, indicating that targeting this site can significantly impact the catalytic activity of the SARS-CoV-2 polymerase.Communicated by Ramaswamy H. Sarma.

GPCR_LigandClassify.py; a rigorous machine learning classifier for GPCR targeting compounds.

The current study describes the construction of various ligand-based machine learning models to be used for drug-repurposing against the family of G-Protein Coupled Receptors (GPCRs). In building these models, we collected > 500,000 data points, encompassing experimentally measured molecular association data of > 160,000 unique ligands against > 250 GPCRs. These data points were retrieved from the GPCR-Ligand Association (GLASS) database. We have used diverse molecular featurization methods to describe the input molecules. Multiple supervised ML algorithms were developed, tested and compared for their accuracy, F scores, as well as for their Matthews' correlation coefficient scores (MCC). Our data suggest that combined with molecular fingerprinting, ensemble decision trees and gradient boosted trees ML algorithms are on the accuracy border of the rather sophisticated deep neural nets (DNNs)-based algorithms. On a test dataset, these models displayed an excellent performance, reaching a ~ 90% classification accuracy. Additionally, we showcase a few examples where our models were able to identify interesting connections between known drugs from the Drug-Bank database and members of the GPCR family of receptors. Our findings are in excellent agreement with previously reported experimental observations in the literature. We hope the models presented in this paper synergize with the currently ongoing interest of applying machine learning modeling in the field of drug repurposing and computational drug discovery in general.

Structure-based screening and validation of potential dengue virus inhibitors through classical and QM/MM affinity estimation.

The recurrent outbreaks of dengue virus around the globe represent a huge challenge for governments and public health organizations. With the rapid growth and ease of transportation, dengue disease continues to spread, placing more of the world population under constant threat. Despite decades of research efforts, no effective small molecule antivirals are available against dengue virus. With the efficacy of the recently developed vaccine to be determined, there is an urgent unmet need for small molecule dengue virus treatments. In the current study, we employed state-of-the-art molecular modelling simulations to identify novel inhibitors of the dengue virus envelope protein. The binding modes of all compounds within the conserved ß-OctylGlucoside (ß-OG) pocket were studied using a combination of docking, molecular dynamics simulations and binding free energy calculations. Here, we describe ten new compounds that significantly reduce production of dengue virus as determined using standard cell-based virological assays. Moreover, we present a comprehensive structural analysis of the identified hits, focusing on their electrostatic and lipophilic binding energy contributions. Finally, we highlight the effect of the desolvation penalty in limiting the activity of some of these compounds. The data presented here paves the way toward rationally designing selective and potent novel inhibitors against dengue virus.

Comprehensive in vitro characterization of PD-L1 small molecule inhibitors.

Blockade of the programmed cell death 1 (PD-1)/programmed cell death-ligand 1 (PD-L1) interaction has emerged as a powerful strategy in cancer immunotherapy. Recently, there have been enormous efforts to develop potent PD-1/PD-L1 inhibitors. In particular, Bristol-Myers Squibb (BMS) and Aurigene Discovery Technologies have individually disclosed several promising PD-1/PD-L1 inhibitors, whose detailed experimental data are not publicly disclosed. In this work, we report the rigorous and systematic in vitro characterization of a selected set of potent PD-1/PD-L1 macrocyclic peptide (BMSpep-57) and small-molecule inhibitors (BMS-103, BMS-142) from BMS and a peptidomimetic small-molecule inhibitor from Aurigene (Aurigene-1) using a series of biochemical and cell-based assays. Our results confirm that BMS-103 and BMS-142 are strongly active in biochemical assays; however, their acute cytotoxicity greatly compromised their immunological activity. On the other hand, Aurigene-1 did not show any activity in both biochemical and immunological assays. Furthermore, we also report the discovery of a small-molecule immune modulator, whose mode-of-action is not clear; however, it exhibits favorable drug-like properties and strong immunological activity. We hope that the results presented here will be useful in guiding the development of next-generation PD-1/PD-L1 small molecule inhibitors.

Effects of protein-protein interactions and ligand binding on the ion permeation in KCNQ1 potassium channel.

The voltage-gated KCNQ1 potassium ion channel interacts with the type I transmembrane protein minK (KCNE1) to generate the slow delayed rectifier (IKs) current in the heart. Mutations in these transmembrane proteins have been linked with several heart-related issues, including long QT syndromes (LQTS), congenital atrial fibrillation, and short QT syndrome. Off-target interactions of several drugs with that of KCNQ1/KCNE1 ion channel complex have been known to cause fatal cardiac irregularities. Thus, KCNQ1/KCNE1 remains an important avenue for drug-design and discovery research. In this work, we present the structural and mechanistic details of potassium ion permeation through an open KCNQ1 structural model using the combined molecular dynamics and steered molecular dynamics simulations. We discuss the processes and key residues involved in the permeation of a potassium ion through the KCNQ1 ion channel, and how the ion permeation is affected by (i) the KCNQ1-KCNE1 interactions and (ii) the binding of chromanol 293B ligand and its derivatives into the complex. The results reveal that interactions between KCNQ1 with KCNE1 causes a pore constriction in the former, which in-turn forms small energetic barriers in the ion-permeation pathway. These findings correlate with the previous experimental reports that interactions of KCNE1 dramatically slows the activation of KCNQ1. Upon ligand-binding onto the complex, the energy-barriers along ion permeation path are more pronounced, as expected, therefore, requiring higher force in our steered-MD simulations. Nevertheless, pulling the ion when a weak blocker is bound to the channel does not necessitate high force in SMD. This indicates that our SMD simulations have been able to discern between strong and week blockers and reveal their influence on potassium ion permeation. The findings presented here will have some implications in understanding the potential off-target interactions of the drugs with the KCNQ1/KCNE1 channel that lead to cardiotoxic effects.

When theory meets experiment: the PD-1 challenge.

Applying atomistic computational modeling to drug discovery has proven to be a hugely successful approach, allowing drug-receptor interactions to be predicted and drugs to be optimized for potency, selectivity, and safety. However, when it comes to predicting protein-protein interactions and to rationally designing regulators of these interactions, computational tools often fail. Here, we report one of the rare instances where state-of-the-art computer simulations, guided by experiment, were able to correctly predict one of the most sophisticated protein-protein interactions. We revisit our previous discovery of the complex of human PD-1 with the ligand PD-L1 and compare our earlier findings with the recently published crystal structure of the same complex. Side-by-side comparison of the model of the complex with its crystal structure reveals outstanding agreement and suggests that our protein-protein prediction workflow could be applied to similar problems.

A comprehensive structural model for the human KCNQ1/KCNE1 ion channel.

The voltage-gated KCNQ1/KCNE1 potassium ion channel complex, forms the slow delayed rectifier (IKs) current in the heart, which plays an important role in heart signaling. The importance of KCNQ1/KCNE1 channel's function is further implicated by the linkage between loss-of-function and gain-of-function mutations in KCNQ1 or KCNE1, and long QT syndromes, congenital atrial fibrillation, and short QT syndrome. Also, KCNQ1/KCNE1 channels are an off-target for many non-cardiovascular drugs, leading to fatal cardiac irregularities. One solution to address and study the mentioned aspects of KCNQ1/KNCE1 channel would be the structural studies using a validated and accurate model. Along the same line in this study, we have used several top-notch modeling approaches to build a structural model for the open state of KCNQ1 protein, which is both accurate and compatible with available experimental data. Next, we included the KCNE1 protein components using data-driven protein-protein docking simulations, encompassing a 4:2 stoichiometry to complete the picture of the channel complex formed by these two proteins. All the protein systems generated through these processes were refined by long Molecular Dynamics simulations. The refined models were analyzed extensively to infer data about the interaction of KCNQ1 channel with its accessory KCNE1 beta subunits.

Modeling the human Nav1.5 sodium channel: structural and mechanistic insights of ion permeation and drug blockade.

Abnormalities in the human Nav1.5 (hNav1.5) voltage-gated sodium ion channel (VGSC) are associated with a wide range of cardiac problems and diseases in humans. Current structural models of hNav1.5 are still far from complete and, consequently, their ability to study atomistic interactions of this channel is very limited. Here, we report a comprehensive atomistic model of the hNav1.5 ion channel, constructed using homology modeling technique and refined through long molecular dynamics simulations (680 ns) in the lipid membrane bilayer. Our model was comprehensively validated by using reported mutagenesis data, comparisons with previous models, and binding to a panel of known hNav1.5 blockers. The relatively long classical MD simulation was sufficient to observe a natural sodium permeation event across the channel's selectivity filters to reach the channel's central cavity, together with the identification of a unique role of the lysine residue. Electrostatic potential calculations revealed the existence of two potential binding sites for the sodium ion at the outer selectivity filters. To obtain further mechanistic insight into the permeation event from the central cavity to the intracellular region of the channel, we further employed "state-of-the-art" steered molecular dynamics (SMD) simulations. Our SMD simulations revealed two different pathways through which a sodium ion can be expelled from the channel. Further, the SMD simulations identified the key residues that are likely to control these processes. Finally, we discuss the potential binding modes of a panel of known hNav1.5 blockers to our structural model of hNav1.5. We believe that the data presented here will enhance our understanding of the structure-property relationships of the hNav1.5 ion channel and the underlying molecular mechanisms in sodium ion permeation and drug interactions. The results presented here could be useful for designing safer drugs that do not block the hNav1.5 channel.

A Comprehensive Computational Analysis for the Binding Modes of Hepatitis C Virus NS5A Inhibitors: The Question of Symmetry.

Direct-acting antivirals (DAAs) form the current standard of care (SOC) against hepatitis C virus (HCV). These drugs selectively target the viral proteins, offering a unique mechanism to avoid toxicity, to increase their efficacy, and to evolve from decades of interferon- and ribavirin-based therapy. Among the promising HCV targets for DAAs is the NS5A protein, and daclatasvir (DCV) forms a first-in-class compound that selectively targets this protein. Despite the exceptional potency of DCV (∼picomolar IC50) and although several DCV derivatives have been approved for human use or are close to approval, the exact mode of action of these drugs is still incomplete. This is simply due to the vast complexity of cocrystallizing DCV with NS5A in the absence of two amphipathic helices that are required for DCV binding. In this context, computational modeling provides a unique alternative to solve this problem. Here, we build upon our recent discovery of a completely symmetrical interaction between DCV and NS5A and investigate the mode of binding of six other structures similar to DCV. The selected compounds include both symmetric and asymmetric molecules. In addition, we show that our model correlates very well with mutations that can confer resistance to DCV. The current study enhances our understanding of the mode of action of this class of HCV inhibitors and helps in defining the origin of resistance to these drugs.