Computational modeling of the p7 monomer from HCV and its interaction with small molecule drugs.
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ABSTRACT:
SUBMITTER: Wang YT
PROVIDER: S-EPMC3724979 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:The current standard care therapy for hepatitis C virus (HCV) infection consists of two regimes, namely interferon-based and interferon-free treatments. The treatment through the combination of ribavirin and pegylated interferon is expensive, only mildly effective, and is associated with severe side effects. In 2011, two direct-acting antiviral (DAA) drugs, boceprevir and telaprevir, were licensed that have shown enhanced sustained virologic response (SVR) in phase III clinical trial, however, these interferon-free treatments are more sensitive to HCV genotype 1 infection. The variable nature of HCV, and the limited number of inhibitors developed thus aim in expanding the repertoire of available drug targets, resulting in targeting the virus assembly therapeutically. AIM:We conducted this study to predict the 3D structure of the p7 protein from the HCV genotypes 3 and 4. Approximately 63 amino acid residues encoded in HCV render this channel sensitive to inhibitors, making p7 a promising target for novel therapies. HCV p7 protein forms a small membrane known as viroporin, and is essential for effective self-assembly of large channels that conduct cation assembly and discharge infectious virion particles. METHOD:In this study, we screened drugs and flavonoids known to disrupt translation and production of HCV proteins, targeted against the active site of p7 residues of HCV genotype 3 (GT3) (isolatek3a) and HCV genotype 4a (GT4) (isolateED43). Furthermore, we conducted a quantitative structure-activity relationship and docking interaction study. RESULTS:The drug NB-DNJ formed the highest number of hydrogen bond interactions with both modeled p7 proteins with high interaction energy, followed by BIT225. A flavonoid screen demonstrated that Epigallocatechin gallate (EGCG), nobiletin, and quercetin, have more binding modes in GT3 than in GT4. Thus, the predicted p7 protein molecule of HCV from GT3 and GT4 provides a general avenue to target structure-based antiviral compounds. CONCLUSIONS:We hypothesize that the inhibitors of viral p7 identified in this screen may be a new class of potent agents, but further confirmation in vitro and in vivo is essential. This structure-guided drug design for both GT3 and GT4 can lead to the identification of drug-like natural compounds, confirming p7 as a new target in the rapidly increasing era of HCV.
| S-EPMC4451521 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNA) are small endogenously transcribed regulatory RNA which modulates gene expression at a post transcriptional level. These small RNAs have now been shown to be critical regulators in a number of biological processes in the cell including pathophysiology of diseases like cancers. The increasingly evident roles of microRNA in disease processes have also motivated attempts to target them therapeutically. Recently there has been immense interest in understanding small molecule mediated regulation of RNA, including microRNA.ResultsWe have used publicly available datasets of high throughput screens on small molecules with potential to inhibit microRNA. We employed computational methods based on chemical descriptors and machine learning to create predictive computational models for biological activity of small molecules. We further used a substructure based approach to understand common substructures potentially contributing to the activity.ConclusionWe generated computational models based on Naïve Bayes and Random Forest towards mining small RNA binding molecules from large molecular datasets. We complement this with substructure based approach to identify and understand potentially enriched substructures in the active dataset. We use this approach to identify miRNA binding potential of a set of approved drugs, suggesting a probable novel mechanism of off-target activity of these drugs. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding RNA binding activities of small molecules and predictive modeling of these activities.
| S-EPMC3466443 | biostudies-literature
Project description:BackgroundThe dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries.MethodsIn the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules.ResultsWe show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.
| S-EPMC5021286 | biostudies-literature
Project description:PurposeFor optimizing the local, pulmonary targeting of inhaled medications, it is important to analyze the relationship between the physicochemical properties of small molecules and their absorption, retention and distribution in the various cell types of the airways and alveoli.MethodsA computational, multiscale, cell-based model was constructed to facilitate analysis of pulmonary drug transport and distribution. The relationship between the physicochemical properties and pharmacokinetic profile of monobasic molecules was explored. Experimental absorption data of compounds with diverse structures were used to validate this model. Simulations were performed to evaluate the effect of active transport and organelle sequestration on the absorption kinetics of compounds.ResultsRelating the physicochemical properties to the pharmacokinetic profiles of small molecules reveals how the absorption half-life and distribution of compounds are expected to vary in different cell types and anatomical regions of the lung. Based on logP, pK(a) and molecular radius, the absorption rate constants (K(a)) calculated with the model were consistent with experimental measurements of pulmonary drug absorption.ConclusionsThe cell-based mechanistic model developed herein is an important step towards the rational design of local, lung-targeted medications, facilitating the design and interpretation of experiments aimed at optimizing drug transport properties in lung.
| S-EPMC2907074 | biostudies-literature
Project description:Hepatitis C virus (HCV) is the causative agent of end-stage liver disease. Recent advances in the last decade in anti HCV treatment strategies have dramatically increased the viral clearance rate. However, several limitations are still associated, which warrant a great need of novel, safe and selective drugs against HCV infection. Towards this objective, we explored highly potent and selective small molecule inhibitors, the ellagitannins, from the crude extract of Pomegranate (Punica granatum) fruit peel. The pure compounds, punicalagin, punicalin, and ellagic acid isolated from the extract specifically blocked the HCV NS3/4A protease activity in vitro. Structural analysis using computational approach also showed that ligand molecules interact with the catalytic and substrate binding residues of NS3/4A protease, leading to inhibition of the enzyme activity. Further, punicalagin and punicalin significantly reduced the HCV replication in cell culture system. More importantly, these compounds are well tolerated ex vivo and'no observed adverse effect level' (NOAEL) was established upto an acute dose of 5000 mg/kg in BALB/c mice. Additionally, pharmacokinetics study showed that the compounds are bioavailable. Taken together, our study provides a proof-of-concept approach for the potential use of antiviral and non-toxic principle ellagitannins from pomegranate in prevention and control of HCV induced complications.
| S-EPMC4067622 | biostudies-literature
Project description:Recent studies identified signal peptidase complex subunit 1 (SPCS1) as a proviral host factor for Flaviviridae viruses, including HCV. One of the SPCS1's roles in flavivirus propagation was attributed to its regulation of signal peptidase complex (SPC)-mediated processing of flavivirus polyprotein, especially C-prM junction. However, whether SPCS1 also regulates any SPC-mediated processing sites within HCV polyprotein remains unclear. In this study, we determined that loss of SPCS1 specifically impairs the HCV E2-p7 processing by the SPC. We also determined that efficient separation of E2 and p7, regardless of its dependence on SPC-mediated processing, leads to SPCS1 dispensable for HCV assembly These results suggest that SPCS1 regulates HCV assembly by facilitating the SPC-mediated processing of E2-p7 precursor. Structural modeling suggests that intrinsically delayed processing of the E2-p7 is likely caused by the structural rigidity of p7 N-terminal transmembrane helix-1 (p7/TM1/helix-1), which has mostly maintained membrane-embedded conformations during molecular dynamics (MD) simulations. E2-p7-processing-impairing p7 mutations narrowed the p7/TM1/helix-1 bending angle against the membrane, resulting in closer membrane embedment of the p7/TM1/helix-1 and less access of E2-p7 junction substrate to the catalytic site of the SPC, located well above the membrane in the ER lumen. Based on these results we propose that the key mechanism of action of SPCS1 in HCV assembly is to facilitate the E2-p7 processing by enhancing the E2-p7 junction site presentation to the SPC active site. By providing evidence that SPCS1 facilitates HCV assembly by regulating SPC-mediated cleavage of E2-p7 junction, equivalent to the previously established role of this protein in C-prM junction processing in flavivirus, this study establishes the common role of SPCS1 in Flaviviridae family virus propagation as to exquisitely regulate the SPC-mediated processing of specific, suboptimal target sites.
| S-EPMC8853643 | biostudies-literature
Project description:In our initial publication on the in vitro testing of more than 200 compounds, we demonstrated that small molecules can inhibit phagocytosis. We therefore theorized that a small molecule drug discovery-based approach to the treatment of immune cytopenias (ITP, AIHA, HTR, DHTR) is feasible. Those earlier studies showed that small molecules with anti-phagocytic groups, such as the pyrazole core, are good models for producing efficacious phagocytosis inhibitors with low toxicity. We recently screened a chemical library of 80 compounds containing pyrazole/isoxazole/pyrrole core structures and found four hit molecules for further follow-up, all having the pyrazole core structure. Subsequent evaluation via MTT viability, LDH release, and apoptosis, led to the selection of two lead compounds with negligible toxicity and high efficacy. In an in vitro assay for inhibition of phagocytosis, their IC50 values were 2-4 µM. The rational development of these discoveries from hit to lead molecule stage, viz. independent synthesis/scale up of hit molecules, and in vivo activities in mouse models of autoimmune disease, will result in the selection of a lead compound(s) for further pre-clinical evaluation.
| S-EPMC9867408 | biostudies-literature
Project description:The binding affinity of a protein-protein interaction is concentrated at amino acids known as hot spots. It has been suggested that small molecules disrupt protein-protein interactions by either (i) engaging receptor protein hot spots or (ii) mimicking hot spots of the protein ligand. Yet, no systematic studies have been done to explore how effectively existing small-molecule protein-protein interaction inhibitors mimic or engage hot spots at protein interfaces. Here, we employ explicit-solvent molecular dynamics simulations and end-point MM-GBSA free energy calculations to explore this question. We select 36 compounds for which high-quality binding affinity and cocrystal structures are available. Five complexes that belong to three classes of protein-protein interactions (primary, secondary, and tertiary) were considered, namely, BRD4•H4, XIAP•Smac, MDM2•p53, Bcl-xL•Bak, and IL-2•IL-2R?. Computational alanine scanning using MM-GBSA identified hot-spot residues at the interface of these protein interactions. Decomposition energies compared the interaction of small molecules with individual receptor hot spots to those of the native protein ligand. Pharmacophore analysis was used to investigate how effectively small molecules mimic the position of hot spots of the protein ligand. Finally, we study whether small molecules mimic the effects of the native protein ligand on the receptor dynamics. Our results show that, in general, existing small-molecule inhibitors of protein-protein interactions do not optimally mimic protein-ligand hot spots, nor do they effectively engage protein receptor hot spots. The more effective use of hot spots in future drug design efforts may result in smaller compounds with higher ligand efficiencies that may lead to greater success in clinical trials.
| S-EPMC6148373 | biostudies-literature
Project description:The Prokineticin receptor (PKR) 1 and 2 subtypes are novel members of family A GPCRs, which exhibit an unusually high degree of sequence similarity. Prokineticins (PKs), their cognate ligands, are small secreted proteins of ?80 amino acids; however, non-peptidic low-molecular weight antagonists have also been identified. PKs and their receptors play important roles under various physiological conditions such as maintaining circadian rhythm and pain perception, as well as regulating angiogenesis and modulating immunity. Identifying binding sites for known antagonists and for additional potential binders will facilitate studying and regulating these novel receptors. Blocking PKRs may serve as a therapeutic tool for various diseases, including acute pain, inflammation and cancer.Ligand-based pharmacophore models were derived from known antagonists, and virtual screening performed on the DrugBank dataset identified potential human PKR (hPKR) ligands with novel scaffolds. Interestingly, these included several HIV protease inhibitors for which endothelial cell dysfunction is a documented side effect. Our results suggest that the side effects might be due to inhibition of the PKR signaling pathway. Docking of known binders to a 3D homology model of hPKR1 is in agreement with the well-established canonical TM-bundle binding site of family A GPCRs. Furthermore, the docking results highlight residues that may form specific contacts with the ligands. These contacts provide structural explanation for the importance of several chemical features that were obtained from the structure-activity analysis of known binders. With the exception of a single loop residue that might be perused in the future for obtaining subtype-specific regulation, the results suggest an identical TM-bundle binding site for hPKR1 and hPKR2. In addition, analysis of the intracellular regions highlights variable regions that may provide subtype specificity.
| S-EPMC3221691 | biostudies-literature
Project description:BACKGROUND:Cyclic peptide-based drug discovery is attracting increasing interest owing to its potential to avoid target protein depletion. In drug discovery, it is important to maintain the biostability of a drug within the proper range. Plasma protein binding (PPB) is the most important index of biostability, and developing a computational method to predict PPB of drug candidate compounds contributes to the acceleration of drug discovery research. PPB prediction of small molecule drug compounds using machine learning has been conducted thus far; however, no study has investigated cyclic peptides because experimental information of cyclic peptides is scarce. RESULTS:First, we adopted sparse modeling and small molecule information to construct a PPB prediction model for cyclic peptides. As cyclic peptide data are limited, applying multidimensional nonlinear models involves concerns regarding overfitting. However, models constructed by sparse modeling can avoid overfitting, offering high generalization performance and interpretability. More than 1000 PPB data of small molecules are available, and we used them to construct a prediction models with two enumeration methods: enumerating lasso solutions (ELS) and forward beam search (FBS). The accuracies of the prediction models constructed by ELS and FBS were equal to or better than those of conventional non-linear models (MAE?=?0.167-0.174) on cross-validation of a small molecule compound dataset. Moreover, we showed that the prediction accuracies for cyclic peptides were close to those for small molecule compounds (MAE?=?0.194-0.288). Such high accuracy could not be obtained by a simple method of learning from cyclic peptide data directly by lasso regression (MAE?=?0.286-0.671) or ridge regression (MAE?=?0.244-0.354). CONCLUSION:In this study, we proposed a machine learning techniques that uses low-dimensional sparse modeling to predict the PPB value of cyclic peptides computationally. The low-dimensional sparse model not only exhibits excellent generalization performance but also improves interpretation of the prediction model. This can provide common an noteworthy knowledge for future cyclic peptide drug discovery studies.
| S-EPMC6311893 | biostudies-other