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Showing 1871 to 1874 of 5186 entries
Published on February 26, 2021
Uncovering the mechanism of Ge-Gen-Qin-Lian decoction for treating ulcerative colitis based on network pharmacology and molecular docking verification.
Abstract: BACKGROUND: Ge-Gen-Qin-Lian Decoction (GGQLD), a traditional Chinese medicine (TCM) formula, has been widely used for ulcerative colitis (UC) in China, but the pharmacological mechanisms remain unclear. This research was designed to clarify the underlying pharmacological mechanism of GGQLD against UC. METHOD: In this research, a GGQLD-compound-target-UC network was constructed based on public databases to clarify the relationship between active compounds in GGQLD and potential targets. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses were performed to investigate biological functions associated with potential targets. A protein-protein interaction network was constructed to screen and evaluate hub genes and key active ingredients. Molecular docking was used to verify the activities of binding between hub targets and ingredients. RESULTS: Finally, 83 potential therapeutic targets and 118 corresponding active ingredients were obtained by network pharmacology. Quercetin, kaempferol, wogonin, baicalein, and naringenin were identified as potential candidate ingredients. GO and KEGG enrichment analyses revealed that GGQLD had anti-inflammatory, antioxidative, and immunomodulatory effects. The effect of GGQLD on UC might be achieved by regulating the balance of cytokines (e.g., IL-6, TNF, IL-1beta, CXCL8, CCL2) in the immune system and inflammation-related pathways, such as the IL-17 pathway and the Th17 cell differentiation pathway. In addition, molecular docking results demonstrated that the main active ingredient, quercetin, exhibited good affinity to hub targets. CONCLUSION: This research fully reflects the multicomponent and multitarget characteristics of GGQLD in the treatment of UC. Furthermore, the present study provided new insight into the mechanisms of GGQLD against UC.
Published on February 26, 2021
Androgen Receptor Binding Category Prediction with Deep Neural Networks and Structure-, Ligand-, and Statistically Based Features.
Abstract: Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine-learning classifiers and regressors and to evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks (DNNs) on user-defined physicochemically relevant features developed for this work outperforming graph convolutional, random forest, and large featurizations. The results show that these user-provided structure-, ligand-, and statistically based features and specific DNNs provided the best results as determined by AUC (0.87), MCC (0.47), and other metrics and by their interpretability and chemical meaning of the descriptors/features. In addition, the same features in the DNN method performed better than in a multivariate logistic model: validation MCC = 0.468 and training MCC = 0.868 for the present work compared to evaluation set MCC = 0.2036 and training set MCC = 0.5364 for the multivariate logistic regression on the full, unbalanced set. Techniques of this type may improve AR and toxicity description and prediction, improving assessment and design of compounds. Source code and data are available on github.
Published on February 26, 2021
Identification of Potential HCV Inhibitors Based on the Interaction of Epigallocatechin-3-Gallate with Viral Envelope Proteins.
Abstract: Hepatitis C is affecting millions of people around the globe annually, which leads to death in very high numbers. After many years of research, hepatitis C virus (HCV) remains a serious threat to the human population and needs proper management. The in silico approach in the drug discovery process is an efficient method in identifying inhibitors for various diseases. In our study, the interaction between Epigallocatechin-3-gallate, a component of green tea, and envelope glycoprotein E2 of HCV is evaluated. Epigallocatechin-3-gallate is the most promising polyphenol approved through cell culture analysis that can inhibit the entry of HCV. Therefore, various in silico techniques have been employed to find out other potential inhibitors that can behave as EGCG. Thus, the homology modelling of E2 protein was performed. The potential lead molecules were predicted using ligand-based as well as structure-based virtual screening methods. The compounds obtained were then screened through PyRx. The drugs obtained were ranked based on their binding affinities. Furthermore, the docking of the topmost drugs was performed by AutoDock Vina, while its 2D interactions were plotted in LigPlot+. The lead compound mms02387687 (2-[[5-[(4-ethylphenoxy) methyl]-4-prop-2-enyl-1,2,4-triazol-3-yl] sulfanyl]-N-[3(trifluoromethyl) phenyl] acetamide) was ranked on top, and we believe it can serve as a drug against HCV in the future, owing to experimental validation.
Published on February 25, 2021
Repurposing FDA-approved drugs to fight COVID-19 using in silico methods: Targeting SARS-CoV-2 RdRp enzyme and host cell receptors (ACE2, CD147) through virtual screening and molecular dynamic simulations.
Abstract: Background: Different approaches have been proved effective for combating the COVID-19 pandemic. Accordingly, in silico drug repurposing strategy, has been highly regarded as an accurate computational tool to achieve fast and reliable results. Considering SARS-CoV-2's structural proteins and their interaction the host's cell-specific receptors, this study investigated a drug repurposing strategy aiming to screen compatible inhibitors of FDA-approved drugs against viral entry receptors (ACE2 and CD147) and integral enzyme of the viral polymerase (RdRp). Methods: The study screened the FDA-approved drugs against ACE2, CD147, and RDRP by virtual screening and molecular dynamics (MD) simulation. Results: The results of this study indicated that five drugs with ACE2, four drugs with RDRP , and seven drugs with CD147 achieved the most favorable free binding energy (DeltaG < -10). This study selected these drugs for MD simulation investigation whose results demonstrated that ledipasvir with ACE2, estradiol benzoate with CD147, and vancomycin with RDRP represented the most favorable DeltaG. Also, paritaprevir and vancomycin have good binding energy with both targets (ACE2 and RdRp). Conclusions: Ledipasvir, estradiol benzoate, and vancomycin and paritaprevir are potentially suitable candidates for further investigation as possible treatments of COVID-19 and novel drug development.
Published on February 24, 2021
The tumor therapy landscape of synthetic lethality.
Abstract: Synthetic lethality is emerging as an important cancer therapeutic paradigm, while the comprehensive selective treatment opportunities for various tumors have not yet been explored. We develop the Synthetic Lethality Knowledge Graph (SLKG), presenting the tumor therapy landscape of synthetic lethality (SL) and synthetic dosage lethality (SDL). SLKG integrates the large-scale entity of different tumors, drugs and drug targets by exploring a comprehensive set of SL and SDL pairs. The overall therapy landscape is prioritized to identify the best repurposable drug candidates and drug combinations with literature supports, in vitro pharmacologic evidence or clinical trial records. Finally, cladribine, an FDA-approved multiple sclerosis treatment drug, is selected and identified as a repurposable drug for treating melanoma with CDKN2A mutation by in vitro validation, serving as a demonstrating SLKG utility example for novel tumor therapy discovery. Collectively, SLKG forms the computational basis to uncover cancer-specific susceptibilities and therapy strategies based on the principle of synthetic lethality.
Published on February 24, 2021
Targeting multiple conformations of SARS-CoV2 Papain-Like Protease for drug repositioning: An in-silico study.
Abstract: Papain-Like Protease (PLpro) is a key protein for SARS-CoV-2 viral replication which is the cause of the emerging COVID-19 pandemic. Targeting PLpro can suppress viral replication and provide treatment options for COVID-19. Due to the dynamic nature of its binding site loop, PLpro multiple conformations were generated through a long-range 1 micro-second molecular dynamics (MD) simulation. Clustering the MD trajectory enabled us to extract representative structures for the conformational space generated. Adding to the MD representative structures, X-ray structures were involved in an ensemble docking approach to screen the FDA approved drugs for a drug repositioning endeavor. Guided by our recent benchmarking study of SARS-CoV-2 PLpro, FRED docking software was selected for such a virtual screening task. The results highlighted potential consensus binders to many of the MD clusters as well as the newly introduced X-ray structure of PLpro complexed with a small molecule. For instance, three drugs Benserazide, Dobutamine and Masoprocol showed a superior consensus enrichment against the PLpro conformations. Further MD simulations for these drugs complexed with PLpro suggested the superior stability and binding of dobutamine and masoprocol inside the binding site compared to Benserazide. Generally, this approach can facilitate identifying drugs for repositioning via targeting multiple conformations of a crucial target for the rapidly emerging COVID-19 pandemic.
Published on February 24, 2021
Elucidating Solution Structures of Cyclic Peptides Using Molecular Dynamics Simulations.
Abstract: Protein-protein interactions are vital to biological processes, but the shape and size of their interfaces make them hard to target using small molecules. Cyclic peptides have shown promise as protein-protein interaction modulators, as they can bind protein surfaces with high affinity and specificity. Dozens of cyclic peptides are already FDA approved, and many more are in various stages of development as immunosuppressants, antibiotics, antivirals, or anticancer drugs. However, most cyclic peptide drugs so far have been natural products or derivatives thereof, with de novo design having proven challenging. A key obstacle is structural characterization: cyclic peptides frequently adopt multiple conformations in solution, which are difficult to resolve using techniques like NMR spectroscopy. The lack of solution structural information prevents a thorough understanding of cyclic peptides' sequence-structure-function relationship. Here we review recent development and application of molecular dynamics simulations with enhanced sampling to studying the solution structures of cyclic peptides. We describe novel computational methods capable of sampling cyclic peptides' conformational space and provide examples of computational studies that relate peptides' sequence and structure to biological activity. We demonstrate that molecular dynamics simulations have grown from an explanatory technique to a full-fledged tool for systematic studies at the forefront of cyclic peptide therapeutic design.
Published on February 24, 2021
Revealing the role of the human blood plasma proteome in obesity using genetic drivers.
Abstract: Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.
Published on February 23, 2021
Probing the Effect of Halogen Substituents (Br, Cl, and F) on the Non-covalent Interactions in 1-(Adamantan-1-yl)-3-arylthiourea Derivatives: A Theoretical Study.
Abstract: The effect of halogen substituents (X = Br, Cl, and F) on the crystal packing and intra- and intermolecular interactions in four adamantane-thiourea hybrid derivatives is investigated using different theoretical tools. The bromo and chloro derivatives exhibit 3D isostructurality as evident from lattice parameters, molecular conformation, and crystal packing. The density functional theory study suggests that the molecular conformation of the parent (unsubstituted) and fluoro derivatives exhibits a stable low energy anti-syn conformation. In contrast, bromo and chloro derivatives adopt stable and relatively high energy minima on their potential energy surfaces. Hirshfeld surface analysis reveals the effect of halogen substituents on the intermolecular contacts. The halogen atoms mainly reduce the contribution of H...H contacts toward crystal packing. PIXEL energy analysis indicates the strong dimer formed by N-H...S hydrogen bonds in all four structures. It also revealed that a vast number of H...H contacts observed in different dimers of these structures either presented along with other conventional interactions or solely stabilize the dimeric topology. The topological parameters for intermolecular interactions in these structures suggest an intermediate bonding character between shared and closed-shell interactions for N-H...S hydrogen bonds in the parent and chloro derivatives. In contrast, the N-H...S hydrogen bond in other structures is of a closed-shell interaction. Among four derivatives, the fluoro derivative is weakly packed in the solid state based on the PIXEL method's lattice energy calculation.
Published on February 23, 2021
Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks.
Abstract: The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene's popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear computational approaches for estimating gene popularity to then compare their relative importance. We find that roughly 25% of the studies are the result of a historical positive feedback, which we may think of as social reinforcement. Of the remaining features, gene family membership is the most indicative followed by disease relevance and finally regulatory pathway association. Disease relevance has been an important driver until the 1990s, after which the focus shifted to exploring every single gene. We also present a resource that allows one to study the impact of reinforcement, which may guide our research toward genes that have not yet received proportional attention.
Showing 1871 to 1874 of 5186 entries