Current computer-aided drug design最新文献

Introduction: The enteric protozoa, Giardia duodenalis (G. duodenalis), consists of eight distinct assemblages (A-H) with identical morphological characteristics and a direct life cycle. Successful axenic cultivation of this parasite is an important preliminary step for biological, drug resistance and phylogenetic studies. Moreover, G. duodenalis exhibits great genetic and biotypic diversity.

Aim: The current study aimed to evaluate in vitro culture and multilocus genotyping of G. duodenalis trophozoites obtained from human fecal samples in southwest Iran.

Methods: Thirty human fecal specimens containing G. duodenalis cysts were collected from Ahvaz city (southwest of Iran). The purification of cysts was carried out by the sucrose flotation technique. The cysts were inoculated in a modified TYI-S-33 medium and was daily monitored for the development and viability of trophozoites. After extracting DNA, gdh, bg and tpi genes were evaluated using molecular techniques (the semi-nested PCR for gdh gene and the nested PCR for tpi and bg genes). Eventually, the amplified fragments were sequenced and then, the phylogenetic tree was drawn.

Results: Of 30, the trophozoites were encysted from five samples. All three genes were detected in two cases of five samples using molecular techniques. The multilocus phylogenetic analysis demonstrated that all the two samples belonged to assemblage A and sub-assemblage AІІ.

Conclusion: Our findings indicated the presence of different numbers of trophozoites with variable development and survival rates in modified TYI-S-33 medium. Furthermore, the multilocus genotyping showed that these trophozoites belonged to assemblage A and sub-assemblage AІІ.

Background: Endostatin is an antiangiogenic compound with anticancer activity. The poor stability and low half-life of endostatin are the main barriers to the clinical use of this protein. Cell-penetrating peptides (CPPs) are extensively applied as carrier in the delivery of drugs and different therapeutic agents. Therefore, they can be proper candidates to improve endostatin delivery to the target cells.

Objective: In this study, we aim to computationally predict appropriate CPPs for the delivery of endostatin.

Methods: Potential appropriate CPPs for protein delivery were selected based on the literature. The main parameters for detection of best CPP-endostatin fusions, including stability, hydrophobicity, antigenicity, and subcellular localization, were predicted using ProtParam, VaxiJen, and DeepLoc-1.0 servers, respectively. The 3D structures of the best CPP-Endostatin fusions were modeled by the I-TASSER server. The predicted models were validated using PROCHECK, ERRAT, Verify3D and ProSA-Web servers. The best models were visualized by the PyMol molecular graphics system.

Results: Considering the principal parameters in the selection of best CPPs for endostatin delivery, endostatin fusions with four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, high stability and hydrophobicity, no antigenicity and extracellular localization were predicted as the best potential fusions for endostatin delivery. Four CPPs, including Cyt c-ss-MAP, TP-biot1, MPGα, and DPV1047, were predicted as the best potential candidates to improve endostatin delivery.

Conclusion: Application of these CPPs may overcome the limitation of endostatin therapeutic applications, including poor stability and low half-life. Subsequent experimental studies will contribute to verifying these computational results.

Background: Drug re-purposing is one of the cost-effective methods to establish novel therapeutics against many diseases. Established natural products are collected from databases and used to potentially screen them against HPV E6 protein, a critical viral protein.

Objective: This study aims to design potential small molecule inhibitors against HPV E6 protein using structure-based approaches. Ten natural anti-cancerous compounds (Apigenin, Baicalein, Baicalin, Ponicidin, Oridonin, Lovastatin, Triterpenoid, Narirutin, Rosmarinic Acid, and Xanthone) were selected by review of the literature.

Methods: These compounds were screened using Lipinski Rule of Five. Out of ten compounds, seven were found to satisfy Rule of five. Docking of these seven compounds was carried out using AutoDock software and corresponding Molecular Dynamics Simulations were performed by GROMACS.

Results: Among the seven compounds docked with the E6 target protein, six compounds showed lesser binding energy than the reference compound, Luteolin. The three-dimensional structures of E6 protein and the corresponding ligand complexes were visualised and analysed using PyMOL whereas the two-dimensional images of protein-ligand interactions were obtained by LigPlot⁺ software to study the specific interactions. ADME analysis using SwissADME software revealed that all the compounds except Rosmarinic acid have good gastrointestinal absorption and solubility characteristics while Xanthone and Lovastatin showed blood brain barrier penetration properties. Considering the binding energy and ADME analysis, Apigenin and Ponicidin are found to be most suitable for de novo designing of potential inhibitors against the HPV16 E6 protein.

Conclusion: Further, synthesis and characterization of these potential HPV16 E6 inhibitors will be carried out and their functional evaluation using cell culture-based assays will be undertaken.

Background: There has been a growing interest in discovering a viable drug for the new coronavirus (SARS-CoV-2) since the beginning of the pandemic. Protein-ligand interaction studies are a crucial step in the drug discovery process, as it helps us narrow the search space for potential ligands with high drug-likeness. Derivatives of popular drugs like Remdesivir generated through tools employing evolutionary algorithms are usually considered potential candidates. However, screening promising molecules from such a large search space is difficult. In a conventional screening process, for each ligand-target pair, there are time-consuming interaction studies that use docking simulations before downstream tasks like thermodynamic, kinetic, and electrostatic-potential evaluation.

Objective: This work aims to build a model based on deep learning applied over the graph structure of the molecules to accelerate the screening process for novel potential candidates for SARS-CoV-2 by predicting the binding energy of the protein-ligand complex.

Methods: In this work, 'Graph Convolutional Capsule Regression' (GCCR), a model which uses Capsule Neural Networks (CapsNet) and Graph Convolutional Networks (GCN) to predict the binding energy of a protein-ligand complex is being proposed. The model's predictions were further validated with kinetic and free energy studies like Molecular Dynamics (MD) for kinetic stability and MM/GBSA analysis for free energy calculations.

Results: The GCCR showed an RMSE value of 0.0978 for 81.3% of the concordance index. The RMSE of GCCR converged around the iteration of just 50 epochs scoring a lower RMSE than GCN and GAT. When training with Davis Dataset, GCCR gave an RMSE score of 0.3806 with a CI score of 87.5%.

Conclusion: The proposed GCCR model shows great potential in improving the screening process based on binding affinity and outperforms baseline machine learning models like DeepDTA, KronRLS, Sim- Boost, and other Graph Neural Networks (GNN) based models like Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT).

Aims and objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS.

Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs.

Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS.

Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Background: In vitro, the molecular docking method has been suggested for estimating the biological affinity of the pharmacophores with physiologically active compounds. It is the latter stage in molecular docking, and the docking scores are examined using the AutoDock 4.2 tool program. The chosen compounds can be evaluated for in vitro activity based on the binding scores, and the IC₅₀ values can be computed.

Objective: The purpose of this work was to create methyl isatin compounds as potential antidepressants, compute physicochemical characteristics, and carry out docking analysis.

Methods: The protein data bank of the RCSB (Research Collaboratory for Structural Bioinformatics) was used to download the PDB structures of monoamine oxidase (PDB ID: 2BXR) and indoleamine 2,3-dioxygenase (PDB ID: 6E35). Based on the literature, methyl isatin derivatives were chosen as the lead chemicals. By determining their IC₅₀ values, the chosen compounds were tested for in vitro anti-depressant activity.

Results: The binding scores for the interactions of SDI 1 and SD 2 with indoleamine 2,3 dioxygenase were found to be -10.55 kcal/mol and -11.08 kcal/mol, respectively, while the scores for their interactions with monoamine oxidase were found to be -8.76 kcal/mol and -9.28 kcal/mol, respectively, using AutoDock 4.2. The relationship between biological affinity and pharmacophore electrical structure was examined using the docking technique. The chosen compounds were tested for their ability to inhibit MAO, and the IC₅₀ values for each were found to be 51.20 and 56, respectively.

Conclusion: This investigation has identified many novel and effective MAO-A inhibitors from the family of chemicals known as methyl isatin derivatives. Lead optimization was applied to the SDI 1 and SDI 2 derivatives. The superior bioactivity, pharmacokinetic profile, BBB penetration, pre-ADMET profiles, such as HIA (human intestinal absorption) and MDCK (Madin-Darby canine kidney), plasma protein binding, toxicity assessment, and docking outcomes, have been obtained. According to the study, synthesised isatin 1 and SDI 2 derivatives exhibited a stronger MAO inhibitory activity and effective binding energy, which may help prevent stress-induced depression and other neurodegenerative disorders caused by a monoamine imbalance.

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.

Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.

Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.

Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.

Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Introduction: Sepsis is a state of the systemic inflammatory response of the host induced by infection, frequently affecting numerous organs and producing varied degrees of damage. The most typical consequence of sepsis is sepsis-associated acute kidney injury(SA-AKI). Xuebijing is developed based on XueFuZhuYu Decoction. Five Chinese herbal extracts, including Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix, make up the majority of the mixture. It has properties that are anti-inflammatory and anti-oxidative stress. Xuebijing is an effective medication for the treatment of SA-AKI, according to clinical research. But its pharmacological mechanism is still not completely understood.

Methods: First, the composition and target information of Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix were collected from the TCMSP database, while the therapeutic targets of SA-AKI were exported from the gene card database. To do a GO and KEGG enrichment analysis, we first screened the key targets using a Venn diagram and Cytoscape 3.9.1. To assess the binding activity between the active component and the target, we lastly used molecular docking.

Results: For Xuebijing, a total of 59 active components and 267 corresponding targets were discovered, while for SA-AKI, a total of 1,276 targets were connected. There were 117 targets in all that was shared by goals for active ingredients and objectives for diseases. The TNF signaling pathway and the AGE-RAGE pathway were later found to be significant pathways for the therapeutic effects of Xuebijing by GO analysis and KEGG pathway analysis. Quercetin, luteolin, and kaempferol were shown to target and modulate CXCL8, CASP3, and TNF, respectively, according to molecular docking results.

Conclusion: This study predicts the mechanism of action of the active ingredients of Xuebijing in the treatment of SA-AKI, which provides a basis for future applications of Xuebijing and studies targeting the mechanism.

Background: Leptin is predominant in regulating body weight by stimulating energy expenditure through its neuronal action in the brain. Moreover, it is projected to adipose tissue and induces adipocyte browning by activating the β3-adrenergic receptor (β3AR). However, the expression of leptin receptor (Lep-R) and β3AR in people with obesity is downregulated.

Aim: We hypothesized that Hibiscus sabdariffa Linn. extract (HSE) would increase hypothalamus arcuate nucleus (ARC) Lep-R and white adipose tissue (WAT) β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as activators of Lep-R and β3AR by an in-silico experiment.

Methods: Twenty-four male Sprague-Dawley rats were divided into four groups: Control (standard food), DIO (high-fat diet), DIO-Hib200 (HFD+HSE 200 mg/kg BW), and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE was administered orally for five weeks, once a day.

Results: HSE administration significantly (p <0,05) increased the ARC Lep-R expression. The Lee index significantly decreased to the normal range (≤ 310) with p <0,001 for DIO-Hib200 and p <0,01 for DIO-Hib400. Among 39 bioactive compounds, 5-O-caffeoyl shikimic acid exhibited high free binding scores (-8,63) for Lep-R, and myricetin_3_arabinogalactoside had high free binding scores (-9,39) for β3AR. These binding predictions could activate Lep-R and β3AR.

Conclusion: This study highlights that HSE could be a potential therapeutic target for obesity by increasing LepR mRNA and leptin sensitivity, enhancing energy expenditure, and reducing obesity.

Objective: IBD is a chronic idiopathic gut condition characterised by recurring and remitting inflammation of the colonic mucosal epithelium. Benzimidazole is a prominent and attractive heterocyclic compound with diverse actions. Although seven locations in the benzimidazole nucleus can be changed with a number of chemical entities for biological activity, benzimidazole fused with a phenyl ring has caught our interest.

Methods: To find and optimize novel 1-H phenyl benzimidazole compounds with favorable physicochemical features and drug-like characteristics for the treatment of IBD, in-silico studies and in-vitro approach were being used to identify and optimize these derivatives as potent inhibitors of IL-23 mediated inflammatory signaling pathway.

Results: All six compounds exhibit favorable drug-like properties with good intestinal absorption properties. Its high affinity for the target JAK and TYK, which is thought to be a key immunological signaling cascade in the pathophysiology of IBD, is revealed by docking studies.

Conclusion: Because of their effects on decreasing iNOS-derived NO release and IL-23-mediated immune signaling by decreasing COX-2 and LOX activity, it's conceivable that the compounds CS3 and CS6 are better options for the treatment of IBD based on in-vitro cell line investigations.