Current computer-aided drug design最新文献

Background: Vibrio cholerae, the causative agent of cholera, has been responsible for global epidemics and many other problems over the centuries. It is one of the main public health issues in less-developed and developing countries and is considered one of the deadliest infectious agents. Therefore, precise and susceptible detection of V. cholerae from environmental and biological samples is critical. Aptamers provide a rapid, sensitive, highly specific, and inexpensive alternative to traditional methods.

Objective: The present study develops a new protocol inspired by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) to implement an in silico aptamer selection against V. cholerae, which can also be employed in the case of other pathogenic microorganisms.

Methods: First, we built an oligonucleotide pool and screened it based on the secondary structure. Following that, we modeled the tertiary structures of filtered sequences and performed RNAprotein dockings to assess binding affinities between RNA sequences and Outer Membrane Protein U (OmpU), an effective marker in distinguishing epidemic strains of V. cholerae, which constitute up to 60% of the total outer membrane protein. Finally, we used molecular dynamics simulation to validate the results.

Results: Three sequences (ChOmpU^apta) were proposed as final aptameric candidates. Analysis of the top-ranked docking results revealed that these candidate aptamers bound to all subunits of OmpU at the extracellular side with high affinity. Moreover, ChOmpU^apta-3 and ChOmpU^apta-2 were fully stable and formed strong bonds under dynamic conditions.

Conclusion: We propose incorporating these candidate sequences into aptasensors for V. cholerae detection.

Background: Hydrazone and its azomethine (-NHN=CH-) derivatives are widely reported for their immense pharmacological potential. They have also been reported to possess potent anti-tuberculosis, anti-malarial, anti-inflammatory, and anti-oxidant activities. Considering their pharmacological significance, we herein synthesized a set of 10 hydrazones (1S-10S) using green, biodegradable chitosan and HCl as catalyst.

Methods: All synthesized compounds were characterized using modern spectroscopic techniques, including Nuclear magnetic resonance, 1H-/13C-NMR; Fourier transform infrared spectroscopy (FT-IR); Ultraviolet-visible spectroscopy; Mass spectrometry (m/z), etc. Synthesized compounds were in silico screened using molecular docking, dynamics, pharmacokinetics, theoretical properties, and common pharmacophore analysis. Moreover, we also subjected all compounds to DPPH radical scavenging assay, protein denaturation assay, Trypan Blue assay for cell viability assessments, β-hematin assay for hemozoin inhibition analysis and standard antimicrobial analysis.

Results: Our results suggested that the synthesized compound 2S had high potency against studied microbial strains (minimum MIC = 3.12 μg/mL). Our antioxidant analysis for 1S-10S revealed that our compounds had radical scavenging effects ranging from 25.1-80.3 %. Compounds 2S exhibited % cell viability of 68.92% (at 100 μg concentration of sample), while the same compound retained anti-inflammatory % inhibition at 62.16 %. Compound 2S was obtained as the best docked molecule, with a docking score of -5.32 Kcal/mol with target pdb id: 1d7u protein. Molecular dynamics simulation and normal mode analysis for 100 ns for 1d7u:2S retained good stability. Finally, in silico pharmacokinetics, theoretical properties and pharmacophoric features were assessed.

Conclusion: In summary, synthesized hydrazone exhibited a good biological profile according to in silico and in vitro studies. However, further in vivo studies are required that may shed more insights on its potencies.

Aims: Detecting and classifying a brain tumor amid a sole image can be problematic for doctors, although improvements can be made with medical image fusions.

Background: A brain tumor develops in the tissues surrounding the brain or the skull and has a major impact on human life. Primary tumors begin within the brain, whereas secondary tumors, identified as brain metastasis tumors, are generated outside the brain.

Objective: This paper proposes hybrid fusion techniques to fuse multi-modal images. The evaluations are based on performance metrics, and the results are compared with conventional ones.

Methods: In this paper, pre-processing is done considering enhancement methods like Binarization, Contrast Stretching, Median Filter, & Contrast Limited Adaptive Histogram Equalization (CLAHE). Authors have proposed three techniques, PCA-DWT, DCT-PCA, and Discrete ComponentWaveletCosine Transform (DCWCT), which were used to fuse CT-MR images of brain tumors.

Results: The different features were evaluated from the fused images, which were classified using various machine learning approaches. Maximum accuracy of 97.9% and 93.5% is obtained using DCWCT for Support Vector Machine (SVM) and k Nearest Neighbor (kNN), respectively, considering the combination of both feature's shape & Grey Level Difference Statistics. The model is validated using another online dataset.

Conclusion: It has been observed that the classification accuracy for detecting cerebrovascular disease is better after employing the proposed image fusion technique.

Introduction: Cucurbitacins are one of the most important components of Ecballium elaterium. Among the cucurbitacins, Cucurbitacin E was the first to be isolated. This study focused on screening the anticancer and insecticidal potential of Cucurbitacin E by the in-vitro, invivo, and in-silico methods.

Methods: In the study, toxicity analysis of Cucurbitacin E was determined on HeLa, Caco 2 cancer cell lines and D. melanogaster. While the expression levels of the BAD, BCL-2, AKT-1 and H-purine genes of cancer cell lines were determined, the CG15530, BUFFY, AKT-1 and Purine genes of D. melanogaster were determined by RT-PCR. Besides, molecular docking and dynamic properties of Cucurbitacin E with human and insectoid enzymes were presented in silico.

Results: The IC₅₀ value of Cucurbitacin E in the HeLa ovarian and Caco 2 colon cancer cell lines was determined to be 42 ug/ml and 85 ug/ml, respectively. The LC₅₀ and LC₉₉ doses for fruit flies were determined to be 47,693 μg/ml and 133,251 μg/ml, respectively. Gene expression analysis revealed that Cucurbitacin E showed the greatest effect on Purine and AKT-1 genes in D. melanogaster. We analyzed all genes by Western blot but did not detect significant changes in genes other than H-purine. In silico studies revealed that the Purine protein of D. melanogaster had the highest bonding energy with Cucurbitacin E as a ligand. Similarly, Cucurbitacin E showed great affinity towards H-purine (-10.2 kcal/mol). Molecular dynamics simulation studies were also performed to determine the stability of the dynamic process.

Conclusion: As a result of our in vivo, in vitro and bioinformatic analyzes, it has been seen that Cucurbitacin E is effective against the cancer types and model insects studied.

Background: Phosphodiesterase type 5 (PDE5), exclusively specific for cyclic guanidine monophosphate (cGMP), a potential target for the therapy of various diseases, and PDE5 inhibitors could be used as a treatment for erectile dysfunction (ED) or chronic pulmonary hypertension.

Objective: In the present study, we carried out an integrated computer-aided virtual screening technique against the natural products in the ZINC database to discover potential inhibitors of PDE5.

Methods: Pharmacophore, molecular docking and ADMET (Absorption, distribution, metabolism, excretion and toxicity) properties filtration were used to select the PDE5 inhibitors with the best binding affinities and drug-like properties. The binding modes of PDE5 inhibitors were investigated, and these complexes' stabilities were explored by molecular dynamic simulations and MM/GBSA free energy calculations.

Results: Two natural compounds (Z171 and Z283) were identified and may be used as a critical starting point for the development of novel PDE5 inhibitors. The MM/GBSA free energy decomposition analysis quantitatively analyzed the importance of hydrophobic interaction in PDE5- ligands binding.

Conclusion: In this study, we identified two novel natural compounds from the ZINC database to effectively inhibit PDE5 through virtual screening. The novel scaffolds of these compounds can be used as the starting templates in the drug design of PDE5 inhibitors with good pharmacokinetic profiles. These results may promote the de novo design of new compounds against PDE5.

Background: The south Indian Telugu states will celebrate a new year called 'Ugadi' which is a south Indian traditional festival. The ingredients used in ugadi pachadi have often also been used in food as well as traditional Ayurveda and Siddha medicinal preparations. Coronaviruses (CoVs) are a diverse family of enveloped positive-sense single-stranded RNA viruses which can infect humans and have the potential to cause large-scale outbreaks.

Objective: Considering the benefits of ugadi pachadi, we investigated the binding modes of various phytochemical constituents reported from its ingredients against five targets of SARS-CoV-2.

Methods: Flexible-ligand docking simulations were achieved through AutoDock version 1.5.6. Following 50ns of molecular dynamics simulation using GROMACS 2018.1 software and binding free energy (ΔG_bind) of the protein-ligand complexes were calculated using the g_mmpbsa tool. ADME prediction was done using Qikprop of Schrodinger.

Results: From the molecular docking and MM/PBSA results compound Eriodictin exhibited the highest binding energy when complexed with nucleocapsid N protein (6M3M) (-6.8 kcal/mol, - 82.46 kJ/mol), bound SARS-CoV-2-hACE2 complex (6M0J) (-7.4 kcal/mol, -71.10 kJ/mol) and Mpro (6XR3) (-8.6 kcal/mol, -140.21 kJ/mol). Van der Waal and electrostatic energy terms highly favored total free energy binding.

Conclusion: The compounds Eriodictin, Vitexin, Cycloart-3, 24, 27-triol, Agigenin, Mangiferin, Mangiferolic acid, Schaftoside, 27-Hydroxymangiferonic acid, Quercetin, Azadirachtol, Cubebin, Isomangiferin, Isoquercitrin, Malicarpin, Orientin and procyanidin dimer exhibited satisfactory binding energy values when compared with standard molecules. The further iterative optimization of high-ranked compounds following validation by in vitro and in vivo techniques assists in discovering therapeutic anti-SARS-CoV-2 molecules.

Background: Human parainfluenza viruses type 3 (HPIV-3) through bronchiolitis and pneumonia is a common cause of lower respiratory tract infections. It is the main cause of hospitalization of infants and young children and also one of the main causes of morbidity and mortality in immuno-compromised and transplant patients. Despite many efforts, there is currently no specific anti-HPIV-3 drug or approved vaccine to prevent and control the virus. Identification of HPIV-3 epitopes with the capability of binding to human leukocyte antigen (HLA) class II molecules can be helpful in designing new vaccine candidates against HPIV-3 infection, and also can be useful for the in vitro stimulation and proliferation of HPIV-3-specific T cells for transplant and immunocompromised patients.

Objective: To predict and comprehensively evaluate CD4⁺T cell epitope (HLA-II binders) from four main HPIV-3 antigens.

Methods: In the present work, we predicted and comprehensively evaluated CD4⁺T cell epitope (HLA-II binders) from four main HPIV-3 antigens, including fusion protein (F), hemagglutininneuraminidase (HN), nucleocapsid (N) and matrix (M) proteins using bio- and immunoinformatics software. The toxicity, allergenicity, Blast screening and population coverage of the predicted epitopes were evaluated. The binding ability of the final selected epitopes was evaluated via a docking study.

Results: After several filtering steps, including blast screening, toxicity and allergenicity assay, population coverage and docking study, 9 epitopes were selected as candidate epitopes. The selected epitopes showed high population coverage and docking studies revealed a significantly higher binding affinity for the final epitopes in comparison with the negative control peptides.

Conclusion: The final selected epitopes could be useful in designing vaccine candidates and for the treatment of immune-compromised individuals and patients with transplantation.

Background: To date, very few small drug molecules are used for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has been discovered since the epidemic commenced in November 2019. SARS-CoV-2 RdRp and spike protein are essential targets for drug development amidst whole variants of coronaviruses.

Objective: This study aims to discover and recognize the most effective and promising small molecules against SARS-CoV-2 RNA-dependent RNA polymerase (RdRp) and spike protein targets through molecular docking screening of 39 phytochemicals from five different Ayurveda medicinal plants.

Methods: The phytochemicals were downloaded from PubChem, and SARS-CoV-2 RdRp and spike protein were taken from the protein data bank. The molecular interactions, binding energy, and ADMET properties were analyzed.

Results: Molecular docking analysis identified some phytochemicals, oleanolic acid, friedelin, serratagenic acid, uncinatone, clemaphenol A, sennosides B, trilobine and isotrilobine from ayurvedic medicinal plants possessing greater affinity against SARS-CoV-2-RdRp and spike protein targets. Two molecules, namely oleanolic acid and sennosides B, with low binding energies, were the most promising. Furthermore, based on the docking score, we carried out MD simulations for the oleanolic acid and sennosides B-protein complexes.

Conclusion: Molecular ADMET profile estimation showed that the docked phytochemicals were safe. The present study suggested that active phytochemicals from medicinal plants could inhibit RdRp and spike protein of SARS-CoV-2.

Background: Drug discovery requires the use of hybrid technologies for the discovery of new chemical substances. One of those interesting strategies is QSAR via applying an artificial intelligence system that effectively predicts how chemical alterations can impact biological activity via in-silico.

Aim: Our present study aimed to work on a trending machine learning approach with a new opensource data analysis python script for the discovery of anticancer lead via building the QSAR model by using 53 compounds of thiazole derivatives.

Methods: A python script has been executed with 53 small thiazole chemicals using Google collaboratory interface. A total of 82 CDK molecular descriptors were downloaded from "chemdes" web server and used for our study. After training the model, we checked the model performance via cross-validation of the external test set.

Results: The generated QSAR model afforded the ordinary least squares (OLS) regression as R² = 0.542, F=8.773, and adjusted R² (Q2) =0.481, std. error = 0.061, reg.coef_ developed were of, - 0.00064 (PC1), -0.07753 (PC2), -0.09078 (PC3), -0.08986 (PC4), 0.05044 (PC5), and reg.intercept_ of 4.79279 developed through stats models, formula module. The performance of test set prediction was done by multiple linear regression, support vector machine, and partial least square regression classifiers of sklearn module, which generated the model score of 0.5424, 0.6422 and 0.6422 respectively.

Conclusion: Hence, we conclude that the R2values (i.e. the model score) obtained using this script via three diverse algorithms were correlated well and there is not much difference between them and may be useful in the design of a similar group of thiazole derivatives as anticancer agents.

Background: Silymarin is a flavonolignan extracted from Silybum marianum with various therapeutic applications. Many studies have focused on improving the bioavailability of silymarin due to its wide range of efficacy and low bioavailability. Chitosan, a naturally occurring polymeric substance, has a strong reputation for increasing the solubility of poorly soluble compounds.

Objective: This study used artificial neural networks (ANNs) to measure the effects of pH, chitosan to silymarin ratio, chitosan to tripolyphosphate ratio, and stirring time on the loading efficiency of silymarin into chitosan particles.

Methods: A model was developed to investigate the interactions between input factors and silymarin loading efficiency. The DPPH method was utilized to determine the antioxidant activity of an optimized formula and pure raw materials.

Results: According to the outcome of the ANN model, pH and the chitosan to silymarin ratio demonstrated significant effects on loading efficiency. In addition, increased stirring time decreased silymarin loading, whereas the chitosan-to-tripolyphosphate ratio showed a negligible effect on loading efficiency.

Conclusion: Maximum loading efficiency occurred at a pH of approximately~5. Moreover, silymarin- loaded chitosan particles with a lower IC₅₀ value (36.17 ± 0.02 ppm) than pure silymarin (165.04 ± 0.07 ppm) demonstrated greater antioxidant activity.