scholarly journals Hits Discovery on the Androgen Receptor: In Silico Approaches to Identify Agonist Compounds

Cells ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1431
Author(s):  
Réau ◽  
Lagarde ◽  
Zagury ◽  
Montes
Keyword(s):  
Androgen Receptor ◽  
In Silico ◽  
High Performance ◽  
Prediction Models ◽  
Endocrine System ◽  
Pharmacophore Modeling ◽  
Activity Data ◽  
Starting Point ◽  
Exogenous Compounds ◽  
Modeling Strategy

The androgen receptor (AR) is a transcription factor that plays a key role in sexual phenotype and neuromuscular development. AR can be modulated by exogenous compounds such as pharmaceuticals or chemicals present in the environment, and particularly by AR agonist compounds that mimic the action of endogenous agonist ligands and whether restore or alter the AR endocrine system functions. The activation of AR must be correctly balanced and identifying potent AR agonist compounds is of high interest to both propose treatments for certain diseases, or to predict the risk related to agonist chemicals exposure. The development of in silico approaches and the publication of structural, affinity and activity data provide a good framework to develop rational AR hits prediction models. Herein, we present a docking and a pharmacophore modeling strategy to help identifying AR agonist compounds. All models were trained on the NR-DBIND that provides high quality binding data on AR and tested on AR-agonist activity assays from the Tox21 initiative. Both methods display high performance on the NR-DBIND set and could serve as starting point for biologists and toxicologists. Yet, the pharmacophore models still need data feeding to be used as large scope undesired effect prediction models.

scholarly journals In Silico Analysis of Ixodid Tick Aqauporin-1 Protein as a Candidate Anti-Tick Vaccine Antigen

2019 ◽  
Author(s):  
Christian Ndekezi ◽  
Joseph Nkamwesiga ◽  
Sylvester Ochwo ◽  
Magambo Phillip Kimuda ◽  
Frank Norbert Mwiine ◽  
...  
Keyword(s):  
In Silico ◽  
Prediction Models ◽  
Vaccine Development ◽  
Sequence Similarity ◽  
Vaccine Antigen ◽  
Systematic Research ◽  
Motif Analysis ◽  
Peptide Motifs ◽  
Starting Point ◽  
Tick Vaccine

AbstractTicks are arthropod vectors of pathogens of both Veterinary and Public health importance. Ticks are largely controlled by acaricide application. However, acaricide efficacy is hampered by high cost, the need for regular application and selection of multi-acaricide resistant tick populations. In light of this, future tick control approaches are poised to rely on integration of rational acaricide application and other methods such as vaccination. To contribute to systematic research-guided efforts to produce anti-tick vaccines, we carried out an in silico tick Aquaporin-1 protein (AQP1) analysis to identify unique tick AQP1 peptide motifs that can be used in future peptide anti-tick vaccine development. We used multiple sequence alignment (MSA), motif analysis, homology modeling, and structural analysis to identify unique tick AQP1 peptide motifs. BepiPred, Chou & Fasman-Turn, Karplus & Schulz Flexibility and Parker-Hydrophilicity prediction models were used to asses these motifs’ abilities to induce antibody mediated immune responses. Tick AQP1 (MK334178) protein homology was largely similar to the bovine AQP1 (PDB:1J4N) (23% sequence similarity; Structural superimposition RMS=1.475). The highest similarities were observed in the transmembrane domains while differences were observed in the extra and intra cellular protein loops. Two unique tick AQP1 (MK334178) motifs, M7 (residues 106-125, p=5.4e-25) and M8 (residues 85-104, p=3.3e-24) were identified. These two motifs are located on the extra-cellular AQP1 domain and showed the highest Parker-Hydrophilicity prediction immunogenic scores of 1.153 and 2.612 respectively. The M7 and M8 motifs are a good starting point for the development of potential peptide-based anti-tick vaccine. Further analyses such as in vivo immunization assays are required to validate these findings.

scholarly journals Insights into the molecular properties underlying antibacterial activity of prenylated (iso)flavonoids against MRSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sylvia Kalli ◽  
Carla Araya-Cloutier ◽  
Jos Hageman ◽  
Jean-Paul Vincken
Keyword(s):  
Prediction Models ◽  
External Validation ◽  
Qsar Model ◽  
Prediction Errors ◽  
Activity Data ◽  
Gram Positive Bacteria ◽  
Level Of Activity ◽  
Formal Charge ◽  
Validation Set

AbstractHigh resistance towards traditional antibiotics has urged the development of new, natural therapeutics against methicillin-resistant Staphylococcus aureus (MRSA). Prenylated (iso)flavonoids, present mainly in the Fabaceae, can serve as promising candidates. Herein, the anti-MRSA properties of 23 prenylated (iso)flavonoids were assessed in-vitro. The di-prenylated (iso)flavonoids, glabrol (flavanone) and 6,8-diprenyl genistein (isoflavone), together with the mono-prenylated, 4′-O-methyl glabridin (isoflavan), were the most active anti-MRSA compounds (Minimum Inhibitory Concentrations (MIC) ≤ 10 µg/mL, 30 µM). The in-house activity data was complemented with literature data to yield an extended, curated dataset of 67 molecules for the development of robust in-silico prediction models. A QSAR model having a good fit (R2adj 0.61), low average prediction errors and a good predictive power (Q2) for the training (4% and Q2LOO 0.57, respectively) and the test set (5% and Q2test 0.75, respectively) was obtained. Furthermore, the model predicted well the activity of an external validation set (on average 5% prediction errors), as well as the level of activity (low, moderate, high) of prenylated (iso)flavonoids against other Gram-positive bacteria. For the first time, the importance of formal charge, besides hydrophobic volume and hydrogen-bonding, in the anti-MRSA activity was highlighted, thereby suggesting potentially different modes of action of the different prenylated (iso)flavonoids.

In Silico Identification and in Vitro Validation of Potential Cholestatic Compounds through 3D Ligand-Based Pharmacophore Modeling of BSEP Inhibitors

2014 ◽  
Vol 27 (5) ◽  
pp. 873-881 ◽  
Author(s):  
Tina Ritschel ◽  
Susanne M. A. Hermans ◽  
Marieke Schreurs ◽  
Jeroen J. M. W. van den Heuvel ◽  
Jan B. Koenderink ◽  
...  
Keyword(s):  
In Silico ◽  
Pharmacophore Modeling ◽  
In Silico Identification

The search of CAR, AhR, ESRs binding sites in promoters of intronic and intergenic microRNAs

2018 ◽  
Vol 16 (01) ◽  
pp. 1750029 ◽  
Author(s):  
Vladimir Y. Ovchinnikov ◽  
Denis V. Antonets ◽  
Lyudmila F. Gulyaeva
Keyword(s):  
Transcriptional Activation ◽  
Binding Sites ◽  
In Silico ◽  
Target Genes ◽  
Nuclear Translocation ◽  
Regulation Of Gene Expression ◽  
Experimental Studies ◽  
Transcriptional Level ◽  
Aryl Hydrocarbon ◽  
Exogenous Compounds

MicroRNAs (miRNAs) play important roles in the regulation of gene expression at the post-transcriptional level. Many exogenous compounds or xenobiotics may affect microRNA expression. It is a well-established fact that xenobiotics with planar structure like TCDD, benzo(a)pyrene (BP) can bind aryl hydrocarbon receptor (AhR) followed by its nuclear translocation and transcriptional activation of target genes. Another chemically diverse group of xenobiotics including phenobarbital, DDT, can activate the nuclear receptor CAR and in some cases estrogen receptors ESR1 and ESR2. We hypothesized that such chemicals can affect miRNA expression through the activation of AHR, CAR, and ESRs. To prove this statement, we used in silico methods to find DRE, PBEM, ERE potential binding sites for these receptors, respectively. We have predicted AhR, CAR, and ESRs binding sites in 224 rat, 201 mouse, and 232 human promoters of miRNA-coding genes. In addition, we have identified a number of miRNAs with predicted AhR, CAR, and ESRs binding sites that are known as oncogenes and as tumor suppressors. Our results, obtained in silico, open a new strategy for ongoing experimental studies and will contribute to further investigation of epigenetic mechanisms of carcinogenesis.

scholarly journals Revisiting the CompCars Dataset for Hierarchical Car Classification: New Annotations, Experiments, and Results

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 596
Author(s):  
Marco Buzzelli ◽  
Luca Segantin
Keyword(s):  
Real World ◽  
High Performance ◽  
Ad Hoc ◽  
Future Research ◽  
Levels Of Detail ◽  
Excellent Starting Point ◽  
Starting Point ◽  
Hierarchical Nature ◽  
Multiple Levels ◽  
Entire Dataset

We address the task of classifying car images at multiple levels of detail, ranging from the top-level car type, down to the specific car make, model, and year. We analyze existing datasets for car classification, and identify the CompCars as an excellent starting point for our task. We show that convolutional neural networks achieve an accuracy above 90% on the finest-level classification task. This high performance, however, is scarcely representative of real-world situations, as it is evaluated on a biased training/test split. In this work, we revisit the CompCars dataset by first defining a new training/test split, which better represents real-world scenarios by setting a more realistic baseline at 61% accuracy on the new test set. We also propagate the existing (but limited) type-level annotation to the entire dataset, and we finally provide a car-tight bounding box for each image, automatically defined through an ad hoc car detector. To evaluate this revisited dataset, we design and implement three different approaches to car classification, two of which exploit the hierarchical nature of car annotations. Our experiments show that higher-level classification in terms of car type positively impacts classification at a finer grain, now reaching 70% accuracy. The achieved performance constitutes a baseline benchmark for future research, and our enriched set of annotations is made available for public download.

scholarly journals Application of feed forward and recurrent neural networks in simulation of left ventricular mechanics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Yaghoub Dabiri ◽  
Alex Van der Velden ◽  
Kevin L. Sack ◽  
Jenny S. Choy ◽  
Julius M. Guccione ◽  
...  
Keyword(s):  
Real Time ◽  
Material Properties ◽  
In Silico ◽  
Prediction Models ◽  
Heart Function ◽  
Active Material ◽  
Experimental Studies ◽  
Left Ventricular ◽  
Feed Forward ◽  
Ventricular Mechanics

AbstractAn understanding of left ventricle (LV) mechanics is fundamental for designing better preventive, diagnostic, and treatment strategies for improved heart function. Because of the costs of clinical and experimental studies to treat and understand heart function, respectively, in-silico models play an important role. Finite element (FE) models, which have been used to create in-silico LV models for different cardiac health and disease conditions, as well as cardiac device design, are time-consuming and require powerful computational resources, which limits their use when real-time results are needed. As an alternative, we sought to use deep learning (DL) for LV in-silico modeling. We used 80 four-chamber heart FE models for feed forward, as well as recurrent neural network (RNN) with long short-term memory (LSTM) models for LV pressure and volume. We used 120 LV-only FE models for training LV stress predictions. The active material properties of the myocardium and time were features for the LV pressure and volume training, and passive material properties and element centroid coordinates were features of the LV stress prediction models. For six test FE models, the DL error for LV volume was 1.599 ± 1.227 ml, and the error for pressure was 1.257 ± 0.488 mmHg; for 20 LV FE test examples, the mean absolute errors were, respectively, 0.179 ± 0.050 for myofiber, 0.049 ± 0.017 for cross-fiber, and 0.039 ± 0.011 kPa for shear stress. After training, the DL runtime was in the order of seconds whereas equivalent FE runtime was in the order of several hours (pressure and volume) or 20 min (stress). We conclude that using DL, LV in-silico simulations can be provided for applications requiring real-time results.

Abstract P437: Deep Learning-based Models For Complete Atrioventricular Block Heart Rhythm Analysis

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Dahim Choi ◽  
Nam Kyun Kim ◽  
Young H Son ◽  
Yuming Gao ◽  
Christina Sheng ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Atrioventricular Block ◽  
Prediction Models ◽  
Pacemaker Implantation ◽  
Heart Rhythm ◽  
High Sensitivity ◽  
Ventricular Myocardium ◽  
True Positive Rate ◽  
Starting Point

Atrioventricular block (AVB), caused by impairment in the heart conduction system, presents extreme diversity and is associated with other complications. Only half of AVB patients require a permanent pacemaker, and the process determining the pacemaker implantation is associated with an increase in cost and patient morbidity and mortality. Thus, there is a need for models capable of accurately identifying transient or reversible causes for conduction disturbances and predicting the patient risks and the necessity of a pacemaker. Deep learning (DL) is brought to the forefront due to its prediction accuracy, and the DL-based electrocardiogram (ECG) analysis can be a breakthrough to analyze a massive amount of data. However, the current DL models are unsuitable for AVB-ECG, where the P waves are decoupled from the QRS/T waves, and a black-box nature of the DL-based model lowers the credibility of prediction models to physicians. Here, we present a real-time-capable DL-based algorithm that can identify AVB-ECG waves and automate AVB phenotyping for arrhythmogenic risk assessment. Our algorithm can analyze unformatted ECG records with abnormal patterns by integrating the two representative DL algorithms: convolutional neural networks (CNN) and recurrent neural networks (RNN). This hybrid CNN/RNN network can memorize local patterns, spatial hierarchies, and long-range temporal dependencies of ECG signals. Furthermore, by integrating parameters derived from dimension reduction analysis and heart rate variability into the hybrid layers, the algorithm can capture the P/QRS/T-specific morphological and temporal features in ECG waveforms. We evaluated the algorithm using the six AVB porcine models, where TBX18, a pacemaker transcription factor, was transduced into the ventricular myocardium to form a biological pacemaker, and an additional electronic pacemaker was transplanted as a backup pacemaker. We achieved high sensitivity (95% true positive rate) and quantified the potential risks of various pathological ECG patterns. This study may be a starting point in conducting both retrospective and prospective patient studies and will help physicians understand its decision-making workflow and find the incorrect recommendations for AVB patients.

In Silico Modelling of Acceptor Materials by End-capped and π-linker modifications for High-Performance Organic Solar Cells: Estimated PCE > 18%

2021 ◽  
pp. 113555
Author(s):  
Muhammad Yasir Mehboob ◽  
Rania Zaier ◽  
Riaz Hussain ◽  
Muhammad Adnan ◽  
Malik Muhammad Asif Iqbal ◽  
...  
Keyword(s):  
Solar Cells ◽  
Organic Solar Cells ◽  
In Silico ◽  
High Performance ◽  
In Silico Modelling

scholarly journals Towards Biohydrogen Separation Using Poly(Ionic Liquid)/Ionic Liquid Composite Membranes

Membranes ◽  
2018 ◽  
Vol 8 (4) ◽  
pp. 124 ◽  
Author(s):  
Andreia S.L. Gouveia ◽  
Lucas Ventaja ◽  
Liliana C. Tomé ◽  
Isabel M. Marrucho
Keyword(s):  
Ionic Liquid ◽  
High Performance ◽  
Cost Effective ◽  
Clean Energy ◽  
Composite Membranes ◽  
Feed Pressure ◽  
Starting Point ◽  
Poly Ionic Liquid ◽  
Separation Performances ◽  
H2 Selectivity

Considering the high potential of hydrogen (H2) as a clean energy carrier, the implementation of high performance and cost-effective biohydrogen (bioH2) purification techniques is of vital importance, particularly in fuel cell applications. As membrane technology is a potentially energy-saving solution to obtain high-quality biohydrogen, the most promising poly(ionic liquid) (PIL)–ionic liquid (IL) composite membranes that had previously been studied by our group for CO2/N2 separation, containing pyrrolidinium-based PILs with fluorinated or cyano-functionalized anions, were chosen as the starting point to explore the potential of PIL–IL membranes for CO2/H2 separation. The CO2 and H2 permeation properties at the typical conditions of biohydrogen production (T = 308 K and 100 kPa of feed pressure) were measured and discussed. PIL–IL composites prepared with the [C(CN)3]− anion showed higher CO2/H2 selectivity than those containing the [NTf2]− anion. All the membranes revealed CO2/H2 separation performances above the upper bound for this specific separation, highlighting the composite incorporating 60 wt% of [C2mim][C(CN)3] IL.

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