Development of a Simple and Effective Lipid-A Antagonist Based on Computational Prediction.
Ontology highlight
ABSTRACT:
SUBMITTER: Slater O
PROVIDER: S-EPMC9402072 | biostudies-literature | 2022 Jun
REPOSITORIES: biostudies-literature
Publications
Development of a Simple and Effective Lipid-A Antagonist Based on Computational Prediction.
Slater Olivia O Dhami Kapur B KB Shrestha Ganesh G Kontoyianni Maria M Nichols Michael R MR Demchenko Alexei V AV
ACS infectious diseases 20220525 6
Sepsis is a serious medical condition characterized by bacterial infection and a subsequent massive systemic inflammatory response. In an effort to identify compounds that block lipopolysaccharide (LPS)-induced inflammation reported herein is the development of simple Lipid-A analogues that lack a disaccharide core yet still possess potent antagonistic activity against LPS. The structure of the new lead compound was developed based on predictive computational experiments. LPS antagonism by the l ...[more]
Similar Datasets
Project description: Not available
| S-EPMC3967918 | biostudies-literature
Project description:MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets. Small RNA sequencing was done for 10 days old seedlings of Phaseolus vulgaris Cv. Anupam
2015-04-28 | E-GEOD-68305 | biostudies-arrayexpress
Project description:MicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data. We have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20. There is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.
2015-04-28 | GSE68305 | GEO
Project description:In the recent few years, an increasing number of studies have shown that microRNAs (miRNAs) play critical roles in many fundamental and important biological processes. As one of pathogenetic factors, the molecular mechanisms underlying human complex diseases still have not been completely understood from the perspective of miRNA. Predicting potential miRNA-disease associations makes important contributions to understanding the pathogenesis of diseases, developing new drugs, and formulating individualized diagnosis and treatment for diverse human complex diseases. Instead of only depending on expensive and time-consuming biological experiments, computational prediction models are effective by predicting potential miRNA-disease associations, prioritizing candidate miRNAs for the investigated diseases, and selecting those miRNAs with higher association probabilities for further experimental validation. In this study, Path-Based MiRNA-Disease Association (PBMDA) prediction model was proposed by integrating known human miRNA-disease associations, miRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity for miRNAs and diseases. This model constructed a heterogeneous graph consisting of three interlinked sub-graphs and further adopted depth-first search algorithm to infer potential miRNA-disease associations. As a result, PBMDA achieved reliable performance in the frameworks of both local and global LOOCV (AUCs of 0.8341 and 0.9169, respectively) and 5-fold cross validation (average AUC of 0.9172). In the cases studies of three important human diseases, 88% (Esophageal Neoplasms), 88% (Kidney Neoplasms) and 90% (Colon Neoplasms) of top-50 predicted miRNAs have been manually confirmed by previous experimental reports from literatures. Through the comparison performance between PBMDA and other previous models in case studies, the reliable performance also demonstrates that PBMDA could serve as a powerful computational tool to accelerate the identification of disease-miRNA associations.
| S-EPMC5384769 | biostudies-literature
Project description:The high pathogenic nature of the Middle East Respiratory coronavirus (MER) and the associated high fatality rate demands an urgent attention from researchers. Because there is currently no approved drug for the management of the disease, research efforts have been intensified towards the discovery of a potent drug for the treatment of the disease. Papain Like protease (PLpro) is one of the key proteins involved in the viral replication. We therefore docked forty-six compounds already characterized from Azadirachta indica, Xylopia aethipica and Allium cepa against MERS-CoV-PLpro. The molecular docking analysis was performed with AutoDock 1.5.6 and compounds which exhibit more negative free energy of binding, and low inhibition constant (Ki) with the protein (MERS-CoV-PLpro) were considered potent. The physicochemical and pharmacokinetic properties of the compounds were predicted using the Swissadme web server. Twenty-two of the compounds showed inhibition potential similar to dexamethasone and remdesvir, which had binding affinity of -6.8 and -6.3 kcal/mol respectively. The binding affinity of the compounds ranged between -3.4 kcal/mol and -7.7 kcal/mol whereas; hydroxychloroquine had a binding affinity of -4.5 kcal/mol. Among all the compounds, nimbanal and verbenone showed drug likeliness, they did not violate the Lipinski rule neither were they inhibitors of drug-metabolizing enzymes. Both nimbanal and verbenone were further post-scored with MM/GBSA and the binding free energy of nimbanal (-25.51 kcal/mol) was comparable to that of dexamethasone (-25.46 kcal/mol). The RMSD, RMSF, torsional angle, and other analysis following simulation further substantiate the efficacy of nimbanal as an effective drug candidate. In conclusion, our study showed that nimbanal is a more promising therapeutic agent and could be a lead for the discovery of a new drug that may be useful in the management of severe respiratory coronavirus syndrome.
| S-EPMC8161736 | biostudies-literature
Project description:THY1773 is a novel arginine vasopressin 1B (V1B ) receptor antagonist that is under development as an oral drug for the treatment of major depressive disorder (MDD). Here we report our strategy to predict a clinically effective dose of THY1773 for MDD in the preclinical stage, and discuss the important insights gained by retrospective analysis of prediction accuracy. To predict human pharmacokinetic (PK) parameters, several extrapolation methods from animal or in vitro data to humans were investigated. The fu correction intercept method and two-species-based allometry were used to extrapolate clearance from rats and dogs to humans. The physiologically based pharmacokinetics (PBPK)/receptor occupancy (RO) model was developed by linking free plasma concentration with pituitary V1B RO by the Emax model. As a result, the predicted clinically effective dose of THY1773 associated with 50% V1B RO was low enough (10 mg/day, or at maximum 110 mg/day) to warrant entering phase 1 clinical trials. In the phase 1 single ascending dose study, TS-121 capsule (active ingredient: THY1773) showed favorable PKs for THY1773 as expected, and in the separately conducted phase 1 RO study using positron emission tomography, the observed pituitary V1B RO was comparable to our prediction. Retrospective analysis of the prediction accuracy suggested that the prediction methods considering plasma protein binding, and avoiding having to apply unknown scaling factors obtained in animals to humans, would lead to better prediction. Selecting mechanism-based methods with reasonable assumptions would be critical for the successful prediction of a clinically effective dose in the preclinical stage of drug development.
| S-EPMC8252455 | biostudies-literature
Project description:BackgroundDyslipidemia is an extremely prevalent but preventable risk factor for cardiovascular disease. However, many dyslipidemia patients remain undetected in resource limited settings. The study was performed to develop and evaluate a simple and effective prediction approach without biochemical parameters to identify those at high risk of dyslipidemia in rural adult population.MethodsDemographic, dietary and lifestyle, and anthropometric data were collected by a cross-sectional survey from 8,914 participants living in rural areas aged 35-78 years. There were 6,686 participants randomly selected into a training group for constructing the artificial neural network (ANN) and logistic regression (LR) prediction models. The remaining 2,228 participants were assigned to a validation group for performance comparisons of ANN and LR models. The predictors of dyslipidemia risk were identified from the training group using multivariate logistic regression analysis. Predictive performance was evaluated by receiver operating characteristic (ROC) curve.ResultsSome risk factors were significantly associated with dyslipidemia, including age, gender, educational level, smoking, high-fat diet, vegetable and fruit intake, family history, physical activity, and central obesity. For the ANN model, the sensitivity, specificity, positive and negative likelihood ratio, positive and negative predictive values were 90.41%, 76.66%, 3.87, 0.13, 76.33%, and 90.58%, respectively, while LR model were only 57.37%, 70.91%, 1.97, 0.60, 62.09%, and 66.73%, respectively. The area under the ROC cure (AUC) value of the ANN model was 0.86±0.01, showing more accurate overall performance than traditional LR model (AUC = 0.68±0.01, P<0.001).ConclusionThe ANN model is a simple and effective prediction approach to identify those at high risk of dyslipidemia, and it can be used to screen undiagnosed dyslipidemia patients in rural adult population. Further work is planned to confirm these results by incorporating multi-center and longer follow-up data.
| S-EPMC3429495 | biostudies-literature
Project description:To develop a prediction model for adrenal crisis (AC) diagnosis among individuals with adrenal insufficiency that relies on the values of routinely measured clinical parameters, for application in standard clinical practice. We retrospectively analysed data from five referral centres in Japan. Multivariate binary logistic regression was used to identify independent predictors of AC, and receiver operating characteristic curve analysis was used to determine their optimal cut-off points. The analysis included data from 54 patients with 90 AC events. Logistic regression revealed that serum sodium and C-reactive protein (CRP) levels were independent predictors of AC. Serum sodium levels?<?137 mEq/L had a sensitivity of 71.1% and specificity of 95.6%. CRP levels?>?1.3 mg/dL had a sensitivity of 84.4% and specificity of 94.9%. In combination, serum sodium levels?<?137 mEq/L or CRP levels?>?1.3 mg/dL for AC diagnosis had sensitivity and specificity values of 97.8% and 94.4%, respectively. The combined use of serum sodium and CRP levels had high sensitivity and specificity, and can be used for AC screening in standard clinical practice. The model can assist in identifying AC among high-risk individuals. A larger prospective study is needed to validate these results.
| S-EPMC7421495 | biostudies-literature
Project description:BackgroundMicroRNAs (miRNAs) are endogenous, noncoding, short RNAs directly involved in regulating gene expression at the post-transcriptional level. In spite of immense importance, limited information of P. vulgaris miRNAs and their expression patterns prompted us to identify new miRNAs in P. vulgaris by computational methods. Besides conventional approaches, we have used the simple sequence repeat (SSR) signatures as one of the prediction parameter. Moreover, for all other parameters including normalized Shannon entropy, normalized base pairing index and normalized base-pair distance, instead of taking a fixed cut-off value, we have used 99% probability range derived from the available data.ResultsWe have identified 208 mature miRNAs in P. vulgaris belonging to 118 families, of which 201 are novel. 97 of the predicted miRNAs in P. vulgaris were validated with the sequencing data obtained from the small RNA sequencing of P. vulgaris. Randomly selected predicted miRNAs were also validated using qRT-PCR. A total of 1305 target sequences were identified for 130 predicted miRNAs. Using 80% sequence identity cut-off, proteins coded by 563 targets were identified. The computational method developed in this study was also validated by predicting 229 miRNAs of A. thaliana and 462 miRNAs of G. max, of which 213 for A. thaliana and 397 for G. max are existing in miRBase 20.ConclusionsThere is no universal SSR that is conserved among all precursors of Viridiplantae, but conserved SSR exists within a miRNA family and is used as a signature in our prediction method. Prediction of known miRNAs of A. thaliana and G. max validates the accuracy of our method. Our findings will contribute to the present knowledge of miRNAs and their targets in P. vulgaris. This computational method can be applied to any species of Viridiplantae for the successful prediction of miRNAs and their targets.
| S-EPMC4464996 | biostudies-literature
Project description:What motivates an action in the absence of a definite reward? Taking the case of visuomotor control, we consider a minimal control problem that is how select the next saccade, in a sequence of discrete eye movements, when the final objective is to better interpret the current visual scene. The visual scene is modeled here as a partially-observed environment, with a generative model explaining how the visual data is shaped by action. This allows to interpret different action selection metrics proposed in the literature, including the Salience, the Infomax and the Variational Free Energy, under a single information theoretic construct, namely the view-based Information Gain. Pursuing this analytic track, two original action selection metrics named the Information Gain Lower Bound (IGLB) and the Information Gain Upper Bound (IGUB) are then proposed. Showing either a conservative or an optimistic bias regarding the Information Gain, they strongly simplify its calculation. An original fovea-based visual scene decoding setup is then proposed, with numerical experiments highlighting different facets of artificial fovea-based vision. A first and principal result is that state-of-the-art recognition rates are obtained with fovea-based saccadic exploration, using less than 10% of the original image's data. Those satisfactory results illustrate the advantage of mixing predictive control with accurate state-of-the-art predictors, namely a deep neural network. A second result is the sub-optimality of some classical action-selection metrics widely used in the literature, that is not manifest with finely-tuned inference models, but becomes patent when coarse or faulty models are used. Last, a computationally-effective predictive model is developed using the IGLB objective, with pre-processed visual scan-path read-out from memory, bypassing computationally-demanding predictive calculations. This last simplified setting is shown effective in our case, showing both a competing accuracy and a good robustness to model flaws.
| S-EPMC6302111 | biostudies-literature