Objective:The aim was to study in vitro antidiabetic activity of endophytic fungi isolated from Ficus religiosa.
Methods:The explants (leaves and stem) were processed on the potato dextrose media nine colonies were found and colony frequency was calculated. All the colonies were transferred onto potato dextrose broth and incubated for 21 days. The crude was extracted using three solvents petroleum ether (0.1), diethyl ether (2.8), and ethyl acetate (4.4). Three assays were performed to determine in vitro antidiabetic activity of crude extract (α-amylase inhibition assay, α-glucosidase inhibition assay, and glucose diffusion assay) and the percentage of inhibition by crude and standard acarbose was calculated with standard error mean.
Results:The endophytic fungi show the highest percentage of inhibition for α-amylase inhibition assay (91%±0.06), α-glucosidase inhibition assay (42%±0.01).
Conclusion:The results indicate that the hypoglycemic activity of the endophytic crude extract has been proved, hence further studies are focused onto isolate and purify the bioactive compounds and test for in vivo animal studies to confirm the antidiabetic activity.
Transcription factors (TFs) are essential DNA-binding proteins that regulate the rate of transcription of several genes and controls the expression of genes inside a cell. The prediction of TFs with high precision is important for understanding number of biological processes such as cell-differentiation, intracellular signaling, cell-cycle control. In this study, we developed a hybrid method that combine alignment-based and alignment-free methods for predicting transcription factors with higher accuracy. All models have been trained, tested and evaluated on a large dataset that contain 19406 TFs and 523560 non-TFs protein sequences. In order to avoid biasness in evaluation, dataset is divided in training and validation/independent dataset, where 80% data was used for training and remaining 20% for external validation. In case of alignment-free methods, models are developed based on machine learning techniques using compositional features of a protein. Our best alignment-free model obtained AUC 0.97 on independent dataset. In case of alignment-based method, we used BLAST at different cut-off to predict transcription factors. Though alignment-based method shows excellent performance but unable to cover all transcription factor due to no-hits. In order to combine power of both, we developed a hybrid method that combine alignment-free and alignment-based method; achieved maximum AUC of 0.99 on independent dataset. The method proposed in this study perform better than existing methods. We incorporated the best models in the webserver/standalone package “TransFacPred” (https://webs.iiitd.edu.in/raghava/transfacpred).Key PointsTranscription factors (TFs) are vital DNA-binding proteins.A hybrid method for the prediction of TFs using sequence information.Computer-aided model were developed using machine-learning algorithm to predict TFs.Alignment-based and alignment-free approaches were used for the prediction.A user-friendly webserver, python- and Perl-based standalone package available.
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