Review of Theoretical Prediction Models for Organic Extract Metabolites, Effect of Drying Temperature on Smooth Muscle Relaxing Activity Induced by Organic Extracts Specially Cecropia Obtusifolia Portal and Web Server Predictors of Drug-Protein Interaction

2015 ◽  
Vol 15 (999) ◽  
pp. 1-1
Author(s):  
Francisco Aguirre-Crespo ◽  
Xerardo García-Mera ◽  
Mónica Guillén-Poot ◽  
Héctor May-Díaz ◽  
Adrián Tun-Suárez ◽  
...  
Keyword(s):  
Smooth Muscle ◽  
Theoretical Prediction ◽  
Protein Interaction ◽  
Prediction Models ◽  
Web Server ◽  
Organic Extract ◽  
Drying Temperature ◽  
Cecropia Obtusifolia ◽  
Organic Extracts

scholarly journals DELPHI: accurate deep ensemble model for protein interaction sites prediction

2020 ◽  
Author(s):  
Yiwei Li ◽  
G Brian Golding ◽  
Lucian Ilie
Keyword(s):  
Deep Learning ◽  
Protein Interaction ◽  
Binding Sites ◽  
Web Server ◽  
Fine Tuning ◽  
Supplementary Information ◽  
Ensemble Model ◽  
Position Information ◽  
Interaction Sites ◽  
Protein Interaction Sites

Abstract Motivation Proteins usually perform their functions by interacting with other proteins, which is why accurately predicting protein–protein interaction (PPI) binding sites is a fundamental problem. Experimental methods are slow and expensive. Therefore, great efforts are being made towards increasing the performance of computational methods. Results We propose DEep Learning Prediction of Highly probable protein Interaction sites (DELPHI), a new sequence-based deep learning suite for PPI-binding sites prediction. DELPHI has an ensemble structure which combines a CNN and a RNN component with fine tuning technique. Three novel features, HSP, position information and ProtVec are used in addition to nine existing ones. We comprehensively compare DELPHI to nine state-of-the-art programmes on five datasets, and DELPHI outperforms the competing methods in all metrics even though its training dataset shares the least similarities with the testing datasets. In the most important metrics, AUPRC and MCC, it surpasses the second best programmes by as much as 18.5% and 27.7%, respectively. We also demonstrated that the improvement is essentially due to using the ensemble model and, especially, the three new features. Using DELPHI it is shown that there is a strong correlation with protein-binding residues (PBRs) and sites with strong evolutionary conservation. In addition, DELPHI’s predicted PBR sites closely match known data from Pfam. DELPHI is available as open-sourced standalone software and web server. Availability and implementation The DELPHI web server can be found at delphi.csd.uwo.ca/, with all datasets and results in this study. The trained models, the DELPHI standalone source code, and the feature computation pipeline are freely available at github.com/lucian-ilie/DELPHI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals SuperCYPsPred—a web server for the prediction of cytochrome activity

2020 ◽  
Vol 48 (W1) ◽  
pp. W580-W585 ◽  
Author(s):  
Priyanka Banerjee ◽  
Mathias Dunkel ◽  
Emanuel Kemmler ◽  
Robert Preissner
Keyword(s):  
Prediction Models ◽  
External Validation ◽  
Web Server ◽  
Adverse Outcomes ◽  
Cytochrome P450 Enzymes ◽  
Molecular Fingerprints ◽  
Cyp Inhibition ◽  
Clinical Drugs ◽  
Approved Drugs ◽  
The Web

Abstract Cytochrome P450 enzymes (CYPs)-mediated drug metabolism influences drug pharmacokinetics and results in adverse outcomes in patients through drug–drug interactions (DDIs). Absorption, distribution, metabolism, excretion and toxicity (ADMET) issues are the leading causes for the failure of a drug in the clinical trials. As details on their metabolism are known for just half of the approved drugs, a tool for reliable prediction of CYPs specificity is needed. The SuperCYPsPred web server is currently focused on five major CYPs isoenzymes, which includes CYP1A2, CYP2C19, CYP2D6, CYP2C9 and CYP3A4 that are responsible for more than 80% of the metabolism of clinical drugs. The prediction models for classification of the CYPs inhibition are based on well-established machine learning methods. The models were validated both on cross-validation and external validation sets and achieved good performance. The web server takes a 2D chemical structure as input and reports the CYP inhibition profile of the chemical for 10 models using different molecular fingerprints, along with confidence scores, similar compounds, known CYPs information of drugs—published in literature, detailed interaction profile of individual cytochromes including a DDIs table and an overall CYPs prediction radar chart (http://insilico-cyp.charite.de/SuperCYPsPred/). The web server does not require log in or registration and is free to use.

scholarly journals RPITER: A Hierarchical Deep Learning Framework for ncRNA–Protein Interaction Prediction

2019 ◽  
Vol 20 (5) ◽  
pp. 1070 ◽  
Author(s):  
Cheng Peng ◽  
Siyu Han ◽  
Hui Zhang ◽  
Ying Li
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Protein Interaction ◽  
Rna Binding ◽  
Prediction Models ◽  
Rna Binding Proteins ◽  
Biological Research ◽  
Sequence Information ◽  
Learning Framework ◽  
Coding Method

Non-coding RNAs (ncRNAs) play crucial roles in multiple fundamental biological processes, such as post-transcriptional gene regulation, and are implicated in many complex human diseases. Mostly ncRNAs function by interacting with corresponding RNA-binding proteins. The research on ncRNA–protein interaction is the key to understanding the function of ncRNA. However, the biological experiment techniques for identifying RNA–protein interactions (RPIs) are currently still expensive and time-consuming. Due to the complex molecular mechanism of ncRNA–protein interaction and the lack of conservation for ncRNA, especially for long ncRNA (lncRNA), the prediction of ncRNA–protein interaction is still a challenge. Deep learning-based models have become the state-of-the-art in a range of biological sequence analysis problems due to their strong power of feature learning. In this study, we proposed a hierarchical deep learning framework RPITER to predict RNA–protein interaction. For sequence coding, we improved the conjoint triad feature (CTF) coding method by complementing more primary sequence information and adding sequence structure information. For model design, RPITER employed two basic neural network architectures of convolution neural network (CNN) and stacked auto-encoder (SAE). Comprehensive experiments were performed on five benchmark datasets from PDB and NPInter databases to analyze and compare the performances of different sequence coding methods and prediction models. We found that CNN and SAE deep learning architectures have powerful fitting abilities for the k-mer features of RNA and protein sequence. The improved CTF coding method showed performance gain compared with the original CTF method. Moreover, our designed RPITER performed well in predicting RNA–protein interaction (RPI) and could outperform most of the previous methods. On five widely used RPI datasets, RPI369, RPI488, RPI1807, RPI2241 and NPInter, RPITER obtained A U C of 0.821, 0.911, 0.990, 0.957 and 0.985, respectively. The proposed RPITER could be a complementary method for predicting RPI and constructing RPI network, which would help push forward the related biological research on ncRNAs and lncRNAs.

scholarly journals PredHS: a web server for predicting protein–protein interaction hot spots by using structural neighborhood properties

2014 ◽  
Vol 42 (W1) ◽  
pp. W290-W295 ◽  
Author(s):  
Lei Deng ◽  
Qiangfeng Cliff Zhang ◽  
Zhigang Chen ◽  
Yang Meng ◽  
Jihong Guan ◽  
...  
Keyword(s):  
Protein Interaction ◽  
Hot Spots ◽  
Web Server ◽  
Protein Protein Interaction ◽  
Structural Neighborhood

scholarly journals ChAlPred: A Web Server for Prediction of Allergenicity of Chemical Compounds

2021 ◽  
Author(s):  
Neelam Sharma ◽  
Sumeet Patiyal ◽  
Anjali Dhall ◽  
Leimarembi Devi Naorem ◽  
Gajendra P.S. Raghava
Keyword(s):  
Chemical Compound ◽  
Prediction Models ◽  
Web Server ◽  
Chemical Compounds ◽  
Validation Dataset ◽  
Fingerprint Analysis ◽  
Approved Drugs ◽  
3D Descriptors ◽  
User Friendly ◽  
Fda Approved Drugs

Allergy is the abrupt reaction of the immune system that may occur after the exposure with allergens like protein/peptide or chemical allergens. In past number of methods of have been developed for classifying the protein/peptide based allergen. To the best of our knowledge, there is no method to classify the allergenicity of chemical compound. Here, we have proposed a method named ChAlPred, which can be used to fill the gap for predicting the chemical compound that might cause allergy. In this study, we have obtained the dataset of 403 allergen and 1074 non-allergen chemical compounds and used 2D, 3D and FP descriptors to train, test and validate our prediction models. The fingerprint analysis of the dataset indicates that PubChemFP129 and GraphFP1014 are more frequent in the allergenic chemical compounds, whereas KRFP890 is highly present in non-allergenic chemical compounds. Our XGB based model achieved the AUC of 0.89 on validation dataset using 2D descriptors. RF based model has outperformed other classifiers using 3D descriptors (AUC = 0.85), FP descriptors (AUC = 0.92), combined descriptors (AUC = 0.93), and hybrid model (AUC = 0.92) on validation dataset. In addition, we have also reported some FDA-approved drugs like Cefuroxime, Spironolactone, and Tioconazole which can cause the allergic symptoms. A user user-friendly web server named ChAlPred has been developed to predict the chemical allergens. It can be easily accessed at https://webs.iiitd.edu.in/raghava/chalpred/.

scholarly journals Impact of aqueous and organic extracts of Rhodobryum roseum on inhibition of fungal and bacterial growth

2020 ◽  
Vol 21 (1&2) ◽  
pp. 151-161
Author(s):  
Shivom Singh ◽  
Kajal Srivastava Rathore ◽  
D.R. Khanna
Keyword(s):  
Antimicrobial Activity ◽  
Methanol Extract ◽  
Time Intervals ◽  
Organic Extract ◽  
Dilution Assay ◽  
Organic Extracts ◽  
The Rich ◽  
Bacteria And Fungi ◽  
Broth Dilution ◽  
Mic Value

Mosses have been known for millennia and highly esteemed all over the world as the rich source of bioactive compounds. The research targets on evolution of microbicidal potentialities of Rhodobryum roseum (extract) used against selected fungus (X. oryzae pv oryzae, S. enteric, P. multocida and M. plutonius) and bacteria (R. solani, S. rolfsii, F. oxysporum and T. indica) to assay antimicrobial activity. Impact of aqueous and undertaken organic viz., ethanol, acetone, choloform, petroleum ether, methanol extract of R. roseum, at varied concentrations and at different time intervals were examined against the growth of bacteria and fungus. All the aqueous extracts were proved to be infective against all the tested pathogens. The antimicrobial potential of six extracts was screened against undertaken bacteria and fungi using micro broth dilution assay. Out of the six (diverse organic and aqueous) extract of R. roseum in ethanol and acetone showed maximum inhibitory activity in S. rolfsii with the MIC value of 5.00 (µg/ml),  along with MFC value of 6.25 (µg/ml) in acetone extract and the value of MBC was recorded utmost in X. oryzae with value 3.00 (µg/ml) extracted in ethanol. Over all, the organic extract of R. roseum has potent antimicrobial activity and could be possible source of lead molecules considered for the future development of microbicidal agent.

scholarly journals Medicinal plant Potentilla fulgens and its effect in vitro against Fasciola gigantica

2021 ◽  
Vol 5 (1) ◽  
pp. 004-007
Author(s):  
Kumar Pradeep
Keyword(s):  
Medicinal Plant ◽  
Liver Fluke ◽  
Fasciola Hepatica ◽  
Ethanolic Extract ◽  
Ethanol Extract ◽  
Organic Extract ◽  
Lc50 Value ◽  
Organic Extracts ◽  
Giant Liver Fluke

Fascioliasis is a one of the most important serious parasitic zoonotic disease which caused by trematode giant liver fluke Fasciola hepatica and F. gigantica among cattle’s and humans. The infection of Fasciola can be control by the use of phytochemicals as anthelmintic components. The anthelmintic activities of dried root powder of medicinal plant Potentilla fulgens and their different preparations (organic extracts and column purified fraction) are uses in vitro against liver fluke F. gigantica. The dried root powder, different organic extract, and column fractions were time and concentration-dependent. Among all the organic extracts, ethanol extract was high toxic than other organic extracts. The toxic effect of ethanolic extract of P. fulgens after 2h exposure the LC50 value is 5.22 mg/ml against F. gigantica. The column purified fraction of dried root powder of P. fulgens shows more toxicity. The 2h LC50 of column purified fraction was 3.25 mg/ml whereas in 8h exposure the LC50 is 1.24 mg/ml. The phytochemicals of the P. fulgens may be used as anthelmintic components against liver fluke F. gigantica.

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