Hmrbase: a database of hormones and their receptors

Niu

et al. 2020

Preprint

Strategies to screen antihypertensive peptides with high throughput and rapid speed will be doubtlessly contributed to the treatment of hypertension. The food-derived antihypertensive peptides can reduce blood pressure without side effects. In present study, a novel model based on Extreme Gradient Boosting (XGBoost) algorithm was developed using the primary structural features of the food-derived peptides, and its performance in the prediction of antihypertensive peptides was compared with the dominating machine learning models. To further reflect the reliability of the method in real situation, the optimized XGBoost model was utilized to predict the antihypertensive degree of k-mer peptides cutting from 6 key proteins in bovine milk and the peptide-protein docking technology was introduced to verify the findings. The results showed that the XGBoost model achieved outstanding performance with the accuracy of 0.9841 and the area under the receiver operating characteristic curve of 0.9428, which were better than the other models. Using the XGBoost model, the prediction of antihypertensive peptides derived from milk protein was consistent with the peptide-protein docking results, and was more efficient. Our results indicate that using XGBoost algorithm as a novel auxiliary tool is feasible for screening antihypertensive peptide derived from food with high throughput and high efficiency.

Section: Training Data and Test Datamentioning

confidence: 99%

A Novel Machine Learning Strategy for Prediction of Antihypertensive Peptides Derived from Food with High Efficiency

Niu

et al. 2020

Preprint

“…Then, we calculated the average expression plasticity for genes associated with each GO term. We also collected lists of genes with well-defined biological functions from corresponding databases, including homeobox genes (63), hormone and receptor genes (64), and genes that function in the innate immune system (65).…”

Section: Gene Attributesmentioning

confidence: 99%

Multivariable regulation of gene expression plasticity in metazoans

Xiao

Zhao

et al. 2019

Preprint

An important capacity of genes is the rapid change of expression levels to cope with environment, known as expression plasticity. Elucidating the genomic mechanisms determining expression plasticity is critical for understanding the molecular basis of phenotypic plasticity, fitness, and adaptation. In this study, we systematically quantified genome-wide gene expression plasticity in four metazoan species by integrating changes of expression levels under a large number of genetic and environmental conditions. From this, we demonstrated that expression plasticity measures a distinct feature of gene expression that is orthogonal to other well-studies features including gene expression potential and tissue specificity/broadness. Expression plasticity is conserved across species with important physiological implications. The magnitude of expression plasticity is highly correlated with gene function and genes with high plasticity are implicated in disease susceptibility. Genome-wide analysis identified many conserved promoter cis-elements, trans-acting factors (such as CFCF), and gene body histone modifications (H3K36me3, H3K79me2, and H4K20me1) that are significantly associated with expression plasticity. Analysis of expression changes in perturbation experiments further validated a causal role of specific transcription factors and histone modifications. Collectively, this work reveals general properties, physiological implications, and multivariable regulation of gene expression plasticity in metazoans, extending the mechanistic understanding of gene regulation.

“…Group III, peptides are inhibitors which can be used as drug molecules or inhibiting activity of drug targets ( Eldar-Finkelman and Eisenstein, 2009 ; Groner et al, 2012 ; Beekman and Howell, 2016 ). These peptides kill pathogens by disrupting their cell membranes, by inhibiting their regulatory enzymes or by carrying out lysis ( Ivanciuc et al, 2003 ; Rashid et al, 2009 ; Pirtskhalava et al, 2016 ; Wang et al, 2016 , 2018 ; Singh et al, 2018 ). AMPs represent one of the broadest class of this group, for which number of databases and prediction methods have been developed in order to identify novel peptides which could act as drugs ( Saha and Raghava, 2006 ; Gautam et al, 2014 ; Mehta et al, 2014 ; Kumar et al, 2015 ; Meher et al, 2017 ; Agrawal et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Antimicrobial Potential of a Chemically Modified Peptide From Its Tertiary Structure

Agrawal

Raghava

2018

Front. Microbiol.

Designing novel antimicrobial peptides is a hot area of research in the field of therapeutics especially after the emergence of resistant strains against the conventional antibiotics. In the past number of in silico methods have been developed for predicting the antimicrobial property of the peptide containing natural residues. This study describes models developed for predicting the antimicrobial property of a chemically modified peptide. Our models have been trained, tested and evaluated on a dataset that contains 948 antimicrobial and 931 non-antimicrobial peptides, containing chemically modified and natural residues. Firstly, the tertiary structure of all peptides has been predicted using software PEPstrMOD. Structure analysis indicates that certain type of modifications enhance the antimicrobial property of peptides. Secondly, a wide range of features was computed from the structure of these peptides using software PaDEL. Finally, models were developed for predicting the antimicrobial potential of chemically modified peptides using a wide range of structural features of these peptides. Our best model based on support vector machine achieve maximum MCC of 0.84 with an accuracy of 91.62% on training dataset and MCC of 0.80 with an accuracy of 89.89% on validation dataset. To assist the scientific community, we have developed a web server called “AntiMPmod” which predicts the antimicrobial property of the chemically modified peptide. The web server is present at the following link (http://webs.iiitd.edu.in/raghava/antimpmod/).