Unknown

Dataset Information

0

Analysis and prediction of highly effective antiviral peptides based on random forests.

ABSTRACT: The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficient, which exceeds the previous models. The results suggest that aggregation could be an important feature for identifying antiviral peptides. In addition, our analysis reveals the characteristics of the antiviral peptides such as the importance of lysine and the abundance of ?-helical secondary structures.

SUBMITTER: Chang KY 

PROVIDER: S-EPMC3734225 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

Json Xml
altmetric image

Publications

Analysis and prediction of highly effective antiviral peptides based on random forests.

Chang Kuan Y KY   Yang Je-Ruei JR  

PloS one 20130805 8

The goal of this study was to examine and predict antiviral peptides. Although antiviral peptides hold great potential in antiviral drug discovery, little is done in antiviral peptide prediction. In this study, we demonstrate that a physicochemical model using random forests outperform in distinguishing antiviral peptides. On the experimental benchmark, our physicochemical model aided with aggregation and secondary structural features reaches 90% accuracy and 0.79 Matthew's correlation coefficie  ...[more]

Similar Datasets

Unknown Prediction of glycosylation sites using random forests.
| S-EPMC2651179 | biostudies-literature
Unknown Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation.
| S-EPMC6888698 | biostudies-literature
Unknown AIPpred: Sequence-Based Prediction of Anti-inflammatory Peptides Using Random Forest.
| S-EPMC5881105 | biostudies-other
Unknown Analysis and prediction of the critical regions of antimicrobial peptides based on conditional random fields.
| S-EPMC4372350 | biostudies-literature
Unknown A Copula Based Approach for Design of Multivariate Random Forests for Drug Sensitivity Prediction.
| S-EPMC4684346 | biostudies-literature
Unknown Clinical risk prediction with random forests for survival, longitudinal, and multivariate (RF-SLAM) data analysis.
| S-EPMC6937754 | biostudies-literature
Unknown Large-Scale Structure-Based Prediction of Stable Peptide Binding to Class I HLAs Using Random Forests.
| S-EPMC7387700 | biostudies-literature
Unknown Large-scale prediction of long disordered regions in proteins using random forests.
| S-EPMC2637845 | biostudies-other
Unknown Prediction of DNA-binding residues from protein sequence information using random forests.
| S-EPMC2709252 | biostudies-literature
Unknown Improved high-dimensional prediction with Random Forests by the use of co-data.
| S-EPMC5745983 | biostudies-literature