iBitter-Fuse: A Novel Sequence-Based Bitter Peptide Predictor by Fusing Multi-View Features.


Type
Article
Change log
Authors
Charoenkwan, Phasit 
Nantasenamat, Chanin 
Hasan, Md Mehedi 
Moni, Mohammad Ali 
Lio', Pietro 
Abstract

Accurate identification of bitter peptides is of great importance for better understanding their biochemical and biophysical properties. To date, machine learning-based methods have become effective approaches for providing a good avenue for identifying potential bitter peptides from large-scale protein datasets. Although few machine learning-based predictors have been developed for identifying the bitterness of peptides, their prediction performances could be improved. In this study, we developed a new predictor (named iBitter-Fuse) for achieving more accurate identification of bitter peptides. In the proposed iBitter-Fuse, we have integrated a variety of feature encoding schemes for providing sufficient information from different aspects, namely consisting of compositional information and physicochemical properties. To enhance the predictive performance, the customized genetic algorithm utilizing self-assessment-report (GA-SAR) was employed for identifying informative features followed by inputting optimal ones into a support vector machine (SVM)-based classifier for developing the final model (iBitter-Fuse). Benchmarking experiments based on both 10-fold cross-validation and independent tests indicated that the iBitter-Fuse was able to achieve more accurate performance as compared to state-of-the-art methods. To facilitate the high-throughput identification of bitter peptides, the iBitter-Fuse web server was established and made freely available online. It is anticipated that the iBitter-Fuse will be a useful tool for aiding the discovery and de novo design of bitter peptides.

Description
Keywords
bioinformatics, bitter peptide, classification, feature selection, machine learning, support vector machine, Algorithms, Benchmarking, Humans, Machine Learning, Peptide Fragments, Predictive Value of Tests, Software, Support Vector Machine, Taste
Journal Title
International Journal of Molecular Sciences
Conference Name
Journal ISSN
1422-0067
1422-0067
Volume Title
22
Publisher
MDPI AG