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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Charoenkwan, Phasit 
Nantasenamat, Chanin  ORCID logo  https://orcid.org/0000-0003-1040-663X
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

Funder: Chiang Mai University


Funder: College of Arts, Media and Technology, Chiang Mai University


Funder: Mahidol University

Keywords

Classification, Bioinformatics, Support vector machine, Feature Selection, Machine Learning, Bitter Peptide, Humans, Peptide Fragments, Predictive Value of Tests, Taste, Algorithms, Software, Benchmarking, Support Vector Machine

Journal Title

International journal of molecular sciences

Conference Name

Journal ISSN

1422-0067

Volume Title

22

Publisher