Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review
Abstract
:1. Introduction
2. Bitter Peptides
2.1. Sources of Bitter Peptides
2.2. Extraction of Bitter Peptides
2.3. Chemical and Physiological Mechanisms of Bitter Taste Perception
2.4. Functions and Applications of Bitter Peptides
3. Research on Bitter Peptides in Bioinformatics
3.1. Bitter Peptide Database
3.2. Bitter Peptide Prediction Models
3.2.1. Bitter Peptide Quantitative Structure–Activity Relationship Models
3.2.2. Bitter Peptide Classification Prediction Models
Classifier | Dataset | Features | Algorithm | ACC | Sn | Sp | MCC | AUC | Publication | Reference |
---|---|---|---|---|---|---|---|---|---|---|
iBitter-SCM | BTP640: 320 BPs and 320 NBPs | AAC, DPC | SCM | 0.844 | 0.844 | 0.844 | 0.866 | 0.904 | July 2020 | [154] |
BERT4Bitter | BTP640: 320 BPs and 320 NBPs | Original sequence | BERT + LSTM | 0.922 | 0.938 | 0.906 | 0.844 | 0.964 | February 2021 | [157] |
iBitter-Fuse | BTP640: 320 BPs and 320 NBPs | AAC, DPC, PAAC, APAAC, AAI | SVM | 0.930 | 0.938 | 0.922 | 0.859 | 0.933 | August 2021 | [160] |
MIMML | BTP640: 320 BPs and 320 NBPs | TextCNN | Meta-learning | 0.938 | 0.938 | 0.938 | 0.875 | 0.955 | January 2022 | [158] |
iBitter-DRLF | BTP640: 320 BPs and 320 NBPs | SSA; UniRep; BiLSTM | LGBM | 0.944 | 0.922 | 0.977 | 0.899 | 0.977 | July 2022 | [4] |
Bitter-RF | BTP640: 320 BPs and 320 NBPs | AAC, TPAAC, APAAC, ASDC, DPC, DDE, GAA, GDPC, SOCNumber, QSOrder1 | RF | 0.940 | 0.940 | 0.940 | 0.890 | 0.980 | January 2023 | [161] |
Umami_YYDS | 129 BPs and 84 NBPs | 278 descriptor features | GTB | 0.896 | 0.917 | 0.875 | 0.792 | 0.980 | March 2023 | [124] |
CPM-BP | BTP720: 360 BPs and 360 NBPs | Q, Q1, Q2, Q3, Q4, AH, N, C, Percentage-HAA, N-basic AA, LFIYWV-C, Percentage-FWY, P-X-C, RP | LightGBM | 0.903 | 0.891 | - | 0.816 | 0.905 | February 2024 | [159] |
3.2.3. Shift in Research Directions
4. Future Directions
4.1. Database Enhancement
4.2. Diversity of Models
4.2.1. Classification Models
4.2.2. Interaction Models
4.2.3. Generative Models
4.3. Experimental Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Database | Data Volume | Link | Publication | Main Purpose | References |
---|---|---|---|---|---|
BIOPEP-UWM | 2275 BPs | https://biochemia.uwm.edu.pl/biopep-uwm/ (accessed on 11 September 2024) | 2019 | Widely used in the design of functional foods and research on bioactive peptides. | [122] |
Bitter DB | 1041 BMs | https://bitterdb.agri.huji.ac.il/dbbitter.php (accessed on 11 September 2024) | 2019 | Extensively used in selecting experimental ligands and developing bitter taste prediction models. | [123] |
Database of Cheese-Derived Bitter Peptides | 226 BPs | https://github.com/Kuhfeldrf/A-comprehensive-database-of-cheese-derived-bitter-peptides (accessed on 11 September 2024) | 2023 | Aids in understanding the sensory properties of cheese and quality control. | [125] |
TastePeptidesDB | 787 BPs | http://tastepeptides-meta.com/TastePeptidesDB (accessed on 11 September 2024) | 2024 | Helps with the identification and characterization of taste-active peptides in foods. | [124] |
Model | Dataset | Molecular Descriptors | Q2 a | R2 b | RMSEP c | Publication | Reference |
---|---|---|---|---|---|---|---|
PLS | 48 bitter dipeptides | Hydrophobicity, molecular size/volume, and electronic properties | - | - | - | November 1987 | [135] |
PLS | 229 bitter peptides | Total hydrophobicity, residue number, and log mass values | - | Dipeptides 0.750 Pentapeptide 0.900 | Dipeptides 0.530 Pentapeptide 0.480 | November 2006 | [136] |
SVR | 48 bitter dipeptides | 5 molecular descriptors d | 0.912 | 0.962 | 0.123 | May 2010 | [137] |
MLR SVM ANN | 229 bitter peptides | 20 molecular descriptors e | - | MLR 0.723 SVM 0.739 ANN 0.767 | - | November 2013 | [118] |
CoMFA, CoMSIA | 52 bitter peptides | Molecular modeling and molecular alignment | COMFA0.534 COMSIA0.547 | COMFA0.716 COMSIA0.579 | COMFA0.430 COMSIA0.423 | January 2019 | [138] |
PLS | 48 dipeptides, 52 tripeptides, and 23 tetrapeptides | 14 molecular descriptors f | Dipeptides 0.941 ± 0.001 Tripeptides 0.742 ± 0.004 Tetrapeptides 0.956 ± 0.002 | Dipeptides 0.950 ± 0.002 Tripeptides 0.770 ± 0.006 Tetrapeptides 0.972 ± 0.002 | Dipeptides 0.139 ± 0.002 Tripeptides 0.282 ± 0.004 Tetrapeptides 0.127 ± 0.004 | August 2019 | [117] |
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Liu, S.; Shi, T.; Yu, J.; Li, R.; Lin, H.; Deng, K. Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review. Int. J. Mol. Sci. 2024, 25, 9844. https://doi.org/10.3390/ijms25189844
Liu S, Shi T, Yu J, Li R, Lin H, Deng K. Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review. International Journal of Molecular Sciences. 2024; 25(18):9844. https://doi.org/10.3390/ijms25189844
Chicago/Turabian StyleLiu, Shanghua, Tianyu Shi, Junwen Yu, Rui Li, Hao Lin, and Kejun Deng. 2024. "Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review" International Journal of Molecular Sciences 25, no. 18: 9844. https://doi.org/10.3390/ijms25189844
APA StyleLiu, S., Shi, T., Yu, J., Li, R., Lin, H., & Deng, K. (2024). Research on Bitter Peptides in the Field of Bioinformatics: A Comprehensive Review. International Journal of Molecular Sciences, 25(18), 9844. https://doi.org/10.3390/ijms25189844