A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides
Abstract
:1. Introduction
2. Results
2.1. An Overview of the Dataset
2.2. Performance Evaluation of the Model
2.3. Model Validations
3. Discussion
4. Materials and Methods
4.1. Benchmark Dataset
4.2. Literature Searching Strategy
4.3. Representation of the Peptide Sequence
4.4. Machine Learning Algorithms
4.5. Model Evaluations
4.6. The In Vitro ACE Inhibitory Assay
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Peptide Sequence | Predicted IC50 (μM) | Reported IC50 (μM) | Predicted IC50/ Reported IC50 | Reference Reporting the IC50 |
---|---|---|---|---|
KGYGGVSLPEW | 0.23 | 0.70 | 0.33 | [17] |
LLVTLKK | 0.42 | 0.95 | 0.44 | [18] |
LKY | 0.36 | 0.78 | 0.46 | [19] |
PAGELHP | 0.29 | 0.50 | 0.58 | [20] |
DAQSAPLRVY | 7.60 | 12.20 | 0.62 | [17] |
RDGGYCC | 0.56 | 0.84 | 0.67 | [21] |
WV | 217.83 | 307.61 | 0.71 | [22] |
KF | 20.08 | 28.30 | 0.71 | [23] |
LVY | 1.30 | 1.80 | 0.72 | [19] |
IRW | 0.44 | 0.61 | 0.72 | [24] |
FY | 2.71 | 3.70 | 0.73 | [23] |
LEEFCC | 1.36 | 1.85 | 0.73 | [21] |
GF | 213.69 | 277.90 | 0.77 | [25] |
MLPAY | 1.27 | 1.58 | 0.80 | [19] |
IQW | 1.26 | 1.56 | 0.81 | [26] |
LRA | 141.66 | 174.30 | 0.81 | [27] |
KIDKVVK | 0.53 | 0.62 | 0.85 | [18] |
LKP | 2.49 | 2.93 | 0.85 | [26] |
AFVGYVLP | 12.62 | 14.41 | 0.88 | [28] |
LAK | 42.46 | 48.00 | 0.88 | [29] |
NF | 41.66 | 46.30 | 0.90 | [25] |
VY | 10.24 | 11.30 | 0.91 | [23] |
HLNVVHGN | 46.29 | 50.88 | 0.91 | [30] |
DKVGINYW | 23.13 | 25.40 | 0.91 | [17] |
EKSYELP | 16.54 | 18.02 | 0.92 | [28] |
PGSGCAGTDL | 53.67 | 57.86 | 0.93 | [30] |
LSA | 7.26 | 7.81 | 0.93 | [19] |
GAAELPCSADWW | 10.25 | 10.95 | 0.94 | [31] |
KY | 7.25 | 7.70 | 0.94 | [23] |
IVY | 43.52 | 45.77 | 0.95 | [32] |
KW | 10.28 | 10.80 | 0.95 | [23] |
VW | 10.29 | 10.80 | 0.95 | [23] |
VDSDVVK | 8.26 | 8.64 | 0.96 | [33] |
VF | 42.00 | 43.70 | 0.96 | [23] |
LRLESF | 5.21 | 5.39 | 0.97 | [30] |
YY | 46.30 | 47.90 | 0.97 | [27] |
LDSPSEGRAPG | 17.31 | 17.90 | 0.97 | [20] |
VIY | 4.36 | 4.50 | 0.97 | [19] |
VELYP | 5.23 | 5.22 | 1.00 | [28] |
WQVLPNAVPAK | 1023.89 | 1010.00 | 1.01 | [34] |
TFQGGlPPHGIQVER | 3.47 | 3.40 | 1.02 | [29] |
VISDEDGVTH | 8.33 | 8.16 | 1.02 | [35] |
RLSGQTIEVTSEYLFRH | 577.19 | 560.18 | 1.03 | [36] |
ILSKLK | 4.28 | 4.02 | 1.07 | [18] |
AY | 156.44 | 146.76 | 1.07 | [37] |
IISKIK | 1.28 | 1.19 | 1.07 | [18] |
CTFSIPAQC | 26.31 | 24.40 | 1.08 | [38] |
IY | 2.96 | 2.70 | 1.09 | [23] |
LT | 1.22 | 1.11 | 1.10 | [22] |
TVTNPARIA | 16.33 | 14.50 | 1.13 | [20] |
LVLPGELAK | 214.22 | 184.00 | 1.16 | [29] |
LQP | 1.35 | 1.04 | 1.30 | [19] |
IPPAYTK | 35.75 | 23.50 | 1.52 | [29] |
LVLPGE | 20.79 | 13.50 | 1.54 | [29] |
Peptide Sequence | Predicted IC50 (μM) | Experimental IC50 (μM) | Predicted IC50 /Experimental IC50 |
---|---|---|---|
LKPDQ | 0.70 | 0.88 | 0.79 |
WD | 0.63 | 0.51 | 1.23 |
GVPK | 0.61 | 0.25 | 2.44 |
FI | 0.61 | 0.31 | 1.95 |
PDFLI | 0.60 | 0.33 | 1.83 |
HDHR | 0.59 | 0.59 | 1.00 |
LKPNS | 0.56 | 0.5 | 1.12 |
VYHEL | 0.55 | 0.38 | 1.45 |
GPAY | 0.54 | 0.37 | 1.45 |
LVL | 0.51 | 0.32 | 1.59 |
LKL | 0.49 | 0.56 | 0.88 |
FDKA | 0.47 | 0.6 | 0.79 |
VAWKL | 0.46 | 0.23 | 2.00 |
VHLAP | 0.46 | 0.33 | 1.39 |
IQWCA | 0.46 | 0.1 | 4.59 |
PLPLL | 0.55 | 0.2 | 1.75 |
KLPAY | 0.44 | 0.12 | 3.63 |
LKPI | 0.43 | 0.39 | 1.11 |
FALPC | 0.42 | 0.16 | 2.65 |
ALPD | 0.72 | 1.55 | 0.46 |
Amino Acid | Representing Digit | Amino Acid | Representing Digit |
---|---|---|---|
I | 2 | D | 11 |
L | 3 | C | 12 |
S | 4 | T | 13 |
H | 5 | N | 14 |
R | 6 | V | 15 |
P | 7 | G | 16 |
A | 8 | Q | 17 |
W | 9 | K | 18 |
F | 10 | Y | 19 |
E | 20 |
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Liao, W.; Yan, S.; Cao, X.; Xia, H.; Wang, S.; Sun, G.; Cai, K. A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides. Molecules 2023, 28, 4901. https://doi.org/10.3390/molecules28134901
Liao W, Yan S, Cao X, Xia H, Wang S, Sun G, Cai K. A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides. Molecules. 2023; 28(13):4901. https://doi.org/10.3390/molecules28134901
Chicago/Turabian StyleLiao, Wang, Siyuan Yan, Xinyi Cao, Hui Xia, Shaokang Wang, Guiju Sun, and Kaida Cai. 2023. "A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides" Molecules 28, no. 13: 4901. https://doi.org/10.3390/molecules28134901
APA StyleLiao, W., Yan, S., Cao, X., Xia, H., Wang, S., Sun, G., & Cai, K. (2023). A Novel LSTM-Based Machine Learning Model for Predicting the Activity of Food Protein-Derived Antihypertensive Peptides. Molecules, 28(13), 4901. https://doi.org/10.3390/molecules28134901