CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides
Abstract
:1. Introduction
2. Results
2.1. Exploration of the Data Set
2.1.1. Global Overview
2.1.2. AMP/Non-AMP Peptides Analysis
2.2. Antimicrobial Activity Prediction
2.2.1. Feature Selection
2.2.2. Algorithm Choice
2.2.3. Performance and Interpretation
2.2.4. Comparison with Other Prediction Tools
Model | Accuracy | Sensitivity | Specificity | AUC-ROC | MCC |
---|---|---|---|---|---|
Deep-AmPEP30 1 | 0.60 | 0.92 | 0.29 | 0.71 | 0.27 |
RF-AmPEP30 1 | 0.59 | 0.94 | 0.25 | 0.74 | 0.26 |
AMP_Scanner | 0.61 | 0.93 | 0.30 | 0.75 | 0.29 |
iAMPpred | 0.60 | 0.91 | 0.29 | 0.67 | 0.26 |
DBAASP | 0.67 | 0.74 | 0.61 | - 2 | 0.35 |
Average | 0.61 | 0.89 | 0.35 | 0.72 | 0.29 |
CalcAMP+ | 0.79 | 0.79 | 0.79 | 0.86 | 0.58 |
CalcAMP- | 0.80 | 0.78 | 0.82 | 0.87 | 0.61 |
2.3. Antifungal Activity Prediction
2.3.1. Performance and Interpretation
2.3.2. Comparison with Other Prediction Tools
3. Discussion
4. Materials and Methods
4.1. Data Preparation
4.1.1. Data Mining and Preprocessing
4.1.2. Data Labelling
- Gram+: 5791 peptides; 2849 Non-AMP (49%) and 2942 AMP (51%)
- Gram−: 6087 peptides; 3163 Non-AMP (52%) and 2924 AMP (48%)
- Fungi: 2544 peptides; 1475 Non-AMP (58%) and 1069 AMP (42%)
4.1.3. Creation of the Data Sets
4.2. Machine Learning Experiments
4.2.1. Feature Calculation
4.2.2. Model Comparison
4.2.3. Model Creation and Tuning
4.2.4. Feature Selection
4.2.5. Evaluation Metrics
4.3. Implementation
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Feature Set (#) | Accuracy | Sensitivity | Specificity | AUC-ROC | MCC |
---|---|---|---|---|---|
AAC (20) | 0.77 (0.02) | 0.77 (0.02) | 0.78 (0.04) | 0.85 (0.01) | 0.55 (0.04) |
0.78 (0.02) | 0.76 (0.01) | 0.81 (0.03) | 0.86 (0.02) | 0.56 (0.03) | |
CTD (147) | 0.79 (0.02) | 0.77 (0.03) | 0.82 (0.03) | 0.87 (0.02) | 0.59 (0.04) |
0.80 (0.01) | 0.79 (0.02) | 0.82 (0.02) | 0.88 (0.01) | 0.61 (0.03) | |
DPC (400) | 0.77 (0.02) | 0.78 (0.03) | 0.76 (0.03) | 0.85 (0.02) | 0.53 (0.04) |
0.77 (0.02) | 0.77 (0.02) | 0.78 (0.03) | 0.86 (0.01) | 0.55 (0.04) | |
PseAAC (24) | 0.77 (0.02) | 0.76 (0.03) | 0.79 (0.02) | 0.85 (0.02) | 0.55 (0.04) |
0.78 (0.02) | 0.75 (0.03) | 0.81 (0.02) | 0.86 (0.01) | 0.55 (0.04) | |
GPC (10) | 0.78 (0.01) | 0.78 (0.02) | 0.79 (0.03) | 0.85 (0.01) | 0.57 (0.02) |
0.78 (0.01) | 0.78 (0.03) | 0.79 (0.02) | 0.86 (0.01) | 0.57 (0.02) | |
All (601) | 0.81 (0.02) | 0.80 (0.03) | 0.83 (0.03) | 0.89 (0.02) | 0.62 (0.05) |
0.81 (0.02) | 0.80 (0.04) | 0.82 (0.03) | 0.89 (0.02) | 0.62 (0.05) |
Model | Accuracy | Sensitivity | Specificity | AUC-ROC | MCC |
---|---|---|---|---|---|
iAMPpred | 0.52 | 0.77 | 0.27 | 0.56 | 0.04 |
ClassAMP | 0.48 | 0.33 | 0.63 | - 1 | −0.03 |
Antifp | 0.50 | 0.30 | 0.70 | - 1 | 0.00 |
Average | 0.50 | 0.47 | 0.53 | - | 0.00 |
CalcAFP | 0.77 | 0.63 | 0.90 | 0.86 | 0.55 |
Database | Number of Unique Sequence 2 |
---|---|
ADAM 1 (A Database of Anti-Microbial Peptides) http://bioinformatics.cs.ntou.edu.tw/ADAM/index.html | 7007 |
BaAMPs 1 (Biofilm-Active AMPs Database) http://www.baamps.it/ | 225 |
CAMP (Collection of Anti-Microbial Peptides) http://www.camp.bicnirrh.res.in/ | 8177 |
DBAASP (Database of Antimicrobial Activity and Structure of Peptides) https://dbaasp.org/ | 17,783 |
DRAMP (Data Repository of Antimicrobial Peptides) http://dramp.cpu-bioinfor.org/ | 22,259 |
LAMP2 (Linking Antimicrobial Peptides) http://biotechlab.fudan.edu.cn/database/lamp/index.php) | 23,253 |
YADAMP (Yet Another Database of Antimicrobial Peptides) http://yadamp.unisa.it/ | 2525 |
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Bournez, C.; Riool, M.; de Boer, L.; Cordfunke, R.A.; de Best, L.; van Leeuwen, R.; Drijfhout, J.W.; Zaat, S.A.J.; van Westen, G.J.P. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics 2023, 12, 725. https://doi.org/10.3390/antibiotics12040725
Bournez C, Riool M, de Boer L, Cordfunke RA, de Best L, van Leeuwen R, Drijfhout JW, Zaat SAJ, van Westen GJP. CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics. 2023; 12(4):725. https://doi.org/10.3390/antibiotics12040725
Chicago/Turabian StyleBournez, Colin, Martijn Riool, Leonie de Boer, Robert A. Cordfunke, Leonie de Best, Remko van Leeuwen, Jan Wouter Drijfhout, Sebastian A. J. Zaat, and Gerard J. P. van Westen. 2023. "CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides" Antibiotics 12, no. 4: 725. https://doi.org/10.3390/antibiotics12040725
APA StyleBournez, C., Riool, M., de Boer, L., Cordfunke, R. A., de Best, L., van Leeuwen, R., Drijfhout, J. W., Zaat, S. A. J., & van Westen, G. J. P. (2023). CalcAMP: A New Machine Learning Model for the Accurate Prediction of Antimicrobial Activity of Peptides. Antibiotics, 12(4), 725. https://doi.org/10.3390/antibiotics12040725