Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models
Abstract
:1. Introduction
2. Materials and Methods
2.1. Dataset and Data Preprocessing
2.1.1. Feature Generation
Generation of Physico-Chemical Features (Descriptors)
Generation of Sequence-Based, Structure-Based, and Linguistic-Based Features
2.2. Machine Learning Models
2.2.1. Model Construction
2.2.2. Performance Metrics
3. Results
3.1. Training Models Using Physico-Chemical Features
3.2. Data Exploration, Outlier Detection, and Elimination
3.3. Training Models Using an Extended Set of Features
3.4. Training Models Using an Extended Set of Features and Applying Feature Selection
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Name of Sequence | Sequence | Seq. Length | Norm. Hyd. Moment | Norm. Hyd. | Net Charge | Isoelectric Point | Penet. Depth | Tilt Angle | Disordered Conf. Propensity | Linear Moment | Propensity In Vitro Aggregation | Mean MIC | Class (AMP Category) |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
XPF-B2 | GWASKIGTQLGKMAKVGLKEFVQS | 24 | 1.11 | −0.25 | 3 | 10.7 | 15 | 76 | 0.09 | 0.16 | 0 | 256.81 | 0 |
Ovalbumin (271–290) | SNVMEERKIKVYLPRMKMEE | 20 | 0.13 | −0.28 | 1 | 9.38 | 30 | 67 | −0.11 | 0.29 | 0 | 800 | 0 |
MBI 29 A1 | KWKSFIKKLTSVLKKVVTTALPALIS | 26 | 1.03 | −0.54 | 6 | 11.37 | 12 | 106 | 0.16 | 0.27 | 3.4 | 9.33 | 1 |
Cyanophlyctin | FLNALKNFAKTAGKRLKSLLN | 21 | 1.69 | −0.24 | 5 | 11.74 | 15 | 88 | −0.03 | 0.25 | 0 | 12 | 1 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.85 ± 0.06 | 0.92 ± 0.06 | 0.72 ± 0.20 | 0.87 ± 0.07 | 0.88 ± 0.06 | 0.89 ± 0.04 | 0.82 ± 0.13 |
Decision Tree | 0.79 ± 0.06 | 0.87 ± 0.07 | 0.66 ± 0.24 | 0.84 ± 0.07 | 0.78 ± 0.07 | 0.85 ± 0.04 | 0.76 ± 0.15 |
LogitBoost | 0.86 ± 0.05 | 0.92 ± 0.06 | 0.74 ± 0.16 | 0.88 ± 0.06 | 0.89 ± 0.06 | 0.90 ± 0.03 | 0.83 ± 0.11 |
RF | 0.89 ± 0.05 | 0.93 ± 0.04 | 0.79 ± 0.16 | 0.90 ± 0.06 | 0.92 ± 0.05 | 0.91 ± 0.03 | 0.86 ± 0.10 |
SVM | 0.80 ± 0.05 | 0.93 ± 0.06 | 0.56 ± 0.21 | 0.81 ± 0.07 | 0.82 ± 0.06 | 0.86 ± 0.03 | 0.74 ± 0.13 |
SVM + kNN | 0.80 ± 0.07 | 0.93 ± 0.05 | 0.56 ± 0.25 | 0.81 ± 0.08 | 0.82 ± 0.08 | 0.86 ± 0.04 | 0.74 ± 0.15 |
LogitBoost + kNN | 0.80 ± 0.05 | 0.93 ± 0.06 | 0.56 ± 0.21 | 0.81 ± 0.07 | 0.82 ± 0.06 | 0.86 ± 0.03 | 0.74 ± 0.13 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.84 ± 0.06 | 0.85 ± 0.08 | 0.83 ± 0.14 | 0.83 ± 0.10 | 0.86 ± 0.06 | 0.83 ± 0.05 | 0.84 ± 0.11 |
Decision Tree | 0.77 ± 0.07 | 0.77 ± 0.10 | 0.77 ± 0.16 | 0.769 ± 0.09 | 0.77 ± 0.06 | 0.76 ± 0.05 | 0.77 ± 0.13 |
LogitBoost | 0.83 ± 0.06 | 0.84 ± 0.09 | 0.82 ± 0.15 | 0.83 ± 0.10 | 0.87 ± 0.05 | 0.83 ± 0.05 | 0.83 ± 0.12 |
RF | 0.87 ± 0.04 | 0.87 ± 0.07 | 0.87 ± 0.08 | 0.87 ± 0.07 | 0.90 ± 0.04 | 0.87 ± 0.04 | 0.87 ± 0.07 |
SVM | 0.77 ± 0.07 | 0.85 ± 0.11 | 0.71 ± 0.19 | 0.75 ± 0.12 | 0.81 ± 0.06 | 0.78 ± 0.05 | 0.78 ± 0.15 |
SVM + kNN | 0.76 ± 0.08 | 0.81 ± 0.11 | 0.72 ± 0.21 | 0.76 ± 0.13 | 0.80 ± 0.07 | 0.77 ± 0.05 | 0.76 ± 0.16 |
LogitBoost + kNN | 0.77 ± 0.07 | 0.85 ± 0.11 | 0.71 ± 0.19 | 0.75 ± 0.12 | 0.81 ± 0.06 | 0.78 ± 0.05 | 0.78 ± 0.15 |
Features | Gram-Negative Dataset | Gram-Positive Dataset | ||
---|---|---|---|---|
Minimum Threshold | Maximum Threshold | Minimum Threshold | Maximum Threshold | |
Hydrophobic Moment | 0.4 | 2 | 0.1 | 1.7 |
Normalized Hydrophobicity | −0.9 | 0.55 | −0.8 | 1 |
Net Charge | 5 | 13 | 4 | 13 |
Isoelectric Point | 10.5 | 13 | 10 | 13 |
Penetration Depth | 13 | 30 | 12 | 30 |
Tilt Angle | 40 | 150 | 30 | 152 |
Linear Moment | 0.1 | 0.4 | 0.15 | 0.32 |
Propensity in vitro Aggregation | 0 | 250 | 0 | 87 |
Disordered Conformation Propensity | −0.5 | 0.08 | −0.85 | 0.15 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.97 ± 0.03 | 0.99 ± 0.03 | 0.96 ± 0.04 | 0.95 ± 0.05 | 0.99 ± 0.01 | 0.97 ± 0.03 | 0.97 ± 0.04 |
Decision Tree | 0.91 ± 0.06 | 0.92 ± 0.08 | 0.91 ± 0.08 | 0.89 ± 0.09 | 0.91 ± 0.06 | 0.90 ± 0.06 | 0.91 ± 0.08 |
LogitBoost | 0.97 ± 0.03 | 0.99 ± 0.02 | 0.96 ± 0.05 | 0.95 ± 0.05 | 0.99 ± 0.01 | 0.97 ± 0.03 | 0.98 ± 0.03 |
RF | 0.98 ± 0.02 | 0.99 ± 0.02 | 0.97 ± 0.04 | 0.97 ± 0.05 | 0.99 ± 0.01 | 0.98 ± 0.03 | 0.98 ± 0.03 |
SVM | 0.98 ± 0.02 | 0.99 ± 0.03 | 0.97 ± 0.04 | 0.96 ± 0.04 | 0.98 ± 0.01 | 0.97 ± 0.03 | 0.98 ± 0.03 |
SVM + kNN | 0.81 ± 0.11 | 0.82 ± 0.14 | 0.80 ± 0.24 | 0.81 ± 0.16 | 0.84 ± 0.10 | 0.80 ± 0.09 | 0.81 ± 0.19 |
LogitBoost + kNN | 0.98 ± 0.02 | 0.99 ± 0.03 | 0.97 ± 0.04 | 0.96 ± 0.04 | 0.98 ± 0.01 | 0.97 ± 0.03 | 0.98 ± 0.03 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.93 ± 0.04 | 0.92 ± 0.08 | 0.94 ± 0.06 | 0.89 ± 0.09 | 0.96 ± 0.03 | 0.90 ± 0.05 | 0.93 ± 0.07 |
Decision Tree | 0.88 ± 0.05 | 0.82 ± 0.12 | 0.91 ± 0.06 | 0.82 ± 0.11 | 0.86 ± 0.07 | 0.81 ± 0.09 | 0.86 ± 0.09 |
LogitBoost | 0.93 ± 0.05 | 0.93 ± 0.09 | 0.93 ± 0.07 | 0.88 ± 0.11 | 0.96 ± 0.03 | 0.90 ± 0.07 | 0.93 ± 0.08 |
RF | 0.95 ± 0.03 | 0.95 ± 0.07 | 0.95 ± 0.05 | 0.90 ± 0.09 | 0.97 ± 0.02 | 0.92 ± 0.05 | 0.95 ± 0.06 |
SVM | 0.91 ± 0.04 | 0.90 ± 0.11 | 0.91 ± 0.06 | 0.85 ± 0.11 | 0.93 ± 0.04 | 0.86 ± 0.06 | 091 ± 0.09 |
SVM + kNN | 0.77 ± 0.10 | 0.75 ± 0.16 | 0.78 ± 0.20 | 0.68 ± 0.17 | 0.81 ± 0.08 | 0.68 ± 0.08 | 0.76 ± 0.18 |
LogitBoost + kNN | 0.91 ± 0.04 | 0.90 ± 0.11 | 0.91 ± 0.06 | 0.85 ± 0.11 | 0.93 ± 0.04 | 0.86 ± 0.04 | 0.91 ± 0.09 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.96 ± 0.03 | 0.98 ± 0.04 | 0.95 ± 0.05 | 0.94 ± 0.06 | 0.98 ± 0.02 | 0.96 ± 0.03 | 0.96 ± 0.05 |
Decision Tree | 0.90 ± 0.06 | 0.90 ± 0.09 | 0.90 ± 0.08 | 0.88 ± 0.09 | 0.90 ± 0.06 | 0.88 ± 0.07 | 0.90 ± 0.08 |
LogitBoost | 0.97 ± 0.03 | 0.98 ± 0.03 | 0.95 ± 0.06 | 0.95 ± 0.06 | 0.98 ± 0.01 | 0.96 ± 0.03 | 0.97 ± 0.04 |
RF | 0.95 ± 0.04 | 0.98 ± 0.04 | 0.94 ± 0.06 | 0.93 ± 0.07 | 0.98 ± 0.02 | 0.95 ± 0.04 | 0.96 ± 0.05 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.89 ± 0.05 | 0.88 ± 0.10 | 0.90 ± 0.08 | 0.82 ± 0.11 | 0.93 ± 0.04 | 0.84 ± 0.07 | 0.89 ± 0.09 |
Decision Tree | 0.82 ± 0.10 | 0.74 ± 0.14 | 0.86 ± 0.16 | 0.75 ± 0.13 | 0.80 ± 0.07 | 0.73 ± 0.10 | 0.80 ± 0.15 |
LogitBoost | 0.90 ± 0.05 | 0.89 ± 0.09 | 0.91 ± 0.07 | 0.84 ± 0.11 | 0.94 ± 0.03 | 0.85 ± 0.06 | 0.90 ± 0.08 |
RF | 0.92 ± 0.04 | 0.91 ± 0.09 | 0.92 ± 0.06 | 0.86 ± 0.10 | 0.95 ± 0.03 | 0.88 ± 0.06 | 0.92 ± 0.08 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.94 ± 0.05 | 0.97 ± 0.05 | 0.91 ± 0.09 | 0.91 ± 0.09 | 0.95 ± 0.06 | 0.93 ± 0.07 | 0.94 ± 0.07 |
Decision Tree | 0.90 ± 0.07 | 0.90 ± 0.11 | 0.89 ± 0.09 | 0.87 ± 0.10 | 0.90 ± 0.08 | 0.88 ± 0.10 | 0.90 ± 0.10 |
LogitBoost | 0.94 ± 0.05 | 0.98 ± 0.04 | 0.91 ± 0.09 | 0.90 ± 0.09 | 0.96 ± 0.06 | 0.94 ± 0.07 | 0.95 ± 0.06 |
RF | 0.94 ± 0.05 | 0.97 ± 0.06 | 0.92 ± 0.08 | 0.91 ± 0.08 | 0.96 ± 0.05 | 0.93 ± 0.07 | 0.94 ± 0.07 |
Model | Accuracy | Recall | Specificity | Precision | Area Under Curve | F1 | Balanced Acc. |
---|---|---|---|---|---|---|---|
AdaBoost | 0.86 ± 0.06 | 0.91 ± 0.10 | 0.83 ± 0.10 | 0.74 ± 0.12 | 0.90 ± 0.05 | 0.80 ± 0.07 | 0.87 ± 0.10 |
Decision Tree | 0.83 ± 0.10 | 0.77 ± 0.12 | 0.86 ± 0.16 | 0.76 ± 0.14 | 0.82 ± 0.07 | 0.75 ± 0.10 | 0.82 ± 0.14 |
LogitBoost | 0.87 ± 0.05 | 0.90 ± 0.10 | 0.86 ± 0.08 | 0.77 ± 0.11 | 0.91 ± 0.04 | 0.82 ± 0.06 | 0.88 ± 0.09 |
RF | 0.90 ± 0.04 | 0.89 ± 0.10 | 0.91 ± 0.07 | 0.84 ± 0.11 | 0.94 ± 0.04 | 0.86 ± 0.06 | 0.90 ± 0.08 |
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Söylemez, Ü.G.; Yousef, M.; Kesmen, Z.; Büyükkiraz, M.E.; Bakir-Gungor, B. Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. Appl. Sci. 2022, 12, 3631. https://doi.org/10.3390/app12073631
Söylemez ÜG, Yousef M, Kesmen Z, Büyükkiraz ME, Bakir-Gungor B. Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. Applied Sciences. 2022; 12(7):3631. https://doi.org/10.3390/app12073631
Chicago/Turabian StyleSöylemez, Ümmü Gülsüm, Malik Yousef, Zülal Kesmen, Mine Erdem Büyükkiraz, and Burcu Bakir-Gungor. 2022. "Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models" Applied Sciences 12, no. 7: 3631. https://doi.org/10.3390/app12073631
APA StyleSöylemez, Ü. G., Yousef, M., Kesmen, Z., Büyükkiraz, M. E., & Bakir-Gungor, B. (2022). Prediction of Linear Cationic Antimicrobial Peptides Active against Gram-Negative and Gram-Positive Bacteria Based on Machine Learning Models. Applied Sciences, 12(7), 3631. https://doi.org/10.3390/app12073631