Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Transformation
2.2. Model Generation and Evaluation
2.3. Prediction of the Target Protein by Jamu Formulas
3. Results and Discussion
3.1. Preprocessing of Compound and Protein Data
3.2. Prediction Performance
3.3. Prediction Results
4. Conclusions and Future Works
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Description | Number of Data | Identifier | References |
---|---|---|---|
Protein | 3.334 | UniProtID | UniProt, IJAH |
Compound | 17.277 | CAS_ID, PubChem ID, KEGG ID | KNApSAcK, PubChem, KEGG, IJAH |
Compound of Jamu | 94 | Compound ID | Wijaya et al. [11] |
Compound–protein interactions | 149 | KEGG | |
4.144 | Matador | ||
3.696 | UniProt, IJAH | ||
Amino acid sequences | 3.334 | UniProtID | UniProt |
ID | Efficacy Groups | Number of Compounds |
---|---|---|
E1 | Blood and Lymph Diseases | 15 |
E2 | Cancers | 5 |
E3 | The Digestive System | 17 |
E4 | Female-Specific Diseases | 16 |
E5 | The Heart and Blood Vessels | 4 |
E6 | Male-Specific Diseases | 5 |
E7 | Muscle and Bone | 18 |
E8 | Nutritional and Metabolic Diseases | 7 |
E9 | Respiratory Diseases | 32 |
E10 | Skin and Connective Tissue | 4 |
E11 | The Urinary System | 14 |
E12 | Mental and Behavioral Disorders | 8 |
Datasets | Classifiers | Accuracy | Sensitivity | Specificity | |||
---|---|---|---|---|---|---|---|
MACCS_AAC | SVM Linear | 69.16%± | 1.07% | 71.52%± | 1.84% | 66.77%± | 1.87% |
SVM RBF | 81.71%± | 1.52% | 82.79%± | 2.27% | 80.62%± | 1.27% | |
Random Forest | 89.30%± | 0.69% | 87.86%± | 1.20% | 90.74%± | 1.05% | |
MACCS_Dipeptide | SVM Linear | 61.68%± | 0.77% | 63.49%± | 1.61% | 61.27%± | 0.88% |
SVM RBF | 72.71%± | 0.86% | 71.81%± | 1.81% | 73.15%± | 1.15% | |
Random Forest | 60.79%± | 1.20% | 59.14%± | 1.56% | 61.17%± | 1.30% | |
PubChem_AAC | SVM Linear | 70.77%± | 0.90% | 73.08%± | 1.86% | 68.49%± | 1.87% |
SVM RBF | 80.01%± | 1.35% | 80.52%± | 1.80% | 79.51%± | 1.82% | |
Random Forest | 89.28%± | 0.40% | 87.96%± | 0.88% | 90.63%± | 0.58% | |
PubChem_Dipeptide | SVM Linear | 50.49%± | 1.08% | 54.15%± | 1.38% | 50.47%± | 1.01% |
SVM RBF | 49.55%± | 1.28% | 54.83%± | 5.44% | 49.56%± | 1.19% | |
Random Forest | 50.28%± | 0.72% | 50.12%± | 1.60% | 50.28%± | 0.71% |
No | Compound ID | Compound Name | Molecular Formula | UniProt ID | Targeted Protein | OMIM ID | Disease Description |
---|---|---|---|---|---|---|---|
E1 Blood and Lymph Diseases | |||||||
1 | N/A | (4Z)-1-(2,3,5-Trihydroxy-4-methylphenyl)dec-4-en-1-one | C17H24O4 | P02768 | Serum albumin | 615999; 616000 | Hyperthyroxinemia, familial dysalbuminemic; analbuminemia |
2 | 689043, C00000615 | Caffeic acid | C9H8O4 | ||||
3 | 5317587, | Diacetoxy-[6]-gingerdiol | C21H32O6 | ||||
4 | 370, C00002647 | Gallic acid | C7H6O5 | ||||
5 | 5280445, C00000674 | Luteolin | C15H10O6 | ||||
6 | 5280459, C00005373 | Quercitrin | C21H20O11 | ||||
7 | 5281702, C00013329 | Tricin | C17H14O7 | ||||
8 | 64945, C00003558 | Ursolic acid | C30H48O3 | Q92887 | Canalicular multispecific organic anion transporter 1 | 237500 | Dubin–Johnson syndrome |
Q9NPD5 | Solute carrier organic anion transporter family member 1B3 | 237450 | Hyperbilirubinemia, rotor type | ||||
Q9Y6L6 | Solute carrier organic anion transporter family member 1B1 | Hyperbilirubinemia, rotor type | |||||
P08185 | Corticosteroid-binding globulin | 611489 | Corticosteroid-binding globulin deficiency | ||||
P02768 | Serum albumin | 615999; 616000 | Hyperthyroxinemia, familial dysalbuminemic; analbuminemia | ||||
9 | 73145, C00003738 | beta-Amyrin | C30H50O | Q92887 | Canalicular multispecific organic anion transporter 1 | 237500 | Dubin–Johnson syndrome |
10 | 222284, C00003672 | beta-Sitosterol | C29H50O | Dubin–Johnson syndrome | |||
E3 The Digestive System | |||||||
1 | 519857, C00020146 | 1-epi-Cubenol | C15H26O | P08183 | Multidrug resistance protein 1 | 612244 | Inflammatory bowel disease 13 |
2 | N/A | Anisucumarin A | C20H20O8 | ||||
3 | 240, C00034452 | Benzaldehyde | C7H6O | ||||
4 | 6448, C00029844 | Bornyl acetate | C12H20O2 | ||||
5 | 3314, C00000619 | Eugenol | C10H12O2 | ||||
6 | 289151, C00003162 | Longifolene | C15H24 | ||||
7 | N/A | Morin-3-O-lyxoside | C20H18O11 | ||||
8 | 985, C00001233 | Palmitic acid | C16H32O2 | ||||
9 | 442402, C00003194 | Thujopsene | C15H24 | ||||
10 | 12306047, C00029671 | alpha-Muurolene | C15H24 | ||||
11 | 7460, C00003051 | alpha-Phellandrene | C10H16 | ||||
12 | 111037, C00035043 | alpha-Terpinyl acetate | C12H20O2 | ||||
13 | 12313020, C00020130 | gamma-Muurolene | C15H24 | ||||
E4 Female-Specific Diseases | |||||||
1 | 5280794, C00003674 | Stigmasterol | C29H48O | P11511 | Aromatase | 139300; 613546 | Aromatase excess syndrome; aromatase deficiency |
P03372 | Estrogen receptor | 615363 | Estrogen resistance | ||||
E7 Muscle and Bone | |||||||
1 | 10131321, C00055009 | Coumaperine | C16H19NO2 | P20309 | Muscarinic acetylcholine receptor M3 | 100100 | Prune belly syndrome |
E8 Nutritional and Metabolic Diseases | |||||||
1 | 3084331, C00020154 | T-Muurolol | C15H26O | Q92887 | Canalicular multispecific organic anion transporter 1 | 237500 | Dubin–Johnson syndrome |
Q02318 | Sterol 26-hydroxylase, mitochondrial | 213700 | Cerebrotendinous xanthomatosis | ||||
P11473 | Vitamin D3 receptor | 277440 | Rickets vitamin D-dependent 2A | ||||
E10 Skin and Connective Tissue | |||||||
1 | 222284, C00003672 | beta-Sitosterol | C29H50O | Q02318 | Sterol 26-hydroxylase, mitochondrial | 213700 | Cerebrotendinous xanthomatosis |
E12 Mental and Behavioral Disorders | |||||||
1 | 6989, C00000155 | Thymol | C10H14O | P08172 | Muscarinic acetylcholine receptor M2 | 608516 | Major depressive disorder |
Q13002 | Glutamate receptor ionotropic, kainate 2 | 611092 | Mental retardation, autosomal recessive 6 |
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Wijaya, S.H.; Afendi, F.M.; Batubara, I.; Huang, M.; Ono, N.; Kanaya, S.; Altaf-Ul-Amin, M. Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach. Life 2021, 11, 866. https://doi.org/10.3390/life11080866
Wijaya SH, Afendi FM, Batubara I, Huang M, Ono N, Kanaya S, Altaf-Ul-Amin M. Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach. Life. 2021; 11(8):866. https://doi.org/10.3390/life11080866
Chicago/Turabian StyleWijaya, Sony Hartono, Farit Mochamad Afendi, Irmanida Batubara, Ming Huang, Naoaki Ono, Shigehiko Kanaya, and Md. Altaf-Ul-Amin. 2021. "Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach" Life 11, no. 8: 866. https://doi.org/10.3390/life11080866
APA StyleWijaya, S. H., Afendi, F. M., Batubara, I., Huang, M., Ono, N., Kanaya, S., & Altaf-Ul-Amin, M. (2021). Identification of Targeted Proteins by Jamu Formulas for Different Efficacies Using Machine Learning Approach. Life, 11(8), 866. https://doi.org/10.3390/life11080866