Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs
The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the develo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/24/7/1258 |
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| author | Rodrigo A. Nava Lara Longendri Aguilera-Mendoza Carlos A. Brizuela Antonio Peña Gabriel Del Rio |
| author_facet | Rodrigo A. Nava Lara Longendri Aguilera-Mendoza Carlos A. Brizuela Antonio Peña Gabriel Del Rio |
| author_sort | Rodrigo A. Nava Lara |
| collection | DOAJ |
| description | The emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds. |
| first_indexed | 2024-12-20T09:45:21Z |
| format | Article |
| id | doaj.art-4ed99d83c5704702b4c021aac7333f12 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-12-20T09:45:21Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-4ed99d83c5704702b4c021aac7333f122022-12-21T19:44:46ZengMDPI AGMolecules1420-30492019-03-01247125810.3390/molecules24071258molecules24071258Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted DrugsRodrigo A. Nava Lara0Longendri Aguilera-Mendoza1Carlos A. Brizuela2Antonio Peña3Gabriel Del Rio4Department of biochemistry and structural biology, Instituto de Fisiología Celular, UNAM, Mexico City 04510, MexicoComputer Science Department, CICESE Research Center, Ensenada, Baja California 22860, MexicoComputer Science Department, CICESE Research Center, Ensenada, Baja California 22860, MexicoDepartment of genetics, Instituto de Fisiología Celular, UNAM, Mexico City 04510, MexicoDepartment of biochemistry and structural biology, Instituto de Fisiología Celular, UNAM, Mexico City 04510, MexicoThe emergence of microbes resistant to common antibiotics represent a current treat to human health. It has been recently recognized that non-antibiotic labeled drugs may promote antibiotic-resistance mechanisms in the human microbiome by presenting a secondary antibiotic activity; hence, the development of computer-assisted procedures to identify antibiotic activity in human-targeted compounds may assist in preventing the emergence of resistant microbes. In this regard, it is worth noting that while most antibiotics used to treat human infectious diseases are non-peptidic compounds, most known antimicrobials nowadays are peptides, therefore all computer-based models aimed to predict antimicrobials either use small datasets of non-peptidic compounds rendering predictions with poor reliability or they predict antimicrobial peptides that are not currently used in humans. Here we report a machine-learning-based approach trained to identify gut antimicrobial compounds; a unique aspect of our model is the use of heterologous training sets, in which peptide and non-peptide antimicrobial compounds were used to increase the size of the training data set. Our results show that combining peptide and non-peptide antimicrobial compounds rendered the best classification of gut antimicrobial compounds. Furthermore, this classification model was tested on the latest human-approved drugs expecting to identify antibiotics with broad-spectrum activity and our results show that the model rendered predictions consistent with current knowledge about broad-spectrum antibiotics. Therefore, heterologous machine learning rendered an efficient computational approach to classify antimicrobial compounds.https://www.mdpi.com/1420-3049/24/7/1258machine-learningantimicrobial peptidenon-peptidic antimicrobial compoundantimicrobial activity |
| spellingShingle | Rodrigo A. Nava Lara Longendri Aguilera-Mendoza Carlos A. Brizuela Antonio Peña Gabriel Del Rio Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs Molecules machine-learning antimicrobial peptide non-peptidic antimicrobial compound antimicrobial activity |
| title | Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs |
| title_full | Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs |
| title_fullStr | Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs |
| title_full_unstemmed | Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs |
| title_short | Heterologous Machine Learning for the Identification of Antimicrobial Activity in Human-Targeted Drugs |
| title_sort | heterologous machine learning for the identification of antimicrobial activity in human targeted drugs |
| topic | machine-learning antimicrobial peptide non-peptidic antimicrobial compound antimicrobial activity |
| url | https://www.mdpi.com/1420-3049/24/7/1258 |
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