Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance
Antimicrobial resistance (AMR) as one of the most serious problems in the world is especially urgent with the increase in antibiotic resistance of bacteria across the world. Antibiotics reach the environment via excretions from humans and agriculture, and industrial and hospital waste products. The...
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| Format: | Thesis (University of Nottingham only) |
| Language: | English |
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2024
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| Online Access: | https://eprints.nottingham.ac.uk/78201/ |
| _version_ | 1848801058760949760 |
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| author | Chio, Hok In |
| author_facet | Chio, Hok In |
| author_sort | Chio, Hok In |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Antimicrobial resistance (AMR) as one of the most serious problems in the world is especially urgent with the increase in antibiotic resistance of bacteria across the world. Antibiotics reach the environment via excretions from humans and agriculture, and industrial and hospital waste products. The environmental concentrations of antibiotics are usually much lower than the minimal inhibitory concentrations and most often lower than concentrations predicted to select for resistant strains in the laboratory. However, exposure to low levels of antibiotics has also been shown to increase resistance, resulting in the increase of selective pressure.
The resistance pattern of the AMR in different environments has been identified by many studies but the connection between the antibiotics present in the environment and the resistance pattern remains uncertain. To understand how different patterns of resistance emerge, computational method is essential for processing and analyzing the molecular interaction model to estimate the bioactivity of the metabolites of antibiotics and evaluated methods for visualizing high dimensional resistance data, in order to be able to better ascertain patterns of resistance.
Through molecular docking and molecular dynamics, the metabolites (5R) pseudopenicillin, (5S)-penicilloic acid and 6APA are found to be potentially bioactive towards target protein penicillin binding protein. T-SNE has been suggested to be the most suitable for analyzing AMR data compared with other methods (PCA, MDS, isomap and PHATE) and this helps to have a better understanding of correlative of the AMR development. Therefore, some undetected compounds (metabolites of antibiotics) may cause selective pressure and increase resistance. These compounds may also be involved in developing bacteria resistance within environments. This could have considerable significance for environmental surveillance for antibiotics to reduce antimicrobial resist. |
| first_indexed | 2025-11-14T21:01:25Z |
| format | Thesis (University of Nottingham only) |
| id | nottingham-78201 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T21:01:25Z |
| publishDate | 2024 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-782012024-07-23T04:40:33Z https://eprints.nottingham.ac.uk/78201/ Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance Chio, Hok In Antimicrobial resistance (AMR) as one of the most serious problems in the world is especially urgent with the increase in antibiotic resistance of bacteria across the world. Antibiotics reach the environment via excretions from humans and agriculture, and industrial and hospital waste products. The environmental concentrations of antibiotics are usually much lower than the minimal inhibitory concentrations and most often lower than concentrations predicted to select for resistant strains in the laboratory. However, exposure to low levels of antibiotics has also been shown to increase resistance, resulting in the increase of selective pressure. The resistance pattern of the AMR in different environments has been identified by many studies but the connection between the antibiotics present in the environment and the resistance pattern remains uncertain. To understand how different patterns of resistance emerge, computational method is essential for processing and analyzing the molecular interaction model to estimate the bioactivity of the metabolites of antibiotics and evaluated methods for visualizing high dimensional resistance data, in order to be able to better ascertain patterns of resistance. Through molecular docking and molecular dynamics, the metabolites (5R) pseudopenicillin, (5S)-penicilloic acid and 6APA are found to be potentially bioactive towards target protein penicillin binding protein. T-SNE has been suggested to be the most suitable for analyzing AMR data compared with other methods (PCA, MDS, isomap and PHATE) and this helps to have a better understanding of correlative of the AMR development. Therefore, some undetected compounds (metabolites of antibiotics) may cause selective pressure and increase resistance. These compounds may also be involved in developing bacteria resistance within environments. This could have considerable significance for environmental surveillance for antibiotics to reduce antimicrobial resist. 2024-07-23 Thesis (University of Nottingham only) NonPeerReviewed application/pdf en cc_by https://eprints.nottingham.ac.uk/78201/1/Hokin%20Chio%204342366%20thesis%20%28corrected%29.pdf Chio, Hok In (2024) Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance. PhD thesis, University of Nottingham. High dimension reduction methods; Database analysis; Penicillin; Metabolites resistance; Molecular docking; Molecular dynamics |
| spellingShingle | High dimension reduction methods; Database analysis; Penicillin; Metabolites resistance; Molecular docking; Molecular dynamics Chio, Hok In Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title | Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title_full | Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title_fullStr | Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title_full_unstemmed | Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title_short | Use of Computational Methods To Understand The Pattern Of Antimicrobial Resistance |
| title_sort | use of computational methods to understand the pattern of antimicrobial resistance |
| topic | High dimension reduction methods; Database analysis; Penicillin; Metabolites resistance; Molecular docking; Molecular dynamics |
| url | https://eprints.nottingham.ac.uk/78201/ |