De novo design of anti-tuberculosis agents using a structure-based deep learning method
Copyright © 2022 Elsevier Inc. All rights reserved..
Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:118 |
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| Enthalten in: |
Journal of molecular graphics & modelling - 118(2023) vom: 15. Jan., Seite 108361 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Krishnan, Sowmya Ramaswamy [VerfasserIn] |
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| Links: |
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| Themen: |
Antitubercular Agents |
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| Anmerkungen: |
Date Completed 02.12.2022 Date Revised 09.02.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jmgm.2022.108361 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 520 | |a Copyright © 2022 Elsevier Inc. All rights reserved. | ||
| 520 | |a Mycobacterium tuberculosis (Mtb) is a pathogen of major concern due to its ability to withstand both first- and second-line antibiotics, leading to drug resistance. Thus, there is a critical need for identification of novel anti-tuberculosis agents targeting Mtb-specific proteins. The ceaseless search for novel antimicrobial agents to combat drug-resistant bacteria can be accelerated by the development of advanced deep learning methods, to explore both existing and uncharted regions of the chemical space. The adaptation of deep learning methods to under-explored pathogens such as Mtb is a challenging aspect, as most of the existing methods rely on the availability of sufficient target-specific ligand data to design novel small molecules with optimized bioactivity. In this work, we report the design of novel anti-tuberculosis agents targeting the Mtb chorismate mutase protein using a structure-based drug design algorithm. The structure-based deep learning method relies on the knowledge of the target protein's binding site structure alone for conditional generation of novel small molecules. The method eliminates the need for curation of a high-quality target-specific small molecule dataset, which remains a challenge even for many druggable targets, including Mtb chorismate mutase. Novel molecules are proposed, that show high complementarity to the target binding site. The graph attention model could identify the probable key binding site residues, which influenced the conditional molecule generator to design new molecules with pharmacophoric features similar to the known inhibitors | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Chorismate mutase | |
| 650 | 4 | |a Computational chemistry | |
| 650 | 4 | |a Deep learning | |
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| 700 | 1 | |a Padhi, Siladitya |e verfasserin |4 aut | |
| 700 | 1 | |a Bulusu, Gopalakrishnan |e verfasserin |4 aut | |
| 700 | 1 | |a Misra, Parimal |e verfasserin |4 aut | |
| 700 | 1 | |a Pal, Manojit |e verfasserin |4 aut | |
| 700 | 1 | |a Oruganti, Srinivas |e verfasserin |4 aut | |
| 700 | 1 | |a Srinivasan, Rajgopal |e verfasserin |4 aut | |
| 700 | 1 | |a Roy, Arijit |e verfasserin |4 aut | |
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