Computational Investigation of Phytochemicals Targeting Isocitrate Lyase to Inhibit Mycobacterium Tuberculosis.
Current drug discovery technologies(2025)
Abstract
INTRODUCTION:The global burden of tuberculosis (TB) remains a major concern for society that is worsening day by day with the emergence of drug-resistant TB as well as risks associated with latent TB. Isocitrate lyase (ICL) has been shown as a potential target that plays a role in the la-tent/dormant stage of M. tuberculosis. Several inhibitors against ICL have been designed and tested, which have various side effects. METHODOLOGY:This study focuses on the phytochemicals from plant extracts, which have anti-tuber-cular properties. A total of 1413 phytochemicals were virtually screened against ICL to identify the promising therapeutic compounds. The top four lead phytochemicals were selected based on their binding energy and subjected to redocking and intermolecular interaction analysis. These results were further validated through 100 ns MD simulation to check the stability of these complexes. The find-ings of these complexes were compared to the reference compound VGX. RESULTS:The top selected compound viz., Allantoin, Gallic acid, Citric acid, and 3,5-Dihydroxyben-zoic acid from virtual screening result displayed better docking score ranging from -8 kcal/mol to -7.2 kcal/mol than the reference compound VGX (-7.5 kcal/mol). Moreover, during the MD simula-tion analysis, gallic acid exhibited greater stability compared to all other compounds, including the reference compound. CONCLUSION:Among selected phytochemicals, gallic acid exhibited highest stability and binding af-finity within the active site of ICL as compared to previously identified compounds, which suggests that it is as potential candidate against ICL. That can be used for further in vitro and in vivo studies to evaluate its effectiveness against TB.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined