همایش ملی بیوانفورماتیک ایران

صفحه اصلی / 4th international edition and 13th Iranian Conference on Bioinformatics

Herbal Drug Candidate as DNA Gyrase Inhibitor in M. Tuberculosis (Causative Agent of Tuberculosis)

نویسندگان :

Ali Abolhasanzadeh Parizi¹

1- University of Sistan and Baluchestan

کلمات کلیدی :

Tuberculosis (TB)،Mycobacterium tuberculosis،CAS/NITR204 strain،DNA Gyrase،Herbal medicine

چکیده :

Introduction: Tuberculosis (TB) remains a global health emergency according to WHO (Chakaya et al., 2021), with Sistan and Baluchistan province showing concerning rates, probably related to its long border with Pakistan and Afghanistan, where the CAS family of TB bacteria is prevalent (Haeili et al., 2013). TB, caused by Mycobacterium tuberculosis (Shradheya et al., 2020), primarily affects the lungs (Beiranvand et al., 2014). Some recent studies indicate that natural compounds may offer promising treatment options (Shashidhar et al., 2015, Verma et al., 2023). This research aims to use computational tools to 1) analyze drug resistance probability in the most studied CAS strain (CAS/NITR204) and 2) identify a potential herbal drug candidate that inhibits bacterial DNA gyrase, an essential enzyme for DNA replication, comparing it with moxifloxacin, a known synthetic antibiotic against the bacterial DNA gyrase. Methods: The study first compared protein sequences from H37Rv and CAS/NITR204 strains, focusing on rpoB, inhA, katG, pncA, gyrA, and gyrB from UniProt and NCBI databases using Clustal Omega alignment (Sievers and Higgins, 2014). The 3D structure of the drug target was prepared using the PDB database (Berman et al., 2000). Further investigations assessed drug resistance probability in the CAS/NITR204 variant. Molecular docking was performed using Maestro software (Schrödinger LLC) with about 450 natural ligands collected from Sistan and Baluchistan medicinal plants. Docking utilized Glide module with extra precision mode, and protein-ligand binding energies were calculated via MM-GBSA, compared to moxifloxacin. Top candidates were visualized in PyMOL (Schrödinger LLC) to analyze their positioning in the enzyme active site. Results: The analyzed proteins showed that GyrA (DNA Gyrase subunit A) had five mutations, including two in the Quinolone Resistance-Determining Regions (QRDR). Although these mutations did not align with known patterns that typically cause resistance, GyrA was identified as the most likely target for further investigation into drug resistance. Additional studies revealed no significant differences in the interaction patterns of moxifloxacin between the CAS/NITR204 and H37Rv strains, suggesting that the CAS/NITR204 strain has likely not developed antibiotic resistance. Two natural compounds derived from the black myrobalan plant (Terminalia chebula) were identified as the most promising potential inhibitors of DNA gyrase. The first compound, Chebulinic Acid (ZINC000169356891), exhibited mean docking and MM-GBSA scores of -48.15 kcal/mol for the H37Rv strain and -42.9 kcal/mol for the CAS/NITR204 strain in chain 1. The second compound, 3,4,6-tri-O-galloyl-beta-D-glucose (PubChem CID: 14188641), demonstrated scores of -38.13 kcal/mol for H37Rv and -39.58 kcal/mol for CAS/NITR204 in chain 2. In comparison, moxifloxacin recorded approximately -25.7 kcal/mol scores for both strains and chains. Both natural compounds showed a stronger binding affinity than moxifloxacin, suggesting that Terminalia chebula could be a viable treatment option for both strains. Conclusion: This study identified a local medicinal plant from Sistan and Baluchistan as a potential treatment for tuberculosis, a disease prevalent in this region. Two compounds derived from the myrobalan plant (Terminalia chebula) exhibited stronger binding affinities to the bacterial DNA gyrase than moxifloxacin. This suggests potential efficacy against the H37Rv and CAS/NITR204 strains, although further clinical validation is necessary.

لیست مقالات

لیست مقالات بایگانی شده

Harnessing Deep Learning for Epitope Prediction in Immunoinformatics: A Novel Framework for Vaccine Design

Ali Rahmati hagh bonab - Hannaneh sadat Jalilzadeh

Drug reproposing for brain cancer

Zahra Shahini - Farinaz Roshani

Enhancing Predictive Accuracy of CRISPR-Cas9 on-target efficiency using Deep Learning and Active Learning Optimization for Small Datasets

Masoud Mahdavifar - Fateme Zaremirakabad

Applying immunoinformatics methods for Multiepitope Vaccine Design against HIV virus Based on the INT, RT, PRO genes

Fatemeh Hassanzadeh - Zahra Hassanzadeh - Ava Hashempour

A computational approach to identify the biomarker based on the RNA sequencing data analysis for Alzheimer’s disease

Atena Vaghf - Shahram Tahmasebian - Nayereh Abdali

PPI network construction and Cytoscape analysis for metastatic gastric cancer to ovary

Ameneh Hassani - Ghasem Bagherpour - Ali Salari - Behrooz Johari - Abdolmajid Ghaempanah

Integrating Biological Networks and Deep Learning for Microbe-Disease Prediction

Sobhan Ahmadian Moghadam - Zahra Ghorbanali - Fatemeh Zare-Mirakabad - Hesam Montazeri

Bioinformatic Approach to Predict the Regulatory Mechanisms: TF–miRNA–mRNA–lncRNA Network during Cluster Development in Grape

Shahla Sahraei - Nafiseh Mahdinezhad - Abbasali Emamjomeh - Kaveh Kavousi - Mahmood Solouki - Massimo Delledonne

BIRC5: The Silent Architect of Tumor Persistence and Senescence in Hepatocellular Carcinoma

Amirhosein Farrokhzad - Maryam Kaboli - Elahe Hoseinnia - Elham Rismani - Massoud Vosough

Insilico study of CD4+ T cells epitopes in ORF1ab protein of SARS-COV-2 for Iranian common MHCII alleles

Fatemeh Hajighasem - Atefeh Shirkavand

بیشتر