It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
Abstract
Mycobacterial pathogens present a significant challenge to disease control efforts globally due to their inherent resistance to multiple antibiotics. The rise of drug-resistant strains of Mycobacterium tuberculosis has prompted an urgent need for innovative therapeutic solutions. One promising way to discover new tuberculosis drugs is by utilizing natural products from the vast biochemical space. Multidisciplinary methods can used to harness the bioactivity of these natural products. This study aimed to evaluate the antimycobacterial efficacy of functional crude extracts from bacteria isolated from gold mine tailings in South Africa. Bacterial strains were identified using 16S rRNA sequencing. The crude extracts obtained from the bacteria were tested against Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. Untargeted HPLC-qTOF and molecular networking were used to identify the functional constituents present in extracts that exhibited inhibitory activity. A virtual screening workflow (VSW) was used to filter compounds that were strong binders to Mycobacterium tuberculosis Pks13 and PknG. The ligands returned from the VSW were subjected to optimization using density functional theory (DFT) at M06-2X/6-311++ (d,p) level of theory and basis set implemented in Gaussian16 Rev.C01. The optimized ligands were re-docked against Mycobacterium tuberculosis Pks13 and PknG. Molecular dynamics simulation and molecular mechanics generalized born surface area were used to evaluate the stability of the protein–ligand complexes formed by the identified hits. The hit that showed promising binding characteristics was virtually modified through multiple synthetic routes using reaction-driven enumeration. Three bacterial isolates showed significant activity against the two strains of Mycobacterium, while only two, Bacillus subtilis and Bacillus licheniformis, exhibited activity against both Mycobacterium tuberculosis H37Rv, Mycobacterium smegmatis mc2155, and Mycobacterium aurum A+. The tentatively identified compounds from the bacterial crude extracts belonged to various classes of natural compounds associated with antimicrobial activity. Two compounds, cyclo-(L-Pro-4-OH-L-Leu) and vazabitide A, showed strong binding against PknG and Pks13, with pre-MD MM-GBSA values of − 42.8 kcal/mol and − 47.6 kcal/mol, respectively. The DFT-optimized compounds exhibited the same docking scores as the ligands optimized using the OPSL-4 force field. After modifying vazabitide A, its affinity to the Pks13 binding site increased to − 85.8 kcal/mol, as revealed by the post-MD MM-GBSA analysis. This study highlights the potential of bacteria isolates from gold mine tailings as a source of new scaffolds for designing and optimizing anti-Mycobacterium agents. These agents synthesized in-silico can be further tested in-vitro to evaluate their efficacy.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details
1 Stellenbosch University, DST-NRF Centre of Excellence for Biomedical Tuberculosis Research; South African Medical Research Council Centre for Tuberculosis Research; Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Cape Town, South Africa (GRID:grid.11956.3a) (ISNI:0000 0001 2214 904X)
2 University of Zimbabwe, Department of Biotechnology and Biochemistry, Harare, Zimbabwe (GRID:grid.13001.33) (ISNI:0000 0004 0572 0760)
3 University of Johannesburg, Department of Chemical Sciences, Johannesburg, South Africa (GRID:grid.412988.e) (ISNI:0000 0001 0109 131X); National Institute for Theoretical and Computational Sciences (NITheCS), Cape Town, South Africa (GRID:grid.412988.e)
4 University of Stellenbosch, Computer Science Division, Department of Mathematical Sciences, Faculty of Science, Matieland, South Africa (GRID:grid.11956.3a) (ISNI:0000 0001 2214 904X)
5 University of Cape Town, Division of Clinical Pharmacology, Department of Medicine, Faculty of Health Sciences, Cape Town, South Africa (GRID:grid.7836.a) (ISNI:0000 0004 1937 1151)




