References
- Auld, S. C., Shah, N. S., Mathema, B., Brown, T. S., Ismail, N., Omar, S. V., Brust, J. C. M., Nelson, K. N., Allana, S., Campbell, A., Mlisana, K., Moodley, P., & Gandhi, N. R. (2018). Extensively drug-resistant tuberculosis in South Africa: Genomic evidence supporting transmission in communities. European Respiratory Journal, 52(4), 1800246. https://doi.org/https://doi.org/10.1183/13993003.00246-2018
- Bitencourt-Ferreira, G., & de Azevedo, W. F. Jr.(2019). SAnDReS: A computational tool for docking. Methods in Molecular Biology (Clifton, N.J.), 2053, 51–65. https://doi.org/https://doi.org/10.1007/978-1-4939-9752-7_4
- Capriotti, E., Fariselli, P., & Casadio, R. (2005). I-Mutant2.0: Predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Research, 33(Web Server issue), W306–W310. doi: 33/suppl_2/W306 [pii]https://doi.org/https://doi.org/10.1093/nar/gki375.
- Choi, Y., & Chan, A. P. (2015). PROVEAN web server: A tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics (Oxford, England), 31 (16), 2745–2747. https://doi.org/https://doi.org/10.1093/bioinformatics/btv195
- Cohen, K. A., Manson, A. L., Desjardins, C. A., Abeel, T., & Earl, A. M. (2019). Deciphering drug resistance in Mycobacterium tuberculosis using whole-genome sequencing: Progress, promise, and challenges. Genome Medicine, 11 (1), 45. https://doi.org/https://doi.org/10.1186/s13073-019-0660-8
- Ferrer-Costa, C., Gelpi, J. L., Zamakola, L., Parraga, I., de la Cruz, X., & Orozco, M. (2005). PMUT: A web-based tool for the annotation of pathological mutations on proteins. Bioinformatics (Oxford, England), 21(14), 3176–3178.https://doi.org/https://doi.org/10.1093/bioinformatics/bti486.
- Folkman, L., Stantic, B., Sattar, A., & Zhou, Y. (2016). EASE-MM: Sequence-based prediction of mutation-induced stability changes with feature-based multiple models. Journal of Molecular Biology, 428(6), 1394–1405. https://doi.org/https://doi.org/10.1016/j.jmb.2016.01.012
- Frisch, M. J., Trucks, G. W., Schlegel, H. B., Scuseria, G. E., Robb, M. A., Cheeseman, J. R., & Scalmani, G. (2009). Gaussian 09. Gaussian, Inc.
- Jayaraman, M., & Ramadas, K. (2020). An integrated computational investigation to unveil the structural impacts of mutation on the InhA structural gene of Mycobacterium tuberculosis. Journal of Molecular Graphics & Modelling, 101, 107768. https://doi.org/https://doi.org/10.1016/j.jmgm.2020.107768
- Khan, F. I., Shahbaaz, M., Bisetty, K., Waheed, A., Sly, W. S., Ahmad, F., & Hassan, M. I. (2016). Large scale analysis of the mutational landscape in β-glucuronidase: A major player of mucopolysaccharidosis type VII. Gene, 576(1 Pt 1), 36–44. https://doi.org/https://doi.org/10.1016/j.gene.2015.09.062
- Khosravi, A. D., Goodarzi, H., & Alavi, S. M. (2012). Detection of genomic mutations in katG, inhA and rpoB genes of Mycobacterium tuberculosis isolates using polymerase chain reaction and multiplex allele-specific polymerase chain reaction. Brazilian Journal of Infectious Diseases, 16(1), 57–62. doi: S1413-86702012000100010 [pii]. https://doi.org/https://doi.org/10.1016/s1413-8670(12)70275-1
- Kumari, R., Kumar, R., & Lynn, A. (2014). g_mmpbsa-a GROMACS tool for high-throughput MM-PBSA calculations. Journal of Chemical Information and Modeling, 54(7), 1951–1962. https://doi.org/https://doi.org/10.1021/ci500020m
- Manca, C., Paul, S., Barry, C. E., 3rd, Freedman, V. H., & Kaplan, G. (1999). Mycobacterium tuberculosis catalase and peroxidase activities and resistance to oxidative killing in human monocytes in vitro. Infection and Immunity, 67(1), 74–79. https://doi.org/https://doi.org/10.1128/IAI.67.1.74-79.1999
- Marttila, H. J., Soini, H., Huovinen, P., & Viljanen, M. K. (1996). katG mutations in isoniazid-resistant Mycobacterium tuberculosis isolates recovered from Finnish patients. Antimicrobial Agents and Chemotherapy, 40(9), 2187–2189. https://doi.org/https://doi.org/10.1128/AAC.40.9.2187
- Mészáros, B., Erdos, G., & Dosztányi, Z. (2018). IUPred2A: Context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Research, 46(W1), W329–W337. https://doi.org/https://doi.org/10.1093/nar/gky384
- Morlock, G. P., Metchock, B., Sikes, D., Crawford, J. T., & Cooksey, R. C. (2003). ethA, inhA, and katG loci of ethionamide-resistant clinical Mycobacterium tuberculosis isolates. Antimicrobial Agents and Chemotherapy, 47(12), 3799–805. https://doi.org/https://doi.org/10.1128/aac.47.12.3799-3805.2003
- Munir, A., Kumar, N., Ramalingam, S. B., Tamilzhalagan, S., Shanmugam, S. K., Palaniappan, A. N., Nair, D., Priyadarshini, P., Natarajan, M., Tripathy, S., Ranganathan, U. D., Peacock, S. J., Parkhill, J., Blundell, T. L., & Malhotra, S. (2019). Identification and characterization of genetic determinants of isoniazid and rifampicin resistance in Mycobacterium tuberculosis in southern India. Scientific Reports, 9(1), 10283. https://doi.org/https://doi.org/10.1038/s41598-019-46756-x
- Naidoo, P., Theron, G., Rangaka, M. X., Chihota, V. N., Vaughan, L., Brey, Z. O., & Pillay, Y. (2017). The South African tuberculosis care cascade: Estimated losses and methodological challenges. Journal of the Infectious Diseases, 216(suppl_7), S702–S713. https://doi.org/https://doi.org/10.1093/infdis/jix335
- Neudert, G., & Klebe, G. (2011). DSX: A knowledge-based scoring function for the assessment of protein-ligand complexes. Journal of Chemical Information and Modeling, 51(10), 2731–2745. https://doi.org/https://doi.org/10.1021/ci200274q
- Ng, P. C., & Henikoff, S. (2003). SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Research, 31(13), 3812–3814. https://doi.org/https://doi.org/10.1093/nar/gkg509
- Niehaus, A. J., Mlisana, K., Gandhi, N. R., Mathema, B., & Brust, J. C. M. (2015). High prevalence of inhA promoter mutations among patients with drug-resistant tuberculosis in KwaZulu-Natal, South Africa. PLoS One, 10(9), e0135003. https://doi.org/https://doi.org/10.1371/journal.pone.0135003
- Oostenbrink, C., Villa, A., Mark, A. E., & van Gunsteren, W. F. (2004). A biomolecular force field based on the free enthalpy of hydration and solvation: The GROMOS force-field parameter sets 53A5 and 53A6. Journal of Computational Chemistry, 25(13), 1656–1676. https://doi.org/https://doi.org/10.1002/jcc.20090
- Phelan, J., Coll, F., McNerney, R., Ascher, D. B., Pires, D. E. V., Furnham, N., Coeck, N., Hill-Cawthorne, G. A., Nair, M. B., Mallard, K., Ramsay, A., Campino, S., Hibberd, M. L., Pain, A., Rigouts, L., & Clark, T. G. (2016). Mycobacterium tuberculosis whole genome sequencing and protein structure modelling provides insights into anti-tuberculosis drug resistance. BMC Medicine, 14(1), 31. https://doi.org/https://doi.org/10.1186/s12916-016-0575-9
- Pym, A. S., Saint-Joanis, B., & Cole, S. T. (2002). Effect of katG mutations on the virulence of Mycobacterium tuberculosis and the implication for transmission in humans. Infection and Immunity, 70(9), 4955–4960. https://doi.org/https://doi.org/10.1128/iai.70.9.4955-4960.2002
- Schuttelkopf, A. W., & van Aalten, D. M. (2004). PRODRG: A tool for high-throughput crystallography of protein-ligand complexes. Acta Crystallographica Section D Biological Crystallography, 60(Pt 8), 1355–1363. https://doi.org/https://doi.org/10.1107/S0907444904011679.
- Seifert, M., Catanzaro, D., Catanzaro, A., & Rodwell, T. C. (2015). Genetic mutations associated with isoniazid resistance in Mycobacterium tuberculosis: A systematic review. PLoS One, 10(3), e0119628. https://doi.org/https://doi.org/10.1371/journal.pone.0119628
- Shahbaaz, M., Rahman, S., Khan, P., Kim, J., & Hassan, M. I. (2017). Classification and structural analyses of mutational landscapes in hemochromatosis factor E protein: A protein defective in the hereditary hemochromatosis. Gene Reports, 6, 93–102. https://doi.org/https://doi.org/10.1016/j.genrep.2016.12.007
- Smith, I. (2003). Mycobacterium tuberculosis pathogenesis and molecular determinants of virulence. Clinical Microbiology Reviews, 16(3), 463–496. https://doi.org/https://doi.org/10.1128/cmr.16.3.463-496.2003
- Sneha, P., & George Priya Doss, C. (2016). Chapter seven - Molecular dynamics: New frontier in personalized medicine. In R. Donev (Ed.), Advances in protein chemistry and structural biology (pp. 181–224). Academic Press.
- Trott, O., & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, 31(2), 455–461. https://doi.org/https://doi.org/10.1002/jcc.21334
- Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/https://doi.org/10.1002/jcc.20291
- Vanommeslaeghe, K., Hatcher, E., Acharya, C., Kundu, S., Zhong, S., Shim, J., Darian, E., Guvench, O., Lopes, P., Vorobyov, I., & Mackerell, A. D. (2010). CHARMM general force field: A force field for drug-like molecules compatible with the CHARMM all-atom additive biological force fields. Journal of Computational Chemistry, 31(4), 671–690. https://doi.org/https://doi.org/10.1002/jcc.21367
- Worth, C. L., Preissner, R., & Blundell, T. L. (2011). SDM-a server for predicting effects of mutations on protein stability and malfunction. Nucleic Acids Research, 39(Web Server issue), W215–W222. https://doi.org/https://doi.org/10.1093/nar/gkr363gkr363 [pii].
- Yousuf, M., Khan, P., Shamsi, A., Shahbaaz, M., Mustafa Hasan, G., Mohd Rizwanul Haque, Q., Christoffels, A., Islam, A., & Hassan, M. I. (2020). Inhibiting CDK6 activity by quercetin is an attractive strategy for cancer therapy. ACS Omega, 5(42), 27480–27491. https://doi.org/https://doi.org/10.1021/acsomega.0c03975
- Zielkiewicz, J. (2005). Structural properties of water: Comparison of the SPC, SPCE, TIP4P, and TIP5P models of water. Journal of Chemical Physics, 123(10), 104501. https://doi.org/https://doi.org/10.1063/1.2018637