Ashraf M. V., Pant S., Khan M. H., Shah A. A., Siddiqui S., Jeridi M., and Ahmad S. (2023). Phytochemicals as antimicrobials: prospecting Himalayan medicinal plants as source of alternate medicine to combat antimicrobial resistance. Pharmaceuticals, 16(6): 881.
Barh D., Tiwari S., Jain N., Ali A., Santos A. R., Misra A. N., and Kumar A. (2011). In silico subtractive genomics for target identification in human bacterial pathogens. Drug Development Research, 72(2): 162-177.
Blundell T. L., Sibanda B. L., Montalvão R. W., Brewerton S., Chelliah V., Worth C. L., and Burke, D. (2006). Structural biology and bioinformatics in drug design: opportunities and challenges for target identification and lead discovery. Philosophical Transactions of the Royal Society B: Biological Sciences, 361(1467): 413-423.
Borkotoky S., and Banerjee M. (2020). A computational prediction of SARS-CoV-2 structural protein inhibitors from Azadirachta indica (Neem). Journal of Biomolecular Structure and Dynamics, 39(11): 4111-4121.
Carugo O., and Pongor S. (2001). A normalized root‐mean‐spuare distance for comparing protein three‐dimensional structures. Protein Science, 10(7): 1470-1473.
Groenhof G. (2013). Introduction to QM/MM simulations. In: Monticelli, L., Salonen, E. (Eds.), Biomolecular simulations (pp. 43-66), Methods in Molecular Biology, Vol. 924, Humana Press, Totowa, NJ. DOI: https://doi.org/10.1007/978-1-62703-017-5_3.
Guilherme L., Kalil J., and Cunningham M. (2006). Molecular mimicry in the autoimmune pathogenesis of rheumatic heart disease. Autoimmunity, 39(1): 31-39.
Hanwell M. D., Curtis, D. E., Lonie D. C., Vandermeersch T., Zurek, E., and Hutchison G. R. (2012). Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1): 1-17.
Hess B., Bekker H., Berendsen H. J., and Fraaije J. G. (1997). LINCS: a linear constraint solver for molecular simulations. Journal of Computational Chemistry, 18(12): 1463-1472.
Hospital A., Goñi J. R., Orozco M., and Gelpí J. L. (2015). Molecular dynamics simulations: advances and applications. Advances and Applications in Bioinformatics and Chemistry, 8: 37-47. DOI: https://doi.org/10.2147/aabc.s70333.
Islam M. T., Aktaruzzaman M., Saif A., Sourov M. M. H., Sikdar B., Rehman S., and Muhib M. M. A. (2024). Identification of acetylcholinesterase inhibitors from traditional medicinal plants for Alzheimer’s disease using in silico and machine learning approaches. RSC Advances, 14(47): 34620-34636.
Kadi R. H., Altammar K. A., Hassan M. M., Shater A. F., Saleh F. M., Gattan H., and Mohammedsaleh Z. M. (2022). Potential therapeutic candidates against Chlamydia pneumonia discovered and developed in silico using core proteomics and molecular docking and simulation-based approaches. International Journal of Environmental Research and Public Health, 19(12): 7306.
Kang H. J., Coulibaly F., Proft T., and Baker E. N. (2011). Crystal structure of Spy0129, a Streptococcus pyogenes class B sortase involved in pilus assembly. PloS One, 6(1): e15969.
Maheswari K. U., and Sankar S. (2024). In silico molecular docking of phytochemicals of Murraya koenigii against Streptococcus mutans. Cureus, 16(2): e53679. DOI: https://doi.org/10.7759/cureus.53679.
Mangal P., Jha R. K., Jain M., Singh A. K., and Muthukumaran J. (2023). Identification and prioritization of promising lead molecules from Syzygium aromaticum against Sortase C from Streptococcus pyogenes: an in silico investigation. Journal of Biomolecular Structure and Dynamics, 41(12): 5418-5435.
Melk M. M., and El-Sayed A. F. (2024). Phytochemical profiling, antiviral activities, molecular docking, and dynamic simulations of selected Ruellia species extracts. Scientific Reports, 14(1): 15381.
Morris G. M., Huey R., Lindstrom W., Sanner M. F., Belew R. K., Goodsell D. S., and Olson A. J. (2009). AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16): 2785-2791.
Nair P. C., and Miners J. O. (2014). Molecular dynamics simulations: from structure function relationships to drug discovery. In Silico Pharmacology, 2: 1-4.
Pettersen E. F., Goddard T. D., Huang C. C., Couch G. S., Greenblatt D. M., Meng E. C., and Ferrin T. E. (2004). UCSF Chimera—a visualization system for exploratory research and analysis. Journal of Computational Chemistry, 25(13): 1605-1612.
Piard J., Hautefort I., Fischetti V., Ehrlich S., Fons M., and Gruss A. (1997). Cell wall anchoring of the Streptococcus pyogenes M6 protein in various lactic acid bacteria. Journal of Bacteriology, 179(9): 3068-3072.
Rehman A., Wang X., Ahmad S., Shahid F., et al. (2021). In silico core proteomics and molecular docking approaches for the identification of novel inhibitors against Streptococcus pyogenes. International Journal of Environmental Research and Public Health, 18(21): 11355.
Shakeran Z., and Nosrati M. (2019). Bioinformatics study of the effects of some phytocompounds found in Ferulago angulat, Scrophularia striata and Laurus nobilis medicinal plants on inhibition of the proteins causing antibiotic resistance in staphylococcus aureus: a descriptive study. Journal of Rafsanjan University of Medical Sciences, 18(2): 177-192.
Thomford N. E., Senthebane D. A., Rowe A., Munro D., Seele P., Maroyi A., and Dzobo K. (2018). Natural products for drug discovery in the 21st century: innovations for novel drug discovery. International Journal of Molecular Sciences, 19(6): 1578.
Trott O., and 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.
Yırtıcı Ü., Ergene A., Atalar M. N., and Adem Ş. (2022). Phytochemical composition, antioxidant, enzyme inhibition, antimicrobial effects, and molecular docking studies of Centaurea sivasica. South African Journal of Botany, 144: 58-71.
)