80
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Pharmacoinformatics approach for type 2 diabetes mellitus therapeutics using phytocompounds from Costus genus: an in-silico investigation

, , , , &
Received 22 Sep 2023, Accepted 08 Mar 2024, Published online: 21 Mar 2024

References

  • Atlas, I. D. (2021). Diabetes around the world in 2021. https://diabetesatlas.org/
  • Azzam, S. K., Jang, H., Choi, M. C., Alsafar, H., Lukman, S., & Lee, S. (2018). Inhibition of human amylin aggregation and cellular toxicity by lipoic acid and ascorbic acid. Molecular Pharmaceutics, 15(6), 2098–2106. https://doi.org/10.1021/acs.molpharmaceut.7b01009
  • Bardy, G., Virsolvy, A., Quignard, J. F., Ravier, M. A., Bertrand, G., Dalle, S., Cros, G., Magous, R., Richard, S., & Oiry, C. (2013). Quercetin induces insulin secretion by direct activation of L-type calcium channels in pancreatic beta cells. British Journal of Pharmacology, 169(5), 1102–1113. https://doi.org/10.1111/bph.12194
  • Bego, T., Čaušević, A., Dujić, T., Malenica, M., Velija-Asimi, Z., Prnjavorac, B., Marc, J., Nekvindová, J., Palička, V., & Semiz, S. (2019). Association of FTO gene variant (rs8050136) with type 2 diabetes and markers of obesity, glycaemic control and inflammation. Journal of Medical Biochemistry, 38(2), 153–163. https://doi.org/10.2478/jomb-2018-0023
  • Behera, S. K., Mahapatra, N., Tripathy, C. S., & Pati, S. (2021). Drug repurposing for identification of potential inhibitors against SARS-CoV-2 spike receptor-binding domain: An in silico approach. The Indian Journal of Medical Research, 153(1 & 2), 132–143. https://doi.org/10.4103/ijmr.IJMR_1132_20
  • Behera, S. K., Vhora, N., Contractor, D., Shard, A., Kumar, D., Kalia, K., & Jain, A. (2021). Computational drug repurposing study elucidating simultaneous inhibition of entry and replication of novel corona virus by Grazoprevir. Scientific Reports, 11(1),7307. https://doi.org/10.1038/s41598-021-86712-2
  • Bharti, S. K., Krishnan, S., Kumar, A., & Kumar, A. (2018). Antidiabetic phytoconstituents and their mode of action on metabolic pathways. In Therapeutic Advances in Endocrinology and Metabolism, 9(3), 81–100. https://doi.org/10.1177/2042018818755019
  • Boesgaard, T. W., Pruhova, S., Andersson, E. A., Cinek, O., Obermannova, B., Lauenborg, J., Damm, P., Bergholdt, R., Pociot, F., Pisinger, C., Barbetti, F., Lebl, J., Pedersen, O., & Hansen, T. (2010). Further evidence that mutations in INS can be a rare cause of Maturity-Onset Diabetes of the Young (MODY). BMC Medical Genetics, 11(1), 42. http://www.biomedcentral.com/1471-2350/11/42 https://doi.org/10.1186/1471-2350-11-42
  • Chaari, A., Abdellatif, B., Nabi, F., & Khan, R. H. (2020). Date palm (Phoenix dactylifera L.) fruit’s polyphenols as potential inhibitors for human amylin fibril formation and toxicity in type 2 diabetes. International Journal of Biological Macromolecules, 164, 1794–1808. https://doi.org/10.1016/j.ijbiomac.2020.08.080
  • Chang, C. L. T., Lin, Y., Bartolome, A. P., Chen, Y. C., Chiu, S. C., & Yang, W. C. (2013). Herbal therapies for type 2 diabetes mellitus: Chemistry, biology, and potential application of selected plants and compounds. In Evidence-Based Complementary and Alternative Medicine: ECAM, 2013, 378633–378657. https://doi.org/10.1155/2013/378657
  • Chauhan, G., Spurgeon, C. J., Tabassum, R., Bhaskar, S., Kulkarni, S. R., Mahajan, A., Chavali, S., Kumar, M. V. K., Prakash, S., Dwivedi, O. P., Ghosh, S., Yajnik, C. S., Tandon, N., Bharadwaj, D., & Chandak, G. R. (2010). Impact of common variants of PPARG, KCNJ11, TCF7L2, SLC30A8, HHEX, CDKN2A, IGF2BP2, and CDKAL1 on the risk of type 2 diabetes in 5164 Indians. Diabetes, 59(8), 2068–2074. https://doi.org/10.2337/db09-1386
  • Chauhan, G., Tabassum, R., Mahajan, A., Dwivedi, O. P., Mahendran, Y., Kaur, I., Nigam, S., Dubey, H., Varma, B., Madhu, S. V., Mathur, S. K., Ghosh, S., Tandon, N., & Bharadwaj, D. (2011). Common variants of FTO and the risk of obesity and type 2 diabetes in Indians. Journal of Human Genetics, 56(10), 720–726. https://doi.org/10.1038/jhg.2011.87
  • Dey, J., Mahapatra, S. R., Raj, T. K., Misra, N., & Suar, M. (2023). Identification of potential flavonoid compounds as antibacterial therapeutics against Klebsiella pneumoniae infection using structure-based virtual screening and molecular dynamics simulation. Molecular Diversity. https://doi.org/10.1007/s11030-023-10738-z
  • Dixit, S., & Tiwari, S. (2020). Review on plants for management of diabetes in India: An ethno-botanical and pharmacological perspective. Pharmacognosy Journal, 12(6s), 1801–1810. https://doi.org/10.5530/pj.2020.12.243
  • Dong, M., Meng, Z., Kuerban, K., Qi, F., Liu, J., Wei, Y., Wang, Q., Jiang, S., Feng, M., & Ye, L. (2018). Diosgenin promotes antitumor immunity and PD-1 antibody efficacy against melanoma by regulating intestinal microbiota. Cell Death & Disease, 9, 1039. https://doi.org/10.1038/s41419-018-1099-3
  • Durrant, J. D., & McCammon, J. A. (2011). Molecular dynamics simulations and drug discovery. Biomed Central Biology, 9(1), 1–9. https://doi.org/10.1186/1741-7007-9-71
  • Ekins, S., Mestres, J., & Testa, B. (2007). In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. British Journal of Pharmacology, 152(1), 9–20. https://doi.org/10.1038/sj.bjp.0707305
  • Franzago, M., Fraticelli, F., Marchioni, M., Di Nicola, M., Di Sebastiano, F., Liberati, M., Stuppia, L., & Vitacolonna, E. (2021). Fat mass and obesity-associated (FTO) gene epigenetic modifications in gestational diabetes: New insights and possible pathophysiological connections. Acta Diabetologica, 58(8), 997–1007. https://doi.org/10.1007/s00592-020-01668-5
  • Genheden, S., & Ryde, U. (2015). The MM/PBSA and MM/GBSA methods to estimate ligand-binding affinities. Expert Opinion on Drug Discovery, 10(5), 449–461. https://doi.org/10.1517/17460441.2015.1032936
  • Gothai, S., Ganesan, P., Park, S. Y., Fakurazi, S., Choi, D. K., & Arulselvan, P. (2016). Natural phyto-bioactive compounds for the treatment of type 2 diabetes: Inflammation as a target. Nutrients, 8(8), 461. https://doi.org/10.3390/nu8080461
  • Grant, S. F. (2019). The TCF7L2 locus: A genetic window into the pathogenesis of type 1 and type 2 diabetes. Diabetes Care, 42(9), 1624–1629. https://doi.org/10.2337/dci19-0001
  • Hegde, P. L., Rao, H. A., & Rao, P. N. (2014). A review on insulin plant (Costus igneus Nak). Pharmacognosy Reviews, 8(15), 67–72. https://doi.org/10.4103/0973-7847.125536
  • Herzberg, O., & Moult, J. (1991). Analysis of the steric strain in the polypeptide backbone of protein molecules. Proteins, 11(3), 223–229. https://doi.org/10.1002/prot.340110307
  • Jomy, J. A., & Sethumadhavan, R. (2014). Computational identification and structural analysis of deleterious functional SNPs in ARL6 gene causing bardet-biedl syndrome. Research Journal of Pharmaceutical, Biological and Chemical Sciences, 5(5), 1359–1368.
  • Kindt, R. (2020). WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. Application in Plant Science, 8(9), e11388. https://doi.org/10.1002/aps3.11388
  • Kumari, R., Kumar, R., & Lynn, A, Open Source Drug Discovery Consortium. (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/10.1021/ci500020m
  • Laha, S., & Paul, S. (2019). Costus Igneus-a therapeutic anti-diabetic herb with active phytoconstitutents. International Journal of Pharmaceutical Sciences and Research, 10(8), 3583. https://doi.org/10.13040/IJPSR.0975-8232.10(8).3583-91
  • Liu, M., Hodish, I., Haataja, L., Lara-Lemus, R., Rajpal, G., Wright, J., & Arvan, P. (2010). Proinsulin misfolding and diabetes: Mutant INS gene-induced diabetes. Trends in Endocrinology and Metabolism: TEM, 21(11), 652–659. https://doi.org/10.1016/j.tem.2010.07.001
  • López, L. C., Varea, O., Navarro, S., Carrodeguas, J. A., De Groot, N. S., Ventura, S., & Sancho, J. (2016). Benzbromarone, quercetin, and folic acid inhibit amylin aggregation. International Journal of Molecular Sciences, 17(6), 964. https://doi.org/10.3390/ijms17060964
  • Madhu, S. V., Mishra, B. K., Mannar, V., Aslam, M., Banerjee, B., & Agrawal, V. (2022). TCF7L2 gene associated postprandial triglyceride dysmetabolism- a novel mechanism for diabetes risk among Asian Indians. Frontiers in Endocrinology, 13, 973718. https://doi.org/10.3389/fendo.2022.973718
  • Mahapatra, S. R., Dey, J., Raj, T. K., Kumar, V., Ghosh, M., Kumar Verma, K., Kaur, T., Kesawat, M. S., Misra, N., & Suar, M. (2022). The potential of plant-derived secondary metabolites as novel drug candidates against Klebsiella pneumoniae: Molecular docking and simulation investigation. South African Journal of Botany, 149, 789–797. https://doi.org/10.1016/j.sajb.2022.04.043
  • Morris, G. M., Ruth, H., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). Software news and updates AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30(16), 2785–2791. https://doi.org/10.1002/jcc.21256
  • Mulder, N., & Apweiler, R. (2007). InterPro and InterProScan: Tools for protein sequence classification and comparison. Methods in Molecular Biology (Clifton, N.J.), 396, 59–70. https://doi.org/10.1007/978-1-59745-515-2_5
  • Munhoz, A. C. M., & Frode, T. S. (2018). Isolated compounds from natural products with potential antidiabetic activity - a systematic review. Current Diabetes Reviews, 14(1), 36–106. https://doi.org/10.2174/1573399813666170505120621
  • Nagamani, S., Muthusamy, K., & Marshal, J. J. (2016). E-pharmacophore filtering and molecular dynamics simulation studies in the discovery of potent drug-like molecules for chronic kidney disease. Journal of Biomolecular Structure & Dynamics, 34(10), 2233–2250. https://doi.org/10.1080/07391102.2015.1111168
  • Pontius, J., Richelle, J., & Wodak, S. J. (1996). Deviations from standard atomic volumes as a quality measure for protein crystal structures. Journal of Molecular Biology, 264(1), 121–136. https://doi.org/10.1006/jmbi.1996.0628
  • Radha Devi, G. M. (2019). A Comprehensive review on costus pictus D. Don. International Journal of Pharmaceutical Sciences and Research, 10(7), 3187–3195. https://doi.org/10.13040/IJPSR.0975-8232.10(7).3187-95
  • Rollinger, J. M., Stuppner, H., & Langer, T. (2008). Virtual screening for the discovery of bioactive natural products. Progress in Drug Research. Fortschritte Der Arzneimittelforschung. Progres Des Recherches Pharmaceutiques, 65, 211, 213–249. https://doi.org/10.1007/978-3-7643-8117-2_6
  • Saraswathi, R., Upadhyay, L., Venkatakrishnan, R., Meera, R., & Devi, P. (2010). Isolation and biological evaluation of steroid from stem of Costus igneus. Journal of Chemical and Pharmaceutical Research, 2(5), 444–448.
  • Schmid, N., Eichenberger, A. P., Choutko, A., Riniker, S., Winger, M., Mark, A. E., & Van Gunsteren, W. F. (2011). Definition and testing of the GROMOS force-field versions 54A7 and 54B7. European Biophysics Journal: EBJ, 40(7), 843–856. https://doi.org/10.1007/s00249-011-0700-9
  • Sharma, A. K., Srivastava, G. N., Roy, A., & Sharma, V. K. (2017). ToxiM: A toxicity prediction tool for small molecules developed using machine learning and chemoinformatics approaches. Frontiers in Pharmacology, 8, 880. https://doi.org/10.3389/fphar.2017.00880
  • Shelake, G., Baviskar, S., Panda, A. K., Solankure, S., Pandey, K., Chauthe, S., & Behera, S. K. (2023). Exploring the rare variants associated with Type 2 Diabetes Mellitus in Indian population and its disease-drug association studies: An in-silico approach. Journal of Biomolecular Structure & Dynamics, 1–16. https://doi.org/10.1080/07391102.2023.2233634
  • Shiny, C. T., & Saxena, A. (2013). Sharad Prakash Gupta. (2013). Phytochemical investigation of the insulin plant “Costus pictus” D. Don. International Journal of Pharmaceutical and Biomedical Research, 4(2), 97–104.
  • Sohrab, S., Mishra, P., & Kumar Mishra, S. (2021). Phytochemical competence and pharmacological perspectives of an endangered boon—Costus speciosus (Koen.) Sm.: A comprehensive review. Bulletin of the National Research Centre, 45(1), 209. https://doi.org/10.1186/s42269-021-00663-2
  • Swaminathan, S., Harte, W. E., & Beveridge, D. L. (1991). Investigation of domain structure in proteins via molecular dynamics simulation: Application to HIV- 1 protease dimer. Journal of the American Chemical Society, 113(7), 2717–2721. https://doi.org/10.1021/ja00007a054
  • Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., Jensen, L. J., & Mering, C. V (2019). STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Research, 47(D1), D607–D613. https://doi.org/10.1093/nar/gky1131
  • Vasan, S. K., Karpe, F., Gu, H. F., Brismar, K., Fall, C. H., Ingelsson, E., & Fall, T. (2014). FTO genetic variants and risk of obesity and type 2 diabetes: A meta-analysis of 28,394 Indians. Obesity (Silver Spring, Md.), 22(3), 964–970. https://doi.org/10.1002/oby.20606
  • Wang, J., Chen, L., & Qiang, P. (2021). The role of IGF2BP2, an m6A reader gene, in human metabolic diseases and cancers. Cancer Cell International, 21(1), 99. https://doi.org/10.1186/s12935-021-01799-x
  • Webb, B., & Sali, A. (2014). Comparative protein structure modeling using MODELLER. Current Protocols in Bioinformatics, 54, 5.6.1–5.6.37. https://doi.org/10.1002/cpbi.3
  • Wiederstein, M., & Sippl, M. J. (2007). ProSA-web: Interactive web service for the recognition of errors in three-dimensional structures of proteins. Nucleic Acids Research, 35(2), W407–W410. https://doi.org/10.1093/nar/gkm290
  • Zeng, W., Jin, L., Zhang, F., Zhang, C., & Liang, W. (2018). Naringenin as a potential immunomodulator in therapeutics. Pharmacological Research, 135, 122–126. https://doi.org/10.1016/j.phrs.2018.08.002
  • Zhou, X., Zheng, W., Li, Y., Pearce, R., Zhang, C., Bell, E. W., Zhang, G., & Zhang, Y. (2022). I-TASSER-MTD: A deep-learning-based platform for multi-domain protein structure and function prediction. Nature Protocols, 17(10), 2326–2353. https://doi.org/10.1038/s41596-022-00728-0

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.