In silico drug repurposing by combining machine learning classification model and molecular dynamics to identify a potential OGT inhibitor

References

• Albuquerque, S. O., Barros, T. G., Dias, L. R. S., Lima, C. H. d S., Azevedo, P. H. R. d A., Flores-Junior, L. A. P., dos Santos, E. G., Loponte, H. F., Pinheiro, S., Dias, W. B., Muri, E. M. F., & Todeschini, A. R. (2020). Biological evaluation and molecular modeling of peptidomimetic compounds as inhibitors for O-GlcNAc transferase (OGT). European Journal of Pharmaceutical Sciences, 154, 105510. https://doi.org/10.1016/j.ejps.2020.105510
   PubMed Web of Science ®Google Scholar
• Balana, A. T., & Pratt, M. R. (2021). Mechanistic roles for altered O -GlcNAcylation in neurodegenerative disorders. The Biochemical Journal, 478(14), 2733–2758. https://doi.org/10.1042/BCJ20200609
   PubMed Web of Science ®Google Scholar
• Ballester, P. J., & Mitchell, J. B. O. (2010). A machine learning approach to predicting protein–ligand binding affinity with applications to molecular docking. Bioinformatics (Oxford, England), 26(9), 1169–1175. https://doi.org/10.1093/bioinformatics/btq112
   PubMed Web of Science ®Google Scholar
• Ballester, P. J., Schreyer, A., & Blundell, T. L. (2014). Does a more precise chemical description of protein–ligand complexes lead to more accurate prediction of binding affinity? Journal of Chemical Information and Modeling, 54(3), 944–955. https://doi.org/10.1021/ci500091r
   PubMed Web of Science ®Google Scholar
• Berendsen, H. J. C., van der Spoel, D., & van Drunen, R. (1995). GROMACS: A message-passing parallel molecular dynamics implementation. Computer Physics Communications, 91(1-3), 43–56. https://doi.org/10.1016/0010-4655(95)00042-E
   Web of Science ®Google Scholar
• Berishvili, V. P., Voronkov, A. E., Radchenko, E. V., & Palyulin, V. A. (2018). Machine learning classification models to improve the docking-based screening: A case of PI3K-tankyrase inhibitors. Molecular Informatics, 37(11), 1800030. https://doi.org/10.1002/minf.201800030
   Web of Science ®Google Scholar
• BIOVIA. (2020). BIOVIA Discovery Studio Visualizer (v. 19.1.0.18287).
   Google Scholar
• Borodkin, V. S., Schimpl, M., Gundogdu, M., Rafie, K., Dorfmueller, H. C., Robinson, D. A., & van Aalten, D. M. F. (2014). Bisubstrate UDP–peptide conjugates as human O-GlcNAc transferase inhibitors. The Biochemical Journal, 457(3), 497–502. https://doi.org/10.1042/BJ20131272
   PubMedGoogle Scholar
• Cereto-Massagué, A., Guasch, L., Valls, C., Mulero, M., Pujadas, G., & Garcia-Vallvé, S. (2012). DecoyFinder: An easy-to-use python GUI application for building target-specific decoy sets. Bioinformatics (Oxford, England), 28(12), 1661–1662. https://doi.org/10.1093/bioinformatics/bts249
   PubMed Web of Science ®Google Scholar
• Chicco, D., & Jurman, G. (2020). The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genomics, 21(1), 6. https://doi.org/10.1186/s12864-019-6413-7
   PubMed Web of Science ®Google Scholar
• Chicco, D., Warrens, M. J., & Jurman, G. (2021). The Matthews Correlation Coefficient (MCC) is more informative than Cohen’s Kappa and Brier Score in binary classification assessment. IEEE Access, 9, 78368–78381. https://doi.org/10.1109/ACCESS.2021.3084050
   Web of Science ®Google Scholar
• da Silva, T. U., Pougy, K. d C., Albuquerque, M. G., Lima, C. H. d S., & Machado, S. d P. (2023). Molecular dynamics simulations of aqueous systems of inhibitor candidates for adenosine-5’-phosphosufate reductase. Journal of Biomolecular Structure and Dynamics, 41(6), 2466–2477. https://doi.org/10.1080/07391102.2022.2033137
   PubMed Web of Science ®Google Scholar
• Delgado, R., & Tibau, X.-A. (2019). Why Cohen’s Kappa should be avoided as performance measure in classification. PloS One, 14(9), e0222916. https://doi.org/10.1371/journal.pone.0222916
   PubMed Web of Science ®Google Scholar
• Ericksen, S. S., Wu, H., Zhang, H., Michael, L. A., Newton, M. A., Hoffmann, F. M., & Wildman, S. A. (2017). Machine learning consensus scoring improves performance across targets in structure-based virtual screening. Journal of Chemical Information and Modeling, 57(7), 1579–1590. https://doi.org/10.1021/acs.jcim.7b00153
   PubMed Web of Science ®Google Scholar
• Gomes, D. E. B., Silva, A. W., Lins, R. D., Soares, T. A., & Pascutti, P. G. (2009). HbMap2Grace.
   Google Scholar
• Gurung, A. B., Ali, M. A., Lee, J., Farah, M. A., & Al-Anazi, K. M. (2021). An updated review of computer-aided drug design and its application to COVID-19. BioMed Research International, 2021, 8853056. https://doi.org/10.1155/2021/8853056
   PubMed Web of Science ®Google Scholar
• Hanser, T., Barber, C., Marchaland, J. F., & Werner, S. (2016). Applicability domain: Towards a more formal definition. SAR and QSAR in Environmental Research, 27(11), 865–881. https://doi.org/10.1080/1062936X.2016.1250229
   Web of Science ®Google Scholar
• Hanwell, M. D., Curtis, D. E., Lonie, D. C., Vandermeersch, T., Zurek, E., & Hutchison, G. R. (2012). Avogadro: An advanced semantic chemical editor, visualization, and analysis platform. Journal of Cheminformatics, 4(1), 17. https://doi.org/10.1186/1758-2946-4-17
   PubMed Web of Science ®Google Scholar
• Huang, J., Rauscher, S., Nawrocki, G., Ran, T., Feig, M., de Groot, B. L., Grubmüller, H., & MacKerell, A. D. (2017). CHARMM36m: An improved force field for folded and intrinsically disordered proteins. Nature Methods, 14(1), 71–73. https://doi.org/10.1038/nmeth.4067
   PubMed Web of Science ®Google Scholar
• Humphrey, W., Dalke, A., & Schulten, K. (1996). VMD: Visual molecular dynamics. Journal of Molecular Graphics, 14(1), 33–38. https://doi.org/10.1016/0263-7855(96)00018-5
   PubMedGoogle Scholar
• Hussin, S. K., Abdelmageid, S. M., Alkhalil, A., Omar, Y. M., Marie, M. I., & Ramadan, R. A. (2021). Handling imbalance classification virtual screening big data using machine learning algorithms. Complexity, 2021, 1–15. https://doi.org/10.1155/2021/6675279
   Web of Science ®Google Scholar
• Idakwo, G., Thangapandian, S., Luttrell, J., Li, Y., Wang, N., Zhou, Z., Hong, H., Yang, B., Zhang, C., & Gong, P. (2020). Structure–activity relationship-based chemical classification of highly imbalanced Tox21 datasets. Journal of Cheminformatics, 12(1), 66. https://doi.org/10.1186/s13321-020-00468-x
   PubMed Web of Science ®Google Scholar
• Itkonen, H. M., Gorad, S. S., Duveau, D. Y., Martin, S. E. S., Barkovskaya, A., Bathen, T. F., Moestue, S. A., & Mills, I. G. (2016). Inhibition of O-GlcNAc transferase activity reprograms prostate cancer cell metabolism. Oncotarget, 7(11), 12464–12476. https://doi.org/10.18632/oncotarget.7039
   PubMedGoogle Scholar
• Itkonen, H. M., Minner, S., Guldvik, I. J., Sandmann, M. J., Tsourlakis, M. C., Berge, V., Svindland, A., Schlomm, T., & Mills, I. G. (2013). O-GlcNAc transferase integrates metabolic pathways to regulate the stability of c-MYC in human prostate cancer cells. Cancer Research, 73(16), 5277–5287. https://doi.org/10.1158/0008-5472.CAN-13-0549
   PubMed Web of Science ®Google Scholar
• Jain, S., Grandits, M., Richter, L., & Ecker, G. F. (2017). Structure based classification for bile salt export pump (BSEP) inhibitors using comparative structural modeling of human BSEP. Journal of Computer-Aided Molecular Design, 31(6), 507–521. https://doi.org/10.1007/s10822-017-0021-x
   PubMed Web of Science ®Google Scholar
• Jamal, S., Grover, A., & Grover, S. (2019). Machine learning from molecular dynamics trajectories to predict caspase-8 inhibitors against Alzheimer’s disease. Frontiers in Pharmacology, 10, 780. https://doi.org/10.3389/fphar.2019.00780
   PubMed Web of Science ®Google Scholar
• Jiang, J., Lazarus, M. B., Pasquina, L., Sliz, P., & Walker, S. (2012). A neutral diphosphate mimic crosslinks the active site of human O-GlcNAc transferase. Nature Chemical Biology, 8(1), 72–77. https://doi.org/10.1038/nchembio.711
   Web of Science ®Google Scholar
• Jones, G., Willett, P., Glen, R. C., Leach, a R., & Taylor, R. (1997). Development and validation of a genetic algorithm for flexible docking. Journal of Molecular Biology, 267(3), 727–748. https://doi.org/10.1006/jmbi.1996.0897
   PubMed Web of Science ®Google Scholar
• Kaur, G., & Oberoi, A. (2020). Novel approach for brain tumor detection based on Naïve Bayes classification. In N. Sharma, A. Chakrabarti & V. E. Balas (Eds.),Data Management, Analytics and Innovation (pp. 451–462). Springer. https://doi.org/10.1007/978-981-32-9949-8_31
   Google Scholar
• Kaur, H., Pannu, H. S., & Malhi, A. K. (2020). A systematic review on imbalanced data challenges in machine learning. ACM Computing Surveys, 52(4), 1–36. https://doi.org/10.1145/3343440
   Web of Science ®Google Scholar
• Kokabi, M., Donnelly, M., & Xu, G. (2020). Benchmarking small-dataset structure-activity-relationship models for prediction of Wnt signaling inhibition. IEEE Access. 8, 228831–228840. https://doi.org/10.1109/ACCESS.2020.3046190
   Web of Science ®Google Scholar
• Krawczyk, B. (2016). Learning from imbalanced data: Open challenges and future directions. Progress in Artificial Intelligence, 5(4), 221–232. https://doi.org/10.1007/s13748-016-0094-0
   Web of Science ®Google Scholar
• Kumari, P., Nath, A., & Chaube, R. (2015). Identification of human drug targets using machine-learning algorithms. Computers in Biology and Medicine, 56, 175–181. https://doi.org/10.1016/j.compbiomed.2014.11.008
   PubMed Web of Science ®Google Scholar
• 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/10.1021/ci500020m
   PubMed Web of Science ®Google Scholar
• Li, L., Wang, B., & Meroueh, S. O. (2011). Support vector regression scoring of receptor–ligand complexes for rank-ordering and virtual screening of chemical libraries. Journal of Chemical Information and Modeling, 51(9), 2132–2138. https://doi.org/10.1021/ci200078f
   PubMed Web of Science ®Google Scholar
• Lima, C., de Alencastro, R., Kaiser, C., de Souza, M., Rodrigues, C., & Albuquerque, M. (2015). Aqueous molecular dynamics simulations of the M. tuberculosis Enoyl-ACP Reductase-NADH system and its complex with a substrate mimic or diphenyl ethers inhibitors. International Journal of Molecular Sciences, 16(10), 23695–23722. https://doi.org/10.3390/ijms161023695
   PubMed Web of Science ®Google Scholar
• Lin, X., Li, X., & Lin, X. (2020). A review on applications of computational methods in drug screening and design. Molecules, 25(6), 1375. https://doi.org/10.3390/molecules25061375
   PubMed Web of Science ®Google Scholar
• Ma, J., Wu, C., & Hart, G. W. (2021). Analytical and biochemical perspectives of protein O-GlcNAcylation. Chemical Reviews, 121(3), 1513–1581. https://doi.org/10.1021/acs.chemrev.0c00884
   PubMed Web of Science ®Google Scholar
• Maciejewski, T., & Stefanowski, J. (2011). Local neighbourhood extension of SMOTE for mining imbalanced data [Paper presentation]. 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 104–111. https://doi.org/10.1109/CIDM.2011.5949434
   Google Scholar
• Mark, P., & Nilsson, L. (2001). Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. The Journal of Physical Chemistry A, 105(43), 9954–9960. https://doi.org/10.1021/jp003020w
   Web of Science ®Google Scholar
• Marshall, S., Bacote, V., & Traxinger, R. R. (1991). Discovery of a metabolic pathway mediating glucose-induced desensitization of the glucose transport system. Role of hexosamine biosynthesis in the induction of insulin resistance. Journal of Biological Chemistry, 266(8), 4706–4712. https://doi.org/10.1016/S0021-9258(19)67706-9
   PubMed Web of Science ®Google Scholar
• Martin, S. E. S., Tan, Z. W., Itkonen, H. M., Duveau, D. Y., Paulo, J. A., Janetzko, J., Boutz, P. L., Törk, L., Moss, F. A., Thomas, C. J., Gygi, S. P., Lazarus, M. B., & Walker, S. (2018). Structure-based evolution of low nanomolar O-GlcNAc transferase inhibitors. Journal of the American Chemical Society, 140(42), 13542–13545. https://doi.org/10.1021/jacs.8b07328
   PubMed Web of Science ®Google Scholar
• McClain, D. A., Lubas, W. A., Cooksey, R. C., Hazel, M., Parker, G. J., Love, D. C., & Hanover, J. A. (2002). Altered glycan-dependent signaling induces insulin resistance and hyperleptinemia. Proceedings of the National Academy of Sciences of the United States of America, 99(16), 10695–10699. https://doi.org/10.1073/pnas.152346899
   PubMed Web of Science ®Google Scholar
• Morino, K., & Maegawa, H. (2021). Role of O‐linked N‐acetylglucosamine in the homeostasis of metabolic organs, and its potential links with diabetes and its complications. Journal of Diabetes Investigation, 12(2), 130–136. https://doi.org/10.1111/jdi.13359
   PubMed Web of Science ®Google Scholar
• Muegge, I., & Oloff, S. (2006). Advances in virtual screening. Drug Discovery Today: Technologies, 3(4), 405–411. https://doi.org/10.1016/j.ddtec.2006.12.002
   Google Scholar
• Nie, H., & Yi, W. (2019). O-GlcNAcylation, a sweet link to the pathology of diseases. Journal of Zhejiang University. Science. B, 20(5), 437–448. https://doi.org/10.1631/jzus.B1900150
   PubMed Web of Science ®Google Scholar
• Ortiz-Meoz, R. F., Jiang, J., Lazarus, M. B., Orman, M., Janetzko, J., Fan, C., Duveau, D. Y., Tan, Z. W., Thomas, C. J., & Walker, S. (2015). A small molecule that inhibits OGT activity in cells. ACS Chemical Biology, 10(6), 1392–1397. https://doi.org/10.1021/acschembio.5b00004
   PubMed Web of Science ®Google Scholar
• Parker, M. P., Peterson, K. R., & Slawson, C. (2021). O-GlcNAcylation and O-GlcNAc cycling regulate gene transcription: emerging roles in cancer. Cancers, 13(7), 1666. https://doi.org/10.3390/cancers13071666
   PubMed Web of Science ®Google Scholar
• Patel, M., Horgan, P. G., McMillan, D. C., & Edwards, J. (2018). NF-κB pathways in the development and progression of colorectal cancer. Translational Research : The Journal of Laboratory and Clinical Medicine, 197, 43–56. https://doi.org/10.1016/j.trsl.2018.02.002
   PubMed Web of Science ®Google Scholar
• Patro, S. G. K., & Sahu, K. K. (2015). Normalization: A preprocessing stage. IARJSET, 2(3), 20–22. https://doi.org/10.17148/IARJSET.2015.2305
   Google Scholar
• PerkinElmer. (2011). ChemDraw Ultra 12.0 (12.0.).
   Google Scholar
• Priya, S., Tripathi, G., Singh, D. B., Jain, P., & Kumar, A. (2022). Machine learning approaches and their applications in drug discovery and design. Chemical Biology & Drug Design, 100(1), 136–153. https://doi.org/10.1111/cbdd.14057
   PubMed Web of Science ®Google Scholar
• Rácz, A., & Keserű, G. M. (2020). Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling. Journal of Computer-Aided Molecular Design, 34(8), 831–839. https://doi.org/10.1007/s10822-020-00308-y
   PubMed Web of Science ®Google Scholar
• Rodriguez, S., Hug, C., Todorov, P., Moret, N., Boswell, S. A., Evans, K., Zhou, G., Johnson, N. T., Hyman, B. T., Sorger, P. K., Albers, M. W., & Sokolov, A. (2021). Machine learning identifies candidates for drug repurposing in Alzheimer’s disease. Nature Communications, 12(1), 1033. https://doi.org/10.1038/s41467-021-21330-0
   PubMed Web of Science ®Google Scholar
• Rücker, C., Rücker, G., & Meringer, M. (2007). y-Randomization and its variants in QSPR/QSAR. Journal of Chemical Information and Modeling, 47(6), 2345–2357. https://doi.org/10.1021/ci700157b
   PubMed Web of Science ®Google Scholar
• Sabe, V. T., Ntombela, T., Jhamba, L. A., Maguire, G. E. M., Govender, T., Naicker, T., & Kruger, H. G. (2021). Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review. European Journal of Medicinal Chemistry, 224, 113705. https://doi.org/10.1016/j.ejmech.2021.113705
   PubMed Web of Science ®Google Scholar
• Sadeghi, S. S., & Keyvanpour, M. R. (2020). Computational drug repurposing: Classification of the research opportunities and challenges. Current Computer-Aided Drug Design, 16(4), 354–364. https://doi.org/10.2174/1573409915666190613113822
   PubMed Web of Science ®Google Scholar
• Saini, R., & Agarwal, S. M. (2022). EGFRisopred: A machine learning-based classification model for identifying isoform-specific inhibitors against EGFR and HER2. Molecular Diversity, 26(3), 1531–1543. https://doi.org/10.1007/s11030-021-10284-6
   PubMed Web of Science ®Google Scholar
• Salmaso, V., & Moro, S. (2018). Bridging molecular docking to molecular dynamics in exploring ligand-protein recognition process: An overview. Frontiers in Pharmacology, 9, 923. https://doi.org/10.3389/fphar.2018.00923
   PubMed Web of Science ®Google Scholar
• Sander, T., Freyss, J., von Korff, M., & Rufener, C. (2015). DataWarrior: An open-source program for chemistry aware data visualization and analysis. Journal of Chemical Information and Modeling, 55(2), 460–473. https://doi.org/10.1021/ci500588j
   PubMed Web of Science ®Google Scholar
• Singh, J. P., Qian, K., Lee, J.-S., Zhou, J., Han, X., Zhang, B., Ong, Q., Ni, W., Jiang, M., Ruan, H.-B., Li, M.-D., Zhang, K., Ding, Z., Lee, P., Singh, K., Wu, J., Herzog, R. I., Kaech, S., Wendel, H.-G., … Yang, X. (2020). O-GlcNAcase targets pyruvate kinase M2 to regulate tumor growth. Oncogene, 39(3), 560–573. https://doi.org/10.1038/s41388-019-0975-3
   PubMed Web of Science ®Google Scholar
• Śledź, P., & Caflisch, A. (2018). Protein structure-based drug design: From docking to molecular dynamics. Current Opinion in Structural Biology, 48, 93–102. https://doi.org/10.1016/j.sbi.2017.10.010
   PubMed Web of Science ®Google Scholar
• Teo, C. F., Wollaston-Hayden, E. E., & Wells, L. (2010). Hexosamine flux, the O-GlcNAc modification, and the development of insulin resistance in adipocytes. Molecular and Cellular Endocrinology, 318(1-2), 44–53. https://doi.org/10.1016/j.mce.2009.09.022
   PubMed Web of Science ®Google Scholar
• Torres, P. H. M., Sodero, A. C. R., Jofily, P., & Silva, F. P. Jr, (2019). Key topics in molecular docking for drug design. International Journal of Molecular Sciences, 20(18), 4574. https://doi.org/10.3390/ijms20184574
   PubMed Web of Science ®Google Scholar
• Van Der Spoel, D., Lindahl, E., Hess, B., Groenhof, G., Mark, A. E., & Berendsen, H. J. C. (2005). GROMACS: Fast, flexible, and free. Journal of Computational Chemistry, 26(16), 1701–1718. https://doi.org/10.1002/jcc.20291
   PubMed Web of Science ®Google Scholar
• Verdonk, M. L., Berdini, V., Hartshorn, M. J., Mooij, W. T. M., Murray, C. W., Taylor, R. D., & Watson, P. (2004). Virtual screening using protein − ligand docking: Avoiding artificial enrichment. Journal of Chemical Information and Computer Sciences, 44(3), 793–806. https://doi.org/10.1021/ci034289q
   PubMedGoogle Scholar
• Vishwakarma, G., Sonpal, A., & Hachmann, J. (2021). Metrics for benchmarking and uncertainty quantification: Quality, applicability, and best practices for machine learning in chemistry. Trends in Chemistry, 3(2), 146–156. https://doi.org/10.1016/j.trechm.2020.12.004
   Google Scholar
• Walker, K., Lazarus, S., Gross, M. B., & J, B. (2015). O-GlcNAc transferase inhibitors and uses thereof (Patent No. US 8,957,075 B2). https://patents.google.com/patent/US8957075B2/en
   Google Scholar
• Yi, W., Clark, P. M., Mason, D. E., Keenan, M. C., Hill, C., Goddard, W. A., Peters, E. C., Driggers, E. M., & Hsieh-Wilson, L. C. (2012). Phosphofructokinase 1 glycosylation regulates cell growth and metabolism. Science (New York, N.Y.), 337(6097), 975–980. https://doi.org/10.1126/science.1222278
   PubMed Web of Science ®Google Scholar
• Zhang, N., Jiang, H., Zhang, K., Zhu, J., Wang, Z., Long, Y., He, Y., Feng, F., Liu, W., Ye, F., & Qu, W. (2022). OGT as potential novel target: Structure, function and inhibitors. Chemico-Biological Interactions, 357(March), 109886. https://doi.org/10.1016/j.cbi.2022.109886
   PubMedGoogle Scholar