Chemia Naissensis Volume 7, No.1 (2024) (стр. 40-62)
АУТОР(И) / AUTHOR(S): Budimir S. Ilić 
DOI: 10.46793/ChemN7.1.40I
САЖЕТАК / ABSTRACT:
Carbonic anhydrase (CA) enzymes are critical to numerous physiological processes, making them valuable therapeutic targets. Aromatic and heterocyclic sulfonamides have demonstrated exceptional inhibitory activity, with significant applications in managing glaucoma, a complex and progressive neurodegenerative condition. This study employs an integrative approach combining machine learning, specifically Multiple Linear Regression (MLR) modeling, with molecular dynamics simulations to investigate a series of γ-aminobutyric acid (GABA)-conjugated sulfonamides. The MLR model effectively identified key structural and physicochemical features governing inhibitory activity against carbonic anhydrase isoforms II and IV, enabling precise predictions of biological efficacy. Molecular dynamics simulations were conducted exclusively on the most active GABA conjugate identified, in complex with CA II and CA IV enzymes. These simulations revealed atomistic details of enzyme-ligand interactions, highlighting critical binding interactions, dynamic stability, and conformational behavior driving potent inhibitory effects. By integrating machine learning techniques and targeted molecular dynamics simulations, this study not only deepens our understanding of sulfonamide activity but also provides a robust foundation for the rational design of next-generation inhibitors with enhanced therapeutic potential against glaucoma.
КЉУЧНЕ РЕЧИ / KEYWORDS:
Machine learning, Molecular dynamics, GABA, Sulfonamides, Carbonic anhydrase
ПРОЈЕКАТ/ ACKNOWLEDGEMENT:
This work was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia (Contract No. 451-03-65/2024-03/200113). I am deeply grateful to D. E. Shaw Research for generously providing access to the Desmond software package, which was essential for conducting this research.
ЛИТЕРАТУРА / REFERENCES:
- Angeli, A., Chelli, I., Lucarini, L., Sgambellone, S., Marri, S., Villano, S., Ferraroni, M., De Luca, V., Capasso, C., Carta, F., & Supuran, C. T. (2024). Novel carbonic anhydrase inhibitors with dual-tail core sulfonamide show potent and lasting effects for glaucoma therapy. Journal of Medicinal Chemistry, 67, 3066–3089.
- Angeli, A., Paoletti, N., & Supuran, C. T. (2023). Five-membered heterocyclic sulfonamides as carbonic anhydrase inhibitors. Molecules, 28, 1–23.
- Banerjee, A., & Roy, K. (2023). Machine-learning-based similarity meets traditional QSAR: “qRASAR” for the enhancement of the external predictivity and detection of prediction confidence outliers in an hERG toxicity dataset. Chemometrics and Intelligent Laboratory Systems, 237, 104829.
- Cho, H., & Choi, I. S. (2019). Enhanced deep-learning prediction of molecular properties via augmentation of bond topology. ChemMedChem, 14, 1604–1609.
- De Vivo, M., Masetti, M., Bottegoni, G., & Cavalli, A. (2016). Role of molecular dynamics and related methods in drug discovery. Journal of Medicinal Chemistry, 59, 4035–4061.
- Di Fiore, A., Maresca, A., Supuran, C. T., & De Simone, G. (2012). Hydroxamate represents a versatile zinc binding group for the development of new carbonic anhydrase inhibitors. Chemical Communications, 48, 8838–8840.
- Do, P. C., Lee, E. H., & Le, L. (2018). Steered molecular dynamics simulation in rational drug design. Journal of Chemical Information and Modeling, 58, 1473–1482.
- Fischer, T., & Riedl, R. (2013). Strategic targeting of multiple water-mediated interactions: a concise and rational structure-based design approach to potent and selective MMP-13 inhibitors. ChemMedChem, 8, 1457–1461.
- Frasnetti, E., Cucchi, I., Pavoni, S., Frigerio, F., Cinquini, F., Serapian, S. A., Pavarino, L. F., & Colombo, G. (2024). Integrating molecular dynamics and machine learning algorithms to predict the functional profile of kinase ligands. Journal of Chemical Theory and Computation, 20, 9209–9229.
- Golbraikh, A., & Tropsha, A. (2002). Beware of q2! Journal of Molecular Graphics and Modelling, 20, 269–276.
- Hou, R., He, Y., Yan, G., Hou, S., Xie, Z., & Liao, C. (2021). Zinc enzymes in medicinal chemistry. European Journal of Medicinal Chemistry, 226, 113877.
- Ilić, B. S. (2020). Molecular dynamics simulations of ASC09, ritonavir, lopinavir and darunavir with the COVID-19 protease. Chemia Naissensis, 3, 155–166.
- Ilić, B. S., Gajić, M., Bondžić, B. P., Džambaski, Z., Kocić, G., & Šmelcerović, A. (2021). Deoxyribonuclease I inhibitory properties, molecular docking and molecular dynamics simulations of 1-(pyrrolidin-2-yl)propan-2-one derivatives. Chemistry & Biodiversity, 18, 1–8.
- Jaitak, A., Kumari, K., Kounder, S., & Monga, V. (2024). Carbonic anhydrases: Moiety appended derivatives, medicinal and pharmacological implications. Bioorganic & Medicinal Chemistry, 114, 117933.
- Liu, K., & Kokubo, H. (2017). Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations: A cross-docking study. Journal of Chemical Information and Modeling, 57, 2514–2522.
- Mincione, G., Menabuoni, L., Briganti, F., Mincione, F., Scozzafava, A., & Supuran, C. T. (1999). Carbonic anhydrase inhibitors. Part 79. Synthesis of topically acting sulfonamides incorporating GABA moieties in their molecule, with long-lasting intraocular pressure-lowering properties. European Journal of Pharmaceutical Sciences, 9, 185–199.
- Naeem, N., Sadiq, A., Othman, G. A., Yassin, H. M., & Mughal, E. U. (2024). Exploring heterocyclic scaffolds in carbonic anhydrase inhibition: a decade of structural and therapeutic insights. RSC Advances, 14, 35769–35970.
- Nocentini, A., & Supuran, C. T. (2019). Adrenergic agonists and antagonists as antiglaucoma agents: a literature and patent review (2013-2019). Expert Opinion on Therapeutic Patents, 29, 805–815.
- Pagnozzi, D., Pala, N., Biosa, G., Dallocchio, R., Dessì, A., Singh, P. K., Rogolino, D., Di Fiore, A., De Simone, G., Supuran, C. T., & Sechi, M. (2022). Interaction studies between carbonic anhydrase and a sulfonamide inhibitor by experimental and theoretical approaches. ACS Medicinal Chemistry Letters, 13, 271–277.
- Rodríguez-Pérez, R., & Bajorath, J. (2021). Explainable machine learning for property predictions in compound optimization. Journal of Medicinal Chemistry, 64, 17744–17752.
- Roy, K., Das, R. N., Ambure, P., & Aher, R. B. (2016). Be aware of error measures. Further studies on validation of predictive QSAR models. Chemometrics and Intelligent Laboratory Systems, 152, 18–33.
- Supuran, C. T. (2016a). How many carbonic anhydrase inhibition mechanisms exist? Journal of Enzyme Inhibition and Medicinal Chemistry, 31, 345–360.
- Supuran, C. T. (2016b). Structure and function of carbonic anhydrases. Biochemical Journal, 473, 2023–2032.
- Supuran, C. T. (2021). Emerging role of carbonic anhydrase inhibitors. Clinical Science, 135, 1233–1249.
- Supuran, C. T., & Scozzafava, A. (2007). Carbonic anhydrases as targets for medicinal chemistry. Bioorganic & Medicinal Chemistry, 15, 4336–4350.
- Tetko, I. V., Gasteiger, J., Todeschini, R., Mauri, A., Livingstone, D., Ertl, P., Palyulin, V. A., Radchenko, E. V., Zefirov, N. S., Makarenko, A. S., Tanchuk, V. Y., & Prokopenko, V. V. (2005). Virtual computational chemistry laboratory – design and description. Journal of Computer – Aided Molecular Design, 19, 453–463.
- Tinivella, A., Pinzi, L., & Rastelli, G. (2021). Prediction of activity and selectivity profiles of human carbonic anhydrase inhibitors using machine learning classification models. Journal of Cheminformatics, 13, 1–15.
- Todeschini, R., & Consonni, V. (2009). Molecular descriptors for chemoinformatics. Volumes I & II, (2nd ed.). Wiley-VCH Verlag GmbH & Co. KGaA.
- Wei, H., & McCammon, J. A. (2024). Structure and dynamics in drug discovery. npj Drug Discovery, 1, 1–8.
- Xu, W. (2024). Current status of computational approaches for small molecule drug discovery. Journal of Medicinal Chemistry, 67, 18633–18636.