High Throughput in Silico Identification of α-Syn Aggregation Inhibitors for Parkinson's Disease

Authors

  • Amrutha Srivatsav High Schooler
  • Palos Verdes Peninsula High School Faculty Palos Verdes Peninsula High School

DOI:

https://doi.org/10.47611/jsrhs.v10i4.2096

Keywords:

Alpha Synuclein, Parkinson's Disease, Molecular Docking, Treatment Identification, High Throughput Screening

Abstract

Parkinson’s Disease (PD) is caused by the depletion of dopamine as a result of aggregates formed by a protein called alpha-synuclein (a-syn), which are toxic and kill dopamine neurons in the substantia nigra pars compacta brain region. PD currently only has some symptomatic treatments that lose effectiveness over time. The purpose of this study was to identify drugs that bind to the fibril forming segment of the non-amyloid beta component (NAC) domain of neuronal membrane bound a-syn. These drugs will inhibit other a-syn monomers from binding and prevent further a-syn aggregation, slowing the progression of PD.


Molecular docking was used to run a high throughput screening of 646 FDA approved drugs. The interactions between each drug and a-syn were analyzed and compared to those of Baicalein and SynuClean-D, two drugs that can inhibit a-syn aggregation by binding with the NAC domain but are not FDA approved.

13 drugs were identified that can potentially be used to inhibit membrane bound a-syn aggregation and were almost 6 times more potent than Baicalein and 10 times more potent than SynuClean-D at binding with a-syn’s NAC domain. The Blood Brain Barrier (BBB) penetrability of these top 13 drugs was assessed, indicating the drugs with a higher likelihood of penetrating the BBB. The results of this study appoint Telmisartan and Ibrutinib as FDA approved, BBB penetrable drugs that could inhibit membrane bound a-syn aggregation, slowing the progression of PD. Verifying these results through in vitro experiments can result in identifying a promising treatment for PD.

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Author Biography

Palos Verdes Peninsula High School Faculty , Palos Verdes Peninsula High School

Mentor

References or Bibliography

Banks, W. A. (2009). Characteristics of compounds that cross the blood-brain barrier. BMC Neurology, 9(Suppl 1), S3. https://doi.org/10.1186/1471-2377-9-s1-s3

Bisaglia, M., Trolio, A., Bellanda, M., Bergantino, E., Bubacco, L., & Mammi, S. (2006). Structure and topology of the non-amyloid-β component fragment of human α-synuclein bound to micelles: Implications for the aggregation process. Protein Science, 15(6), 1408–1416. https://doi.org/10.1110/ps.052048706

Cumming, H., & Rücker, C. (2017). Octanol–Water Partition Coefficient Measurement by a Simple1H NMR Method. ACS Omega, 2(9), 6244–6249. https://doi.org/10.1021/acsomega.7b01102

Daneman, R., & Prat, A. (2015). The Blood–Brain Barrier. Cold Spring Harbor Perspectives in Biology, 7(1), a020412. https://doi.org/10.1101/cshperspect.a020412

DeMaagd, G., & Philip, A. (2015). Parkinson’s Disease and Its Management: Part 1: Disease Entity, Risk Factors, Pathophysiology, Clinical Presentation, and Diagnosis. P & T : A Peer-Reviewed Journal for Formulary Management, 40(8), 504–532. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4517533/

Dotiwala, A. K., McCausland, C., & Samra, N. S. (2020, September 17). Anatomy, Head and Neck, Blood Brain Barrier. Nih.gov; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK519556/#:~:text=The %20blood%2Dbrain %20barrier%20is,%2C%20i.e.%2C%20projections%20of%20astrocytes.

Du, X., Li, Y., Xia, Y.-L., Ai, S.-M., Liang, J., Sang, P., … Liu, S.-Q. (2016). Insights into Protein–Ligand Interactions: Mechanisms, Models, and Methods. International Journal of Molecular Sciences, 17(2), 144. https://doi.org/10.3390/ijms17020144

Discovery Studio Modeling Environment, Release 4.5. 2015. BIOVIA, Dassault Systèmes, San Diego.

Emamzadeh, F. (2016). Alpha-synuclein structure, functions, and interactions. Journal of Research in Medical Sciences, 21(1), 29. https://doi.org/10.4103/1735-1995.181989

Guex, N. and Peitsch, M.C. (1997) SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modeling. Electrophoresis 18, 2714-2723.

Han, D., Zheng, W., Wang, X., & Chen, Z. (2020). Proteostasis of α-Synuclein and Its Role in the Pathogenesis of Parkinson’s Disease. Frontiers in Cellular Neuroscience, 14. https://doi.org/10.3389/fncel.2020.00045

Hijaz, B. A., & Volpicelli-Daley, L. A. (2020). Initiation and propagation of α-synuclein aggregation in the nervous system. Molecular Neurodegeneration, 15(1). https://doi.org/10.1186/s13024-020-00368-6

Hu, Q., Uversky, V. N., Huang, M., Kang, H., Xu, F., Liu, X., … Zhu, S. (2016). Baicalein inhibits α-synuclein oligomer formation and prevents progression of α-synuclein -accumulation in a rotenone mouse model of Parkinson’s disease. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease, 1862(10), 1883– 1890. https://doi.org/10.1016/j.bbadis.2016.07.008

Jao, C. C., Hegde, B. G., Chen, J., Haworth, I. S., & Langen, R. (2008). Structure of membrane- bound -synuclein from site-directed spin labeling and computational refinement. Proceedings of the National Academy of Sciences, 105(50), 19666–19671. https://doi.org/10.1073/pnas.0807826105

K., A. (2011). Thermodynamics of Ligand-Protein Interactions: Implications for Molecular Design. Thermodynamics - Interaction Studies - Solids, Liquids and Gases. https://doi.org/10.5772/19447

Lashuel, H. A., Overk, C. R., Oueslati, A., & Masliah, E. (2012). The many faces of α-synuclein: from structure and toxicity to therapeutic target. Nature Reviews Neuroscience, 14(1), 38–48. https://doi.org/10.1038/nrn3406

Ligand Binding Efficiency: Trends, Physical Basis, and Implications. (2020). Acs.org. https://pubs.acs.org/doi/10.1021/jm701255b

Meade, R. M., Fairlie, D. P., & Mason, J. M. (2019). Alpha-synuclein structure and Parkinson’s disease – lessons and emerging principles. Molecular Neurodegeneration, 14(1). https://doi.org/10.1186/s13024-019-0329-1

‌Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: a powerful approach for structure- based drug discovery. Current computer-aided drug design, 7(2), 146–157. https://doi.org/10.2174/157340911795677602

‌Mikitsh, J. L., & Chacko, A.-M. (2014). Pathways for Small Molecule Delivery to the Central Nervous System across the Blood-Brain Barrier. Perspectives in Medicinal Chemistry, 6, PMC.S13384. https://doi.org/10.4137/pmc.s13384

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 flexiblity. J. Computational Chemistry 2009, 16: 2785-91

N. M. O'Boyle, M. Banck, C. A. James, C. Morley, T. Vandermeersch, and G. R. Hutchison. 2011. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 3(33).

Obeso, J. A., Stamelou, M., Goetz, C. G., Poewe, W., Lang, A. E., Weintraub, D., Burn, D., Halliday, G. M., Bezard, E., Przedborski, S., Lehericy, S., Brooks, D. J., Rothwell, J.

C., Hallett, M., DeLong, M. R., Marras, C., Tanner, C. M., Ross, G. W., Langston, J. W., & Klein, C. (2017). Past, present, and future of Parkinson’s disease: A special essay on the 200th Anniversary of the Shaking Palsy. Movement Disorders, 32(9), 1264–1310. https://doi.org/10.1002/mds.27115

O. Trott, A. J. Olson, AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization and multithreading, Journal of Computational Chemistry 31 (2010) 455-461

Outeiro, T. F. (2017). Alpha-Synuclein. Reference Module in Neuroscience and Biobehavioral Psychology. https://doi.org/10.1016/b978-0-12-809324-5.00471-5

Pajouhesh, H., & Lenz, G. R. (2005). Medicinal chemical properties of successful central nervous system drugs. NeuroRX, 2(4), 541–553. https://doi.org/10.1602/neurorx.2.4.541

Pandit, R., Chen, L., & Götz, J. (2020). The blood-brain barrier: Physiology and strategies for drug delivery. Advanced Drug Delivery Reviews, 165-166, 1–14. https://doi.org/10.1016/j.addr.2019.11.009

Patil, R., Das, S., Stanley, A., Yadav, L., Sudhakar, A., & Varma, A. K. (2010). Optimized Hydrophobic Interactions and Hydrogen Bonding at the Target-Ligand Interface Leads the Pathways of Drug-Designing. PLoS ONE, 5(8), e12029. https://doi.org/10.1371/journal.pone.0012029

Powers, R., Lei, S., Anandhan, A., Marshall, D., Worley, B., Cerny, R., … Franco, R. (2017). Metabolic Investigations of the Molecular Mechanisms Associated with Parkinson’s Disease. Metabolites, 7(2), 22. https://doi.org/10.3390/metabo7020022

Pujols, J., Peña-Díaz, S., Lázaro, D. F., Peccati, F., Pinheiro, F., González, D., … Ventura, S. (2018). Small molecule inhibits α-synuclein aggregation, disrupts amyloid fibrils, and prevents degeneration of dopaminergic neurons. Proceedings of the National Academy of Sciences, 115(41), 10481–10486. https://doi.org/10.1073/pnas.1804198115

Stefanis, L., Emmanouilidou, E., Pantazopoulou, M., Kirik, D., Vekrellis, K., & Tofaris, G. K. (2019). How is alpha‐synuclein cleared from the cell? Journal of Neurochemistry, 150(5), 577–590. https://doi.org/10.1111/jnc.14704

Thanvi, B. R. (2004). Long term motor complications of levodopa: clinical features, mechanisms, and management strategies. Postgraduate Medical Journal, 80(946), 452– 458. https://doi.org/10.1136/pgmj.2003.013912

Trott, O., & Olson, A. J. (2009). AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. Journal of Computational Chemistry, NA-NA. https://doi.org/10.1002/jcc.21334

Tremblay, M.-E., Cookson, M. R., & Civiero, L. (2019). Glial phagocytic clearance in Parkinson’s disease. Molecular Neurodegeneration, 14(1). https://doi.org/10.1186/s13024- 019-0314-8

Wong, Y. C., & Krainc, D. (2017). α-synuclein toxicity in neurodegeneration: mechanism and therapeutic strategies. Nature Medicine, 23(2), 1–13. https://doi.org/10.1038/nm.4269

9: How Delocalized Electrons Affect pKa Values. (2014, August 30). Chemistry LibreTexts. https://chem.libretexts.org/Bookshelves/Organic_Chemistry/Map %3A_Essential_Organic_Chemistry_(Bruice)/07%3A_Delocalized_Electrons_and_Their_Effect_on_Stability_Reactivity_and_pKa_(Ultraviolet_and_Visible_Spectroscopy)/7.09%3A_How_Delocalized_Electrons_Affect_pKa_Values

Published

11-30-2021

How to Cite

Srivatsav, A., & Palos Verdes Peninsula High School. (2021). High Throughput in Silico Identification of α-Syn Aggregation Inhibitors for Parkinson’s Disease . Journal of Student Research, 10(4). https://doi.org/10.47611/jsrhs.v10i4.2096

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Section

HS Research Articles