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ISSN (Print): 2211-5501
ISSN (Online): 2211-551X

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Research Article

Investigation into the Interaction Sites of the K84s and K102s Peptides with α-Synuclein for Understanding the Anti-Aggregation Mechanism: An In silico Study

Author(s): Priyanka Borah and Venkata Satish Kumar Mattaparthi*

Volume 12, Issue 2, 2023

Published on: 05 May, 2023

Page: [103 - 117] Pages: 15

DOI: 10.2174/2211550112666230331104839

Price: $65

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Abstract

Background: α-Synuclein has become the main therapeutic target in Parkinson's disease and related Synucleinopathies since the discovery of genetic associations between α-Synuclein and Parkinson's disease risk and the identification of aggregated α-Synuclein as the primary protein constituent of Lewy pathology two decades ago. The two new peptides K84s (FLVWGCLRGSAIGECVVHGGPPSRH) and K102s (FLKRWARSTRWGTASCGGS) have recently been found to significantly reduce the oligomerization and aggregation of α-Synuclein. However, it is still unclear where these peptides interact with α-Synuclein at the moment.

Objective: To examine the locations where K84s and K102s interact with α-Synuclein.

Methods: In this investigation, the PEPFOLD3 server was used to generate the 3-D structures of the K84s and K102s peptides. Using the PatchDock web server, the two peptides were docked to the α- Synuclein molecule. After that, 50 ns of Molecular Dynamics (MD) simulations using the Amberff99SBildn force field were performed on the two resulting docked complexes. The two complexes' structure, dynamics, energy profiles, and binding modes were identified through analysis of the respective MD simulation trajectories. By submitting the two complexes' lowest energy structure to the PDBsum website, the interface residues in the two complexes were identified. The per residue energy decomposition (PRED) analysis using the MM-GBSA technique was used to calculate the contributions of each residue in the α-Synuclein of (α-Synuclein-K84s/K102s) complexes to the total binding free energy.

Results: The binding of the two peptides with the α-Synuclein was demonstrated to have high binding free energy. The binding free energies of the (α-Synuclein-K84s) and (α-Synuclein-K102s) complexes are -33.61 kcal/mol and -40.88 kcal/mol respectively. Using PDBsum server analysis, it was determined that in the (α-Synuclein-K84s) complex, the residues GLY 25, ALA 29, VAL 49, LEU 38, VAL 40, GLU 28, GLY 47, LYS 32, GLU 35, GLY 36, TYR 39, VAL 48 and VAL 26 (from α-Synuclein) and SER 23, LEU 7, ILE 12, HIS 25, PHE 1, HIS 18, CYS 6, ARG 24, PRO 21 and ARG 8 (from K84s peptide) were identified to be present at the interface. In the (α-Synuclein- K102s) complex, the residues VAL 40, GLY 36, GLU 35, TYR 39, LYS 45, LEU 38, LYS 43, VAL 37, THR 44, VAL 49, VAL 48, and GLU 46 (from α-Synuclein) and ARG 10, GLY 12, GLY 18, SER 15, THR 13, SER 19, TRP 11, ALA 14, CYS 16, ARG 7, ARG 4 and GLY 17 (from K102s peptide) were identified to be present at the interface. The PRED analysis revealed that the residues PHE 1, LEU 7, ILE 12, LEU 2, VAL 3, GLY 5, and PRO 21 of the K84s peptide and residues VAL 48, ALA 29, VAL 40, TYR 39, VAL 49, VAL 26 and GLY 36 of α-Synuclein in the (α- Synuclein-K84s) complex are responsible for the intermolecular interaction. The residues ARG 4, ARG 10, TRP 11, ALA 14, SER 15, CYS 16 and SER 19 of the K102s peptide and residues GLU 46, LYS 45, VAL 49, GLU 35, VAL 48, TYR 39, and VAL 40 of α-Synuclein are responsible for the intermolecular interaction in the instance of the (α-Synuclein-K102s) complex. Additionally, it has been found that a sizable portion of the helical structure is preserved when α-Synuclein is in a complex form with the K84s and K102s peptides.

Conclusion: Taken together the data implies that the two new peptides investigated here could be suitable candidates for future therapeutic development against α-Synuclein aggregation.

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[1]
Hasegawa M, Nonaka T, Masuda-Suzukake M. Prion-like mechanisms and potential therapeutic targets in neurodegenerative disorders. Pharmacol Ther 2017; 172: 22-33.
[http://dx.doi.org/10.1016/j.pharmthera.2016.11.010] [PMID: 27916654]
[2]
Arosio P, Michaels TCT, Linse S, et al. Kinetic analysis reveals the diversity of microscopic mechanisms through which molecular chaperones suppress amyloid formation. Nat Commun 2016; 7(1): 10948.
[http://dx.doi.org/10.1038/ncomms10948] [PMID: 27009901]
[3]
Burmann BM, Gerez JA. Matečko-Burmann I, et al. Regulation of α-synuclein by chaperones in mammalian cells. Nature 2020; 577(7788): 127-32.
[http://dx.doi.org/10.1038/s41586-019-1808-9] [PMID: 31802003]
[4]
Singh SK, Dutta A, Modi G. α-Synuclein aggregation modulation: an emerging approach for the treatment of Parkinson’s disease. Future Med Chem 2017; 9(10): 1039-53.
[http://dx.doi.org/10.4155/fmc-2017-0016] [PMID: 28632413]
[5]
Pujols J, Peña-Díaz S, Lázaro DF, et al. Small molecule inhibits α-synuclein aggregation, disrupts amyloid fibrils, and prevents degeneration of dopaminergic neurons. Proc Natl Acad Sci USA 2018; 115(41): 10481-6.
[http://dx.doi.org/10.1073/pnas.1804198115] [PMID: 30249646]
[6]
Mason JM. Design and development of peptides and peptide mimetics as antagonists for therapeutic intervention. Future Med Chem 2010; 2(12): 1813-22.
[http://dx.doi.org/10.4155/fmc.10.259] [PMID: 21428804]
[7]
Mason JM, Fairlie DP. Toward peptide-based inhibitors as therapies for Parkinson’s disease. Future Med Chem 2015; 7(16): 2103-5.
[http://dx.doi.org/10.4155/fmc.15.139] [PMID: 26510474]
[8]
Fosgerau K, Hoffmann T. Peptide therapeutics: current status and future directions. Drug Discov Today 2015; 20(1): 122-8.
[http://dx.doi.org/10.1016/j.drudis.2014.10.003] [PMID: 25450771]
[9]
Santos J, Gracia P, Navarro S, et al. α-Helical peptidic scaffolds to target α-synuclein toxic species with nanomolar affinity. Nat Commun 2021; 12(1): 3752.
[http://dx.doi.org/10.1038/s41467-021-24039-2] [PMID: 33397941]
[10]
Kim YS, Lim D, Kim JY, Kang SJ, Kim YH, Im H. β-Sheetbreaking peptides inhibit the fibrillation of human α-synuclein. Biochem Biophys Res Commun 2009; 387(4): 682-7.
[http://dx.doi.org/10.1016/j.bbrc.2009.07.083] [PMID: 19622344]
[11]
Torpey JH, Meade RM, Mistry R, Mason JM, Madine J. Insights into peptide inhibition of alpha-synuclein aggregation. Front Neurosci 2020; 14: 561462.
[http://dx.doi.org/10.3389/fnins.2020.561462] [PMID: 33177976]
[12]
Ruzza P, Gazziero M, Marchi M, et al. Peptides as modulators of α-synuclein aggregation. Protein Pept Lett 2015; 22(4): 354-61.
[http://dx.doi.org/10.2174/0929866522666150209142649] [PMID: 25666040]
[13]
Sangwan S, Sahay S, Murray KA, et al. Inhibition of synucleinopathic seeding by rationally designed inhibitors. eLife 2020; 9: e46775.
[http://dx.doi.org/10.7554/eLife.46775] [PMID: 31895037]
[14]
Jin JW, Fan X, del Cid-Pellitero E, et al. Development of an α-synuclein knockdown peptide and evaluation of its efficacy in Parkinson’s disease models. Commun Biol 2021; 4(1): 232.
[http://dx.doi.org/10.1038/s42003-021-01746-6] [PMID: 33608634]
[15]
Popova B, Wang D, Rajavel A, et al. Identification of two novel peptides that inhibit α-synuclein toxicity and aggregation. Front Mol Neurosci 2021; 14: 659926.
[http://dx.doi.org/10.3389/fnmol.2021.659926] [PMID: 33912013]
[16]
Lamiable A, Thévenet P, Rey J, Vavrusa M, Derreumaux P, Tufféry P. PEP-FOLD3: faster de novo structure prediction for linear peptides in solution and in complex. Nucleic Acids Res 2016; 44(W1): W449-54.
[http://dx.doi.org/10.1093/nar/gkw329] [PMID: 27131374]
[17]
Ulmer TS, Bax A, Cole NB, Nussbaum RL. Structure and dynamics of micelle-bound human α-synuclein. J Biol Chem 2005; 280(10): 9595-603.
[http://dx.doi.org/10.1074/jbc.M411805200] [PMID: 15615727]
[18]
Berman HM, Westbrook J, Feng Z, et al. The protein data bank. Nucleic Acids Res 2000; 28(1): 235-42.
[http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]
[19]
Rose PW. Prlić A, Altunkaya A, et al. The RCSB protein data bank: integrative view of protein, gene and 3D structural information. Nucleic Acids Res 2017; 45(D1): D271-81.
[http://dx.doi.org/10.1093/nar/gku1214] [PMID: 27794042]
[20]
Duhovny D, Nussinov R, Wolfson HJ. Efficient unbound docking of rigid molecules. Berlin, Heidelberg: Springer-Verlag 2002; pp. 185-200.
[http://dx.doi.org/10.1007/3-540-45784-4_14]
[21]
Case DA. Normal mode analysis of protein dynamics. Curr Opin Struct Biol 1994; 4(2): 285-90.
[http://dx.doi.org/10.1016/S0959-440X(94)90321-2]
[22]
Hornak V, Abel R, Okur A, Strockbine B, Roitberg A, Simmerling C. Comparison of multiple Amber force fields and development of improved protein backbone parameters. Proteins 2006; 65(3): 712-25.
[http://dx.doi.org/10.1002/prot.21123] [PMID: 16981200]
[23]
Henriques J, Cragnell C, Skepö M. Molecular dynamics simulations of intrinsically disordered proteins: Force field evaluation and comparison with experiment. J Chem Theory Comput 2015; 11(7): 3420-31.
[http://dx.doi.org/10.1021/ct501178z] [PMID: 26575776]
[24]
Rauscher S, Gapsys V, Gajda MJ, Zweckstetter M, de Groot BL, Grubmüller H. Structural ensembles of intrinsically disordered proteins depend strongly on force field: a comparison to experiment. J Chem Theory Comput 2015; 11(11): 5513-24.
[http://dx.doi.org/10.1021/acs.jctc.5b00736] [PMID: 26574339]
[25]
Jorgensen WL, Chandrasekhar J, Madura JD, Impey RW, Klein ML. Comparison of simple potential functions for simulating liquid water. J Chem Phys 1983; 79(2): 926-35.
[http://dx.doi.org/10.1063/1.445869]
[26]
Salomon-Ferrer R, Götz AW, Poole D, Le Grand S, Walker RC. Routine microsecond molecular dynamics simulations with AMBER on GPUs. 2. Explicit solvent particle mesh Ewald. J Chem Theory Comput 2013; 9(9): 3878-88.
[http://dx.doi.org/10.1021/ct400314y] [PMID: 26592383]
[27]
Darden T, York D, Pedersen L. Particle mesh Ewald: An N ⋅log(N) method for Ewald sums in large systems. J Chem Phys 1993; 98(12): 10089-92.
[http://dx.doi.org/10.1063/1.464397]
[28]
Ryckaert JP, Ciccotti G, Berendsen HJ. Numerical integration of the cartesian equations of motion of a system with constraints: Molecular dynamics of n-alkanes. J Comput Phys 1977; 23(3): 327-41.
[29]
Berendsen HJC, Postma JPM, van Gunsteren WF, DiNola A, Haak JR. Molecular dynamics with coupling to an external bath. J Chem Phys 1984; 81(8): 3684-90.
[http://dx.doi.org/10.1063/1.448118]
[30]
Hou T, Li N, Li Y, Wang W. Characterization of domain-peptide interaction interface: prediction of SH3 domain-mediated protein-protein interaction network in yeast by generic structure-based models. J Proteome Res 2012; 11(5): 2982-95.
[http://dx.doi.org/10.1021/pr3000688] [PMID: 22468754]
[31]
Hou T, Wang J, Li Y, Wang W. Assessing the performance of the MM/PBSA and MM/GBSA methods. 1. The accuracy of binding free energy calculations based on molecular dynamics simulations. J Chem Inf Model 2011; 51(1): 69-82.
[http://dx.doi.org/10.1021/ci100275a] [PMID: 21117705]
[32]
Wan Y, Guan S, Qian M, et al. Structural basis of fullerene derivatives as novel potent inhibitors of protein acetylcholinesterase without catalytic active site interaction: insight into the inhibitory mechanism through molecular modeling studies. J Biomol Struct Dyn 2019; 38(2): 410-25.
[http://dx.doi.org/10.1080/07391102.2019.1576543] [PMID: 30706763]
[33]
Wang C, Greene DA, Xiao L, Qi R, Luo R. Recent developments and applications of the MMPBSA method. Front Mol Biosci 2018; 4(87): 87.
[http://dx.doi.org/10.3389/fmolb.2017.00087] [PMID: 29367919]
[34]
Wang J, Hou T, Xu X. Recent advances in free energy calculations with a combination of molecular mechanics and continuum models. Curr Computeraided Drug Des 2006; 2(3): 287-306.
[http://dx.doi.org/10.2174/157340906778226454]
[35]
Wang J, Morin P, Wang W, Kollman PA. Use of MM-PBSA in reproducing the binding free energies to HIV-1 RT of TIBO derivatives and predicting the binding mode to HIV-1 RT of efavirenz by docking and MM-PBSA. J Am Chem Soc 2001; 123(22): 5221-30.
[http://dx.doi.org/10.1021/ja003834q] [PMID: 11457384]
[36]
Wang W, Donini O, Reyes CM, Kollman PA. Biomolecular simulations: recent developments in force fields, simulations of enzyme catalysis, protein-ligand, protein-protein, and protein-nucleic acid noncovalent interactions. Annu Rev Biophys Biomol Struct 2001; 30(1): 211-43.
[http://dx.doi.org/10.1146/annurev.biophys.30.1.211] [PMID: 11340059]
[37]
Zhang W, Yang F, Ou D, et al. Prediction, docking study and molecular simulation of 3D DNA aptamers to their targets of endocrine disrupting chemicals. J Biomol Struct Dyn 2019; 37(16): 4274-82.
[http://dx.doi.org/10.1080/07391102.2018.1547222] [PMID: 30477404]
[38]
Onufriev A, Bashford D, Case DA. Exploring protein native states and large-scale conformational changes with a modified generalized born model. Proteins 2004; 55(2): 383-94.
[http://dx.doi.org/10.1002/prot.20033] [PMID: 15048829]
[39]
Weiser J, Shenkin PS, Still WC. Approximate atomic surfaces from linear combinations of pairwise overlaps (LCPO). J Comput Chem 1999; 20(2): 217-30.
[http://dx.doi.org/10.1002/(SICI)1096-987X(19990130)20:2<217:AID-JCC4>3.0.CO;2-A]
[40]
Appiah-Kubi P, Soliman M. Hybrid receptor-bound/MM-GBSA-per-residue energy-based pharmacophore modelling: enhanced approach for identification of selective LTA4H inhibitors as potential anti-inflammatory drugs. Cell Biochem Biophys 2017; 75(1): 35-48.
[http://dx.doi.org/10.1007/s12013-016-0772-3] [PMID: 27914004]
[41]
Laskowski RA. Jabłońska J, Pravda L, Vařeková RS, Thornton JM. PDBsum: structural summaries of PDB entries. Protein Sci 2018; 27(1): 129-34.
[http://dx.doi.org/10.1002/pro.3289] [PMID: 28875543]
[42]
Kabsch W, Sander C. Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 1983; 22(12): 2577-637.
[http://dx.doi.org/10.1002/bip.360221211] [PMID: 6667333]
[43]
Borah P, Sanjeev A, Mattaparthi VSK. Computational investigation on the effect of Oleuropein aglycone on the α-synuclein aggregation. J Biomol Struct Dyn 2021; 39(4): 1259-70.
[http://dx.doi.org/10.1080/07391102.2020.1728384] [PMID: 32041489]

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