MOLECULAR DYNAMICS SIMULATIONS OF SMALL MOLECULE DRUGS FOR DYSFERLINOPATHY TARGETING LYS85 AND GLU64 IN DYSFERLIN (DYSF) PROTEIN
Main Article Content
Keywords
LGMD2B, Dysferlin, Dysferlinopathy, Molecular docking, Simulation, Muscular dystrophy
Abstract
Background: Dysferlin is a Ca2+-activated lipid-binding protein. Dysferlin has a crucial role in the regulation of the immune system and the acceleration of muscle repair through the process of muscle regeneration. Muscular dystrophy emerges in dysferlin-deficient muscle due to defective membrane repair and significant muscular inflammation.
Methods: This study proposes an in-silico approach employing molecular docking to suggest potential small molecules for the therapeutic intervention of muscular dystrophy (MD) by inhibiting mutated dysferlin. The extensive information was gathered through a vast literature to be processed for the molecular docking of dysferlin i.e. DYSF and the screened small molecule drugs from ZINC database. The compounds that adhere to the Lipinski rule of an ideal medicine were chosen, and a docking simulation was performed using the Patchdock server.
Results: The molecular docking and molecular dynamics simulations of dysferlin protein with ZINC98607668 and ZINC98606149 drug complexes yielded remarkable results, revealing a substantial correlation with the dysferlin protein's amino acid residues LYS85 and GLU64. These compounds have affinities towards the dysferlin target site, rendering them suitable pharmaceutical agents for treating dysferlinopathy, particularly in case of LGMD2B.
Conclusions: In concern of drugs screening and application for muscular dystrophy it is suggested that additional research must be done to conduct advanced sources on various drug-ligands involved from the recognized databases to identify the most lethal configuration for dysferlinopathy engagements in genetic behavior.
References
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27. Tripathi AK, Desai PP, Vishawanatha JK. First Report of the Peptide Inhibitors of Cancer Cell Migration From MIEN1 Protein Sequence. 2023.
28. Abdusalam AAA, Murugaiyah VJFimb. Identification of potential inhibitors of 3CL protease of SARS-CoV-2 from ZINC database by molecular docking-based virtual screening. 2020;7:419.
29. Quan PM, Binh VN, Ngan VT, Trung NT, Anh NQJVJoC. Molecular docking studies of Vinca alkaloid derivatives on Tubulin. 2019;57(6):702-06.
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31. Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im WJTJocp. CHARMM-GUI supports the Amber force fields. 2020;153(3).
32. Hiew SH, Mohanram H, Ning L, Guo J, Sánchez‐Ferrer A, Shi X, et al. A Short Peptide Hydrogel with High Stiffness Induced by 310‐Helices to β‐Sheet Transition in Water. 2019;6(21):1901173.
33. Khajeh K, Aminfar H, Masuda Y, Mohammadpourfard MJJoMM. Implementation of magnetic field force in molecular dynamics algorithm: NAMD source code version 2.12. 2020;26:1-9.
34. Cavada BS, Osterne VJS, Pinto-Junior VR, Souza LAG, Lossio CF, Silva MTL, et al. Molecular dynamics and binding energy analysis of Vatairea guianensis lectin: a new tool for cancer studies. 2020;26:1-9.
35. Humphrey W, Dalke A, Schulten KJJomg. VMD: visual molecular dynamics. 1996;14(1):33-38.
36. Cáceres EL, Tudor M, Cheng ACJFMC. Deep learning approaches in predicting ADMET properties. 2020;12(22):1995-99.
37. Mahanthesh M, Ranjith D, Yaligar R, Jyothi R, Narappa G, Ravi MJJoP, et al. Swiss ADME prediction of phytochemicals present in Butea monosperma (Lam.) Taub. 2020;9(3):1799-809.
38. Banerjee P, Ulker OCJTm, methods. Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products. 2022;32(7):542-48.
39. Cenacchi G, Fanin M, De Giorgi LB, Angelini CJJocp. Ultrastructural changes in dysferlinopathy support defective membrane repair mechanism. 2005;58(2):190-95.
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42. Haq FU, Abro A, Raza S, Liedl KR, Azam SSJJoMG, Modelling. Molecular dynamics simulation studies of novel β-lactamase inhibitor. 2017;74:143-52.
43. Banerjee P, Eckert AO, Schrey AK, Preissner RJNar. ProTox-II: a webserver for the prediction of toxicity of chemicals. 2018;46(W1):W257-W63.
44. Parra AL, Yhebra RS, Sardiñas IG, Buela LIJP. Comparative study of the assay of Artemia salina L. and the estimate of the medium lethal dose (LD50 value) in mice, to determine oral acute toxicity of plant extracts. 2001;8(5):395-400.
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48. Amor F, Vu Hong A, Corre G, Sanson M, Suel L, Blaie S, et al. Cholesterol metabolism is a potential therapeutic target in Duchenne muscular dystrophy. 2021;12(3):677-93.
49. Finkler JM, de Carvalho SC, Santo Neto H, Marques MJJTAR. Cardiac and skeletal muscle changes associated with rosuvastatin therapy in dystrophic mdx mice. 2020;303(8):2202-12.
50. Mucha O, Podkalicka P, Kaziród K, Samborowska E, Dulak J, Łoboda AJSM. Simvastatin does not alleviate muscle pathology in a mouse model of Duchenne muscular dystrophy. 2021;11(1):1-16.
51. Verhaart IE, Cappellari O, Tanganyika-de Winter CL, Plomp JJ, Nnorom S, Wells KE, et al. Simvastatin treatment does not ameliorate muscle pathophysiology in a mouse model for Duchenne muscular dystrophy. 2021;8(5):845-63.
52. White Z, Milad N, Sellers SL, Bernatchez PJFiP. Effect of dysferlin deficiency on atherosclerosis and plasma lipoprotein composition under normal and hyperlipidemic conditions. 2021;12:675322.
53. Luo J, Yang H, Song B-LJNrMcb. Mechanisms and regulation of cholesterol homeostasis. 2020;21(4):225-45.
54. Terrill JR, Radley‐Crabb HG, Iwasaki T, Lemckert FA, Arthur PG, Grounds MDJTFj. Oxidative stress and pathology in muscular dystrophies: focus on protein thiol oxidation and dysferlinopathies. 2013;280(17):4149-64.
55. Jia FF, Drew AP, Nicholson GA, Corbett A, Kumar KRJND. Facioscapulohumeral muscular dystrophy type 2: An update on the clinical, genetic, and molecular findings. 2021;31(11):1101-12.
56. Díaz J, Woudt L, Suazo L, Garrido C, Caviedes P, CÁrdenas AM, et al. Broadening the imaging phenotype of dysferlinopathy at different disease stages. 2016;54(2):203-10.
57. Choi MH, Ow JR, Yang N-D, Taneja RJOM, Longevity C. Oxidative stress-mediated skeletal muscle degeneration: molecules, mechanisms, and therapies. 2016;2016.
58. Rajakumar D, Senguttuvan S, Alexander M, Oommen AJLs. Involvement of oxidative stress, nuclear factor kappa B and the ubiquitin proteasomal pathway in dysferlinopathy. 2014;108(1):54-61.
59. Dhanarajan R, Patil AB, Alexander M, Chacko G, Oommen AJIJCRI. Degradation of myofibrillar proteins and inadequate antioxidants in selective muscle wasting of limb girdle muscular dystrophy. 2011;2:6-11.
2. Morgan JE, Zammit PSJEcr. Direct effects of the pathogenic mutation on satellite cell function in muscular dystrophy. 2010;316(18):3100-08.
3. Singh A, Vikram A, Singh M, Tripathi S, editors. Classification of neuromuscular disorders using machine learning techniques. Soft Computing: Theories and Applications: Proceedings of SoCTA 2019; 2020: Springer.
4. Bashir R, Britton S, Strachan T, Keers S, Vafiadaki E, Lako M, et al. A gene related to Caenorhabditis elegans spermatogenesis factor fer-1 is mutated in limb-girdle muscular dystrophy type 2B. 1998;20(1):37-42.
5. Liu J, Aoki M, Illa I, Wu C, Fardeau M, Angelini C, et al. Dysferlin, a novel skeletal muscle gene, is mutated in Miyoshi myopathy and limb girdle muscular dystrophy. 1998;20(1):31-36.
6. Aoki M, Liu J, Richard I, Bashir R, Britton S, Keers S, et al. Genomic organization of the dysferlin gene and novel mutations in Miyoshi myopathy. 2001;57(2):271-78.
7. Anderson LV, Davison K, Moss JA, Young C, Cullen MJ, Walsh J, et al. Dysferlin is a plasma membrane protein and is expressed early in human development. 1999;8(5):855-61.
8. Illarioshkin S, Ivanova–Smolenskaya I, Greenberg C, Nylen E, Sukhorukov V, Poleshchuk V, et al. Identical dysferlin mutation in limb-girdle muscular dystrophy type 2B and distal myopathy. 2000;55(12):1931-33.
9. Roseguini BT, Silva LM, Polotow TG, Barros MP, Souccar C, Han SWJJoVS. Effects of N-acetylcysteine on skeletal muscle structure and function in a mouse model of peripheral arterial insufficiency. 2015;61(3):777-86.
10. Díaz Jara J, Woudt L, Suazo Rojas LA, Garrido Inostroza CA, Caviedes Fernández P, Cárdenas AM, et al. Broadening the imaging phenotype of dysferlinopathy at different diseasestages. 2016.
11. Woudt L, Di Capua GA, Krahn M, Castiglioni C, Hughes R, Campero M, et al. Toward an objective measure of functional disability in dysferlinopathy. 2016;53(1):49-57.
12. Fernández G, Arias-Bravo G, Bevilacqua JA, Castillo-Ruiz M, Caviedes P, Sáez JC, et al. Myofibers deficient in connexins 43 and 45 expression protect mice from skeletal muscle and systemic dysfunction promoted by a dysferlin mutation. 2020;1866(8):165800.
13. Abdullah N, Padmanarayana M, Marty NJ, Johnson CPJBj. Quantitation of the calcium and membrane binding properties of the C2 domains of dysferlin. 2014;106(2):382-89.
14. Corbalan-Garcia S, Gómez-Fernández JCJBeBA-B. Signaling through C2 domains: more than one lipid target. 2014;1838(6):1536-47.
15. Matsuda C, Miyake K, Kameyama K, Keduka E, Takeshima H, Imamura T, et al. The C2A domain in dysferlin is important for association with MG53 (TRIM72). 2012;4.
16. Han RJSm. Muscle membrane repair and inflammatory attack in dysferlinopathy. 2011;1(1):1-8.
17. Lingappan KJCoit. NF-κB in oxidative stress. 2018;7:81-86.
18. García-Campos P, Báez-Matus X, Jara-Gutiérrez C, Paz-Araos M, Astorga C, Cea LA, et al. N-acetylcysteine reduces skeletal muscles oxidative stress and improves grip strength in dysferlin-deficient Bla/J mice. 2020;21(12):4293.
19. Cai C, Masumiya H, Weisleder N, Matsuda N, Nishi M, Hwang M, et al. MG53 nucleates assembly of cell membrane repair machinery. 2009;96(3):361a.
20. Aldewachi H, Al-Zidan RN, Conner MT, Salman MMJB. High-throughput screening platforms in the discovery of novel drugs for neurodegenerative diseases. 2021;8(2):30.
21. Blay V, Tolani B, Ho SP, Arkin MRJDDT. High-throughput screening: today’s biochemical and cell-based approaches. 2020;25(10):1807-21.
22. Anwar T, Kumar P, Khan AU. Modern tools and techniques in computer-aided drug design. Molecular docking for computer-aided drug design: Elsevier; 2021. p. 1-30.
23. Pandey S, Singh BJCc-add. De-novo drug design, molecular docking and in-silico molecular prediction of AChEI analogues through CADD approaches as anti-Alzheimer’s agents. 2020;16(1):54-72.
24. Sargolzaei MJJoMG, Modelling. Effect of nelfinavir stereoisomers on coronavirus main protease: Molecular docking, molecular dynamics simulation and MM/GBSA study. 2021;103:107803.
25. Rose Y, Duarte JM, Lowe R, Segura J, Bi C, Bhikadiya C, et al. RCSB Protein Data Bank: architectural advances towards integrated searching and efficient access to macromolecular structure data from the PDB archive. 2021;433(11):166704.
26. Sharma S, Sharma A, Gupta U. Molecular Docking studies on the Anti-fungal activity of Allium sativum (Garlic) against Mucormycosis (black fungus) by BIOVIA discovery studio visualizer 21.1. 0.0. 2021.
27. Tripathi AK, Desai PP, Vishawanatha JK. First Report of the Peptide Inhibitors of Cancer Cell Migration From MIEN1 Protein Sequence. 2023.
28. Abdusalam AAA, Murugaiyah VJFimb. Identification of potential inhibitors of 3CL protease of SARS-CoV-2 from ZINC database by molecular docking-based virtual screening. 2020;7:419.
29. Quan PM, Binh VN, Ngan VT, Trung NT, Anh NQJVJoC. Molecular docking studies of Vinca alkaloid derivatives on Tubulin. 2019;57(6):702-06.
30. Hönig SM, Lemmen C, Rarey MJWIRCMS. Small molecule superposition: A comprehensive overview on pose scoring of the latest methods. 2023;13(2):e1640.
31. Lee J, Hitzenberger M, Rieger M, Kern NR, Zacharias M, Im WJTJocp. CHARMM-GUI supports the Amber force fields. 2020;153(3).
32. Hiew SH, Mohanram H, Ning L, Guo J, Sánchez‐Ferrer A, Shi X, et al. A Short Peptide Hydrogel with High Stiffness Induced by 310‐Helices to β‐Sheet Transition in Water. 2019;6(21):1901173.
33. Khajeh K, Aminfar H, Masuda Y, Mohammadpourfard MJJoMM. Implementation of magnetic field force in molecular dynamics algorithm: NAMD source code version 2.12. 2020;26:1-9.
34. Cavada BS, Osterne VJS, Pinto-Junior VR, Souza LAG, Lossio CF, Silva MTL, et al. Molecular dynamics and binding energy analysis of Vatairea guianensis lectin: a new tool for cancer studies. 2020;26:1-9.
35. Humphrey W, Dalke A, Schulten KJJomg. VMD: visual molecular dynamics. 1996;14(1):33-38.
36. Cáceres EL, Tudor M, Cheng ACJFMC. Deep learning approaches in predicting ADMET properties. 2020;12(22):1995-99.
37. Mahanthesh M, Ranjith D, Yaligar R, Jyothi R, Narappa G, Ravi MJJoP, et al. Swiss ADME prediction of phytochemicals present in Butea monosperma (Lam.) Taub. 2020;9(3):1799-809.
38. Banerjee P, Ulker OCJTm, methods. Combinative ex vivo studies and in silico models ProTox-II for investigating the toxicity of chemicals used mainly in cosmetic products. 2022;32(7):542-48.
39. Cenacchi G, Fanin M, De Giorgi LB, Angelini CJJocp. Ultrastructural changes in dysferlinopathy support defective membrane repair mechanism. 2005;58(2):190-95.
40. Vincent AE, Rosa HS, Alston CL, Grady JP, Rygiel KA, Rocha MC, et al. Dysferlin mutations and mitochondrial dysfunction. 2016;26(11):782-88.
41. Sargsyan K, Grauffel C, Lim CJJoct, computation. How molecular size impacts RMSD applications in molecular dynamics simulations. 2017;13(4):1518-24.
42. Haq FU, Abro A, Raza S, Liedl KR, Azam SSJJoMG, Modelling. Molecular dynamics simulation studies of novel β-lactamase inhibitor. 2017;74:143-52.
43. Banerjee P, Eckert AO, Schrey AK, Preissner RJNar. ProTox-II: a webserver for the prediction of toxicity of chemicals. 2018;46(W1):W257-W63.
44. Parra AL, Yhebra RS, Sardiñas IG, Buela LIJP. Comparative study of the assay of Artemia salina L. and the estimate of the medium lethal dose (LD50 value) in mice, to determine oral acute toxicity of plant extracts. 2001;8(5):395-400.
45. Prosser BL, Khairallah RJ, Ziman AP, Ward CW, Lederer WJJom, cardiology c. X-ROS signaling in the heart and skeletal muscle: Stretch-dependent local ROS regulates [Ca2+] i. 2013;58:172-81.
46. Potgieter M, Pretorius E, Van der Merwe C, Beukes M, Vieira W, Auer R, et al. Histological assessment of SJL/J mice treated with the antioxidants coenzyme Q10 and resveratrol. 2011;42(3):275-82.
47. Van der Spuy WJ, Pretorius E. Qualitative effects of resveratrol and coenzyme Q10 administration on the gluteus complex muscle morphology of SJL/J mice with dysferlinopathy. 2011.
48. Amor F, Vu Hong A, Corre G, Sanson M, Suel L, Blaie S, et al. Cholesterol metabolism is a potential therapeutic target in Duchenne muscular dystrophy. 2021;12(3):677-93.
49. Finkler JM, de Carvalho SC, Santo Neto H, Marques MJJTAR. Cardiac and skeletal muscle changes associated with rosuvastatin therapy in dystrophic mdx mice. 2020;303(8):2202-12.
50. Mucha O, Podkalicka P, Kaziród K, Samborowska E, Dulak J, Łoboda AJSM. Simvastatin does not alleviate muscle pathology in a mouse model of Duchenne muscular dystrophy. 2021;11(1):1-16.
51. Verhaart IE, Cappellari O, Tanganyika-de Winter CL, Plomp JJ, Nnorom S, Wells KE, et al. Simvastatin treatment does not ameliorate muscle pathophysiology in a mouse model for Duchenne muscular dystrophy. 2021;8(5):845-63.
52. White Z, Milad N, Sellers SL, Bernatchez PJFiP. Effect of dysferlin deficiency on atherosclerosis and plasma lipoprotein composition under normal and hyperlipidemic conditions. 2021;12:675322.
53. Luo J, Yang H, Song B-LJNrMcb. Mechanisms and regulation of cholesterol homeostasis. 2020;21(4):225-45.
54. Terrill JR, Radley‐Crabb HG, Iwasaki T, Lemckert FA, Arthur PG, Grounds MDJTFj. Oxidative stress and pathology in muscular dystrophies: focus on protein thiol oxidation and dysferlinopathies. 2013;280(17):4149-64.
55. Jia FF, Drew AP, Nicholson GA, Corbett A, Kumar KRJND. Facioscapulohumeral muscular dystrophy type 2: An update on the clinical, genetic, and molecular findings. 2021;31(11):1101-12.
56. Díaz J, Woudt L, Suazo L, Garrido C, Caviedes P, CÁrdenas AM, et al. Broadening the imaging phenotype of dysferlinopathy at different disease stages. 2016;54(2):203-10.
57. Choi MH, Ow JR, Yang N-D, Taneja RJOM, Longevity C. Oxidative stress-mediated skeletal muscle degeneration: molecules, mechanisms, and therapies. 2016;2016.
58. Rajakumar D, Senguttuvan S, Alexander M, Oommen AJLs. Involvement of oxidative stress, nuclear factor kappa B and the ubiquitin proteasomal pathway in dysferlinopathy. 2014;108(1):54-61.
59. Dhanarajan R, Patil AB, Alexander M, Chacko G, Oommen AJIJCRI. Degradation of myofibrillar proteins and inadequate antioxidants in selective muscle wasting of limb girdle muscular dystrophy. 2011;2:6-11.
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Jun 8, 2024
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Shumaila Azam, Sahar Fazal, & Gulnaz Parveen. (2024). MOLECULAR DYNAMICS SIMULATIONS OF SMALL MOLECULE DRUGS FOR DYSFERLINOPATHY TARGETING LYS85 AND GLU64 IN DYSFERLIN (DYSF) PROTEIN. Journal of Population Therapeutics and Clinical Pharmacology, 31(6), 2477-2494. https://doi.org/10.53555/jptcp.v31i6.6850
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Author Biographies
Shumaila Azam
Department of Bioinformatics and Biosciences, Faculty of Life Sciences, Capital University of Science and Technology, Islamabad, Pakistan.
Sahar Fazal
Department of Bioinformatics and Biosciences, Faculty of Life Sciences, Capital University of Science and Technology, Islamabad, Pakistan.
Gulnaz Parveen
Department of Botany, Women University Swabi, Pakistan
How to Cite
Shumaila Azam, Sahar Fazal, & Gulnaz Parveen. (2024). MOLECULAR DYNAMICS SIMULATIONS OF SMALL MOLECULE DRUGS FOR DYSFERLINOPATHY TARGETING LYS85 AND GLU64 IN DYSFERLIN (DYSF) PROTEIN. Journal of Population Therapeutics and Clinical Pharmacology, 31(6), 2477-2494. https://doi.org/10.53555/jptcp.v31i6.6850