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In Silico Studies of Synthetic Sulfatide as a Potential Drug Candidate Against Covid-19

Year 2022, , 238 - 245, 29.06.2022

Samet Kocabay , Mehmet Abdullah Alagöz , Hıncal Gökhan Bakır , Birnur Akkaya

https://doi.org/10.17776/csj.1081777
Cited By: 1

Abstract

Sulfatides play various roles in many biological processes such as cancer metastasis, viral infections and regulation in nerve cells. The sulfatide molecules are related with hypertension diseases in which ACE2 (Angiotensin converting enzyme) is important for regulating blood pressure. ACE2 is also a key receptor for Covid-19 and highly expressed many different tissue types. Understanding the interaction between the sulfatides and ACE2 might be a key factor to develop potential novel treatments against Covid-19. Here we studied the interaction of main protease enzyme (6LU7) of Covid-19 with native sulfatide(A), chitosan based synthetic sulfatide(B) and inhibitor N3, through in silico studies such as molecular docking, molecular dynamics, ADMET prediction and target selection analysis. The compounds A, B and N3 bind the virus protease enzyme with docking score of -5.420, -6.009, -6.161 kcal/mol respectively indicates synthetic sulfatide binds better than native sulfatide and comparable to N3. Besides, molecular dynamics studies were carried out to reveal the stability of the complexes of interest. ADMET and target prediction studies carried out to reveal pharmacological properties and toxicity of the complexes and synthetic sulfatide found to be a drug-like molecule. We anticipate that computational investigation of virus interaction mechanisms will be an important starting point for experimental research in drug development efforts against Covid-19.

Keywords

Sulfatide Covid-19 Drug design Molecular docking MD simulation.

Project Number

Yok

References

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  • [9] South A.M., Brady T.M., Flynn J.T., ACE2 (Angiotensin-Converting Enzyme 2), COVID-19, and ACE Inhibitor and Ang II (Angiotensin II) receptor blocker use during the pandemic: The pediatric perspective, Hypertension, 76(1) (2020) 16-22.
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  • [17] Buschard K., Høy M., Bokvist K., Olsen H.L., Madsbad S., Sulfatide Controls Insulin Secretion by Modulation of ATP-Sensitive K+-channel Activity and Ca2+-dependent Exocytosis in Rat Pancreatic β-cells, Diabetes, 51(8) (2002) 2514-2521.
  • [18] Yasmin F., Zeeshan M.H., Ullah I., The Role of Fenofibrate in the Treatment of COVID-19, Annals of Medicine and Surgery, 74 (2022) 102974.
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  • [22] Sastry G.M., Adzhigirey M., Day T., Annabhimoju R., Sherman W., Protein and Ligand Preparation: Parameters, Protocols, and Influence on Virtual Screening Enrichments, Journal of Computer-Aided Molecular Design, 27(3) (2013) 221-234.
  • [23] Olsson M.H., Søndergaard C.R., Rostkowski M., Jensen J.H., PROPKA3: consistent treatment of internal and Surface Residues in Empirical p K a Predictions, Journal of Chemical Theory and Computation, 7(2) (2011) 525-537.
  • [24] Friesner R.A., Banks J.L., Murphy R.B., Halgren T.A., Klicic J.J., Glide: a New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy, Journal of Medicinal Chemistry, 47(7) (2004) 1739-1749.
  • [25] Algul O., Ersan R.H., Alagoz M.A., Duran N., Burmaoglu S., An Efficient Synthesis of Novel Di-Heterocyclic Benzazole Derivatives and evaluation of Their Antiproliferative Activities, Journal of Biomolecular Structure and Dynamics, 39(18) (2021) 6926-6938.
  • [26] Bowers K.J., Chow D.E., Xu H., Dror R.O., Eastwood M.P., Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. SC'06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing; 0-7695-2700-0/06 (2006) IEEE.
  • [27] Ozten O., Kurt B.Z., Sonmez F., Dogan B., Durdagi S. Synthesis, Molecular Docking and Molecular Dynamics Studies of novel tacrine-carbamate derivatives as Potent Cholinesterase Inhibitors, Bioorganic Chemistry, 115 (2021) 105225.
  • [28] Harder E., Damm W., Maple J., Wu C., Reboul M., OPLS3: a Force Field Providing Broad Coverage of Drug-Like Small Molecules And Proteins, Journal of Chemical Theory and Computation, 12(1) (2016) 281-296.
  • [29] Daina A., Michielin O., Zoete V., SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules, Nucleic Acids Research, 47(W1) (2019) W357-W364.
  • [30] Vardhan S., Sahoo S.K., In Silico ADMET and Molecular Docking Study on Searching Potential Inhibitors from Limonoids and Triterpenoids for COVID-19, Computers in Biology and Medicine, 124 (2020) 103936.
  • [31] Gfeller D., Grosdidier A., Wirth M., Daina A., Michielin O., SwissTargetPrediction: a Web Server for Target Prediction of Bioactive Small Molecules, Nucleic Acids Research, 42(W1) (2014) W32-W38.
  • [32] Halgren T.A., Murphy R.B., Friesner R.A., Beard H.S., Frye L.L., Glide: a New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening, Journal of Medicinal Chemistry, 47(7) (2004) 1750-1759.
  • [33] Chidambaram S.K., Ali D., Alarifi S., Radhakrishnan S., Akbar I., In Silico Molecular Docking: Evaluation of Coumarin Based Derivatives Against SARS-CoV-2, Journal of Infection and Public Health, 13(11) (2020) 1671-1677.
  • [34] Ertl P., Rohde B., Selzer P., Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and its Application to the Prediction of Drug Transport Properties. Journal of Medicinal Chemistry, 43(20) (2000) 3714-3717.
  • [35] Palm K., Stenberg P., Luthman K., Artursson P., Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans, Pharmaceutical Research, 14(5) (1997) 568-571.
  • [36] Hitchcock S.A., Pennington L.D., Structure− brain Exposure Relationships, Journal of Medicinal Chemistry, 49(26) (2006) 7559-7583.
  • [37] Zhao Y.H., Abraham M.H., Le J., Hersey A., Luscombe C.N., Rate-limited Steps of Human Oral Absorption and QSAR Studies, Pharmaceutical Research, 19(10) (2002) 1446-1457.
  • [38] Wang R., Fu Y., Lai L., A New Atom-additive Method for Calculating Partition Coefficients, Journal of Chemical Information and Computer Sciences, 37(3) (1997) 615-621.
  • [39] Abraham M.H., Takács-Novák K., Mitchell R.C., On the Partition of Ampholytes: Application to Blood–Brain Distribution, Journal of Pharmaceutical Sciences, 86(3) (1997) 310-315.
  • [40] Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J., Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings, Advanced Drug Delivery Reviews, 23(1-3) (1997) 3-25.
  • [41] Delaney J.S., ESOL: Estimating Aqueous Solubility Directly from Molecular Structure, Journal of Chemical Information and Computer Sciences, 44(3) (2004) 1000-1005.
  • [42] Ali J., Camilleri P., Brown M.B., Hutt A.J., Kirton S.B., Revisiting the General Solubility equation: in silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area, Journal of Chemical Information and Modeling, 52(2) (2012) 420-428.
  • [43] Zanger U.M., Schwab M., Cytochrome P450 Enzymes in Drug Metabolism: regulation of gene expression, Enzyme Activities, and Impact of Genetic Variation, Pharmacology & Therapeutics, 138(1) (2013) 103-141.
  • [44] Sepay N., Sekar A., Halder U.C., Alarifi A., Afzal M., Anti-COVID-19 Terpenoid from marine sources: A Docking, Admet and Molecular Dynamics Study, Journal of Molecular Structure, 1228 (2021) 129433.
  • [45] Pires D.E., Blundell T.L., Ascher D.B., pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures, Journal of Medicinal Chemistry, 58(9) (2015) 4066-4072.
  • [46] Basche M., Gustafson D.L., Holden S.N., O'Bryant C.L., Gore L., A Phase I Biological and Pharmacologic Study of the Heparanase Inhibitor PI-88 in patients with Advanced Solid Tumors, Clinical Cancer Research, 12(18) (2006) 5471-5480.
  • [47] Kudchadkar R., Gonzalez R., Lewis KD., PI-88: a Novel Inhibitor of Angiogenesis, Expert Opinion on Investigational Drugs, 17(11) (2008) 1769-1776

Year 2022, , 238 - 245, 29.06.2022

Samet Kocabay , Mehmet Abdullah Alagöz , Hıncal Gökhan Bakır , Birnur Akkaya

https://doi.org/10.17776/csj.1081777
Cited By: 1

Abstract

Supporting Institution

Yok

Project Number

Yok

References

  • [1] Xu H., Zhong L., Deng J., Peng J., Dan H., High Expression Of Ace2 Receptor Of 2019-Ncov On The Epithelial Cells Of Oral Mucosa, International Journal of Oral Science, 12(1) (2020) 1-5.
  • [2] Yan R., Zhang Y., Li Y., Xia L., Guo Y., Structural Basis for the Recognition of SARS-CoV-2 by Full-Length Human ACE2, Science, 367(6485) (2020) 1444-1448.
  • [3] Dawson P., Rabold E.M., Laws R.L., Conners E.E., Gharpure R., Loss of Taste and Smell as Distinguishing Symptoms of COVID-19, Clinical Infectious Diseases, 72(4) (2021) 682-685.
  • [4] Gautier J-F., Ravussin Y., A New Symptom of COVID-19: Loss of Taste and Smell, Obesity (Silver Spring), 28(5) (2020) 848.
  • [5] Yuki K., Fujiogi M., Koutsogiannaki S., COVID-19 Pathophysiology: A review, Clinical Immunology, 215 (2020) 108427.
  • [6] Buschard K., Fenofibrate increases the amount of sulfatide which seems beneficial against Covid-19, Medical Hypotheses, 143 (2020) 110127.
  • [7] Donoghue M., Hsieh F., Baronas E., Godbout K., Gosselin M., UltraRapid Communication, Circulation Research, 87 (2000) e1-e9.
  • [8] Zhangh K., The Digestive System Is A Potential Route Of 2019 Ncovinfection: A Bioinformatics Analysis Based On Single Cell Transcriptomes, BioRxiv, 2020.
  • [9] South A.M., Brady T.M., Flynn J.T., ACE2 (Angiotensin-Converting Enzyme 2), COVID-19, and ACE Inhibitor and Ang II (Angiotensin II) receptor blocker use during the pandemic: The pediatric perspective, Hypertension, 76(1) (2020) 16-22.
  • [10] Pranata R., Lim M.A., Huang I., Raharjo S.B., Lukito A.A., Hypertension is Associated with Increased Mortality and Severity of Disease in COVID-19 Pneumonia: a Systematic Review, Meta-Analysis and Meta-Regression, Journal of the Renin-angiotensin-aldosterone System: JRAAS, 21(2) (2020) 1470320320926899.
  • [11] Yuki K., Fujiogi M., Koutsogiannaki S., COVID-19 Pathophysiology: A review., Clinical Immunology, 215 (2020) 108427.
  • [12] Kocabay S., Akkaya B., Preparation of Sulfatide Mimicking Oleic Acid Sulfated Chitosan as a Potential Inhibitor for Metastasis, International Journal of Biological Macromolecules, 147 (2020) 792-798.
  • [13] Takahashi T., Ito K., Fukushima K., Takaguchi M., Hayakawa T., Sulfatide Negatively Regulates the Fusion Process of Human Parainfluenza Virus Type 3, J. Biochem., 152(4) (2012) 373-380.
  • [14] Blomqvist M., Kaas A., Månsson J.E., Formby B., Rynmark B.M., Developmental Expression of the Type I Diabetes Related Antigen Sulfatide and Sulfated Lactosylceramide in Mammalian Pancreas, Journal of Cellular Biochemistry, 89(2) (2003) 301-310.
  • [15] Buschard K., Fredman P., Bøg‐Hansen E., Blomqvist M., Hedner J., Low Serum Concentration of Sulfatide and Presence of Sulfated Lactosylceramid are Associated with Type 2 Diabetes. The Skaraborg Project, Diabetic Medicine 22(9) (2005) 1190-1198.
  • [16] Guo R., Hu X., Yamada Y., Harada M., Nakajima T., Effects of Hypertension and Antihypertensive Treatments on Sulfatide Levels in Serum and its Metabolism, Hypertension Research, 42(5) (2019) 598-609.
  • [17] Buschard K., Høy M., Bokvist K., Olsen H.L., Madsbad S., Sulfatide Controls Insulin Secretion by Modulation of ATP-Sensitive K+-channel Activity and Ca2+-dependent Exocytosis in Rat Pancreatic β-cells, Diabetes, 51(8) (2002) 2514-2521.
  • [18] Yasmin F., Zeeshan M.H., Ullah I., The Role of Fenofibrate in the Treatment of COVID-19, Annals of Medicine and Surgery, 74 (2022) 102974.
  • [19] Davies S.P., Mycroft-West C.J., Pagani I., Hill H.J., Chen Y-H., The Hyperlipidaemic Drug Fenofibrate Significantly Reduces Infection by SARS-CoV-2 in Cell Culture Models, Frontiers in Pharmacology, 12(660490) (2021) 1755.
  • [20] Wang K.Y., Liu F., Jiang R., Yang X., You T., Structure of Mpro from COVID-19 Virus and Discovery of its Inhibitors, Nature, 2020.
  • [21] Peele K.A., Durthi C.P., Srihansa T., Krupanidhi S., Ayyagari V.S., Molecular Docking and Dynamic Simulations for Antiviral Compounds Against SARS-CoV-2: A Computational Study, Informatics in Medicine Unlocked, 19 (2020) 100345.
  • [22] Sastry G.M., Adzhigirey M., Day T., Annabhimoju R., Sherman W., Protein and Ligand Preparation: Parameters, Protocols, and Influence on Virtual Screening Enrichments, Journal of Computer-Aided Molecular Design, 27(3) (2013) 221-234.
  • [23] Olsson M.H., Søndergaard C.R., Rostkowski M., Jensen J.H., PROPKA3: consistent treatment of internal and Surface Residues in Empirical p K a Predictions, Journal of Chemical Theory and Computation, 7(2) (2011) 525-537.
  • [24] Friesner R.A., Banks J.L., Murphy R.B., Halgren T.A., Klicic J.J., Glide: a New Approach for Rapid, Accurate Docking and Scoring. 1. Method and Assessment of Docking Accuracy, Journal of Medicinal Chemistry, 47(7) (2004) 1739-1749.
  • [25] Algul O., Ersan R.H., Alagoz M.A., Duran N., Burmaoglu S., An Efficient Synthesis of Novel Di-Heterocyclic Benzazole Derivatives and evaluation of Their Antiproliferative Activities, Journal of Biomolecular Structure and Dynamics, 39(18) (2021) 6926-6938.
  • [26] Bowers K.J., Chow D.E., Xu H., Dror R.O., Eastwood M.P., Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters. SC'06: Proceedings of the 2006 ACM/IEEE Conference on Supercomputing; 0-7695-2700-0/06 (2006) IEEE.
  • [27] Ozten O., Kurt B.Z., Sonmez F., Dogan B., Durdagi S. Synthesis, Molecular Docking and Molecular Dynamics Studies of novel tacrine-carbamate derivatives as Potent Cholinesterase Inhibitors, Bioorganic Chemistry, 115 (2021) 105225.
  • [28] Harder E., Damm W., Maple J., Wu C., Reboul M., OPLS3: a Force Field Providing Broad Coverage of Drug-Like Small Molecules And Proteins, Journal of Chemical Theory and Computation, 12(1) (2016) 281-296.
  • [29] Daina A., Michielin O., Zoete V., SwissTargetPrediction: Updated Data and New Features for Efficient Prediction of Protein Targets of Small Molecules, Nucleic Acids Research, 47(W1) (2019) W357-W364.
  • [30] Vardhan S., Sahoo S.K., In Silico ADMET and Molecular Docking Study on Searching Potential Inhibitors from Limonoids and Triterpenoids for COVID-19, Computers in Biology and Medicine, 124 (2020) 103936.
  • [31] Gfeller D., Grosdidier A., Wirth M., Daina A., Michielin O., SwissTargetPrediction: a Web Server for Target Prediction of Bioactive Small Molecules, Nucleic Acids Research, 42(W1) (2014) W32-W38.
  • [32] Halgren T.A., Murphy R.B., Friesner R.A., Beard H.S., Frye L.L., Glide: a New Approach for Rapid, Accurate Docking and Scoring. 2. Enrichment Factors in Database Screening, Journal of Medicinal Chemistry, 47(7) (2004) 1750-1759.
  • [33] Chidambaram S.K., Ali D., Alarifi S., Radhakrishnan S., Akbar I., In Silico Molecular Docking: Evaluation of Coumarin Based Derivatives Against SARS-CoV-2, Journal of Infection and Public Health, 13(11) (2020) 1671-1677.
  • [34] Ertl P., Rohde B., Selzer P., Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and its Application to the Prediction of Drug Transport Properties. Journal of Medicinal Chemistry, 43(20) (2000) 3714-3717.
  • [35] Palm K., Stenberg P., Luthman K., Artursson P., Polar Molecular Surface Properties Predict the Intestinal Absorption of Drugs in Humans, Pharmaceutical Research, 14(5) (1997) 568-571.
  • [36] Hitchcock S.A., Pennington L.D., Structure− brain Exposure Relationships, Journal of Medicinal Chemistry, 49(26) (2006) 7559-7583.
  • [37] Zhao Y.H., Abraham M.H., Le J., Hersey A., Luscombe C.N., Rate-limited Steps of Human Oral Absorption and QSAR Studies, Pharmaceutical Research, 19(10) (2002) 1446-1457.
  • [38] Wang R., Fu Y., Lai L., A New Atom-additive Method for Calculating Partition Coefficients, Journal of Chemical Information and Computer Sciences, 37(3) (1997) 615-621.
  • [39] Abraham M.H., Takács-Novák K., Mitchell R.C., On the Partition of Ampholytes: Application to Blood–Brain Distribution, Journal of Pharmaceutical Sciences, 86(3) (1997) 310-315.
  • [40] Lipinski C.A., Lombardo F., Dominy B.W., Feeney P.J., Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings, Advanced Drug Delivery Reviews, 23(1-3) (1997) 3-25.
  • [41] Delaney J.S., ESOL: Estimating Aqueous Solubility Directly from Molecular Structure, Journal of Chemical Information and Computer Sciences, 44(3) (2004) 1000-1005.
  • [42] Ali J., Camilleri P., Brown M.B., Hutt A.J., Kirton S.B., Revisiting the General Solubility equation: in silico Prediction of Aqueous Solubility Incorporating the Effect of Topographical Polar Surface Area, Journal of Chemical Information and Modeling, 52(2) (2012) 420-428.
  • [43] Zanger U.M., Schwab M., Cytochrome P450 Enzymes in Drug Metabolism: regulation of gene expression, Enzyme Activities, and Impact of Genetic Variation, Pharmacology & Therapeutics, 138(1) (2013) 103-141.
  • [44] Sepay N., Sekar A., Halder U.C., Alarifi A., Afzal M., Anti-COVID-19 Terpenoid from marine sources: A Docking, Admet and Molecular Dynamics Study, Journal of Molecular Structure, 1228 (2021) 129433.
  • [45] Pires D.E., Blundell T.L., Ascher D.B., pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures, Journal of Medicinal Chemistry, 58(9) (2015) 4066-4072.
  • [46] Basche M., Gustafson D.L., Holden S.N., O'Bryant C.L., Gore L., A Phase I Biological and Pharmacologic Study of the Heparanase Inhibitor PI-88 in patients with Advanced Solid Tumors, Clinical Cancer Research, 12(18) (2006) 5471-5480.
  • [47] Kudchadkar R., Gonzalez R., Lewis KD., PI-88: a Novel Inhibitor of Angiogenesis, Expert Opinion on Investigational Drugs, 17(11) (2008) 1769-1776
There are 47 citations in total.

Details

Primary Language English
Journal Section Natural Sciences
Authors

Samet Kocabay 0000-0002-0120-2910

Mehmet Abdullah Alagöz 0000-0001-9961-4616

Hıncal Gökhan Bakır 0000-0003-0974-1481

Birnur Akkaya 0000-0001-9139-1884

Project Number Yok
Publication Date June 29, 2022
Submission Date March 2, 2022
Acceptance Date June 3, 2022
Published in Issue Year 2022

Cite

APA Kocabay, S., Alagöz, M. A., Bakır, H. G., Akkaya, B. (2022). In Silico Studies of Synthetic Sulfatide as a Potential Drug Candidate Against Covid-19. Cumhuriyet Science Journal, 43(2), 238-245. https://doi.org/10.17776/csj.1081777

Cited By

Investigation of inhibitory effect of sulfated chitosan oligomer on human heparanase enzyme: in silico and in vitro studies
Journal of Biomolecular Structure and Dynamics
https://doi.org/10.1080/07391102.2024.2317421