Design of some potent non-toxic Autoimmune disorder inhibitors based on 2D-QSAR, CoMFA, molecular docking, and molecular dynamics investigations

Abstract

Current clinical research suggests that inhibitors of protein arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) which are of biological and therapeutic interest, may show potential in treating rheumatoid arthritis, type 1 diabetes, Graves’ disease and other autoimmune disorder. In the present study, a comprehensive analysis was conducted on a collection of 32 compounds concerning their anti-rheumatoid arthritis activity as inhibitors of PAD4. This analysis represents the first instance in which these compounds were computationally examined, employing an in-silico approach that considered 2D-3D QSAR modeling, and molecular docking and was further validated through molecular dynamics and ADMET properties assessment. A credible 2D QSAR (Q_LOO^2 = 0.6611 and R^2 = 0.7535) model was constructed and verified using an external validation test set, Y-randomization, variance inflation factor (VIF), mean effect (MF), and William’s plot applicability domain (AD). Ligand-based alignment was implemented in the 3D-QSAR examination. The outcomes demonstrated that CoMFA (uvepls) (Q²LOO = 0.5877; R² = 0.9983) possess remarkable stability and foresight. The internal validation indicated that CoMFA (uvepls) MIFs display superior predictive capability compared to COMFA (ffdsel). Structural criteria determined by the contour maps of the model and molecular docking simulations were strategically employed to computationally develop 10 new, non-toxic autoimmune disease inhibitors with increased efficacy. Docking tests were done on the newly developed compounds to illustrate their binding mechanism and to identify critical interaction residues inside the active region of rheumatoid arthritis (PDB id: 3BLU). In addition, docking results of the selected designed compounds inside the active sites of type 1 diabetes receptor (6DFX), and Graves’ disease receptor (4QT5) demonstrated their rheumatoid arthritis (PDB id: 3BLU) selectivity. A molecular dynamics simulation and binding free energy calculations using the MM/GBSA technique confirmed the stability of the proposed compound D4 inside the rheumatoid arthritis (3BLU) receptor active site. In summary, the results of our investigation might give considerable insight into the future design and development of new autoimmune disease inhibitors.

Keywords

Autoimmune disorder / Rheumatoid arthritis / Type 1 diabetes / Graves’ disease / computing assisted molecular design / 2D-QSAR / CoMFA / Docking / MD simulations, and ADMET

References

[1] O’Bryan T. The autoimmune fix: how to stop the hidden autoimmune damage that keeps you sick, fat, and tired before it turns into disease. Rodale. 2016.

[2] Vojdani A, Vojdani E, Vojdani C. The Immune System: Our Body’s Homeland Security against Disease. Integrative and Functional Medical Nutrition Therapy: Principles and Practices. 2020:285–302.

[3] Wang L, Wang FS, Gershwin ME. Human autoimmune diseases: a comprehensive update. J Intern Med. 2015;278(4):369–395.

[4] Antonelli A, Ferrari SM, Corrado A, Di Domenicantonio A, Fallahi P. Autoimmune thyroid disorders. Autoimmun Rev. 2015;14(2):174–180.

[5] Popoviciu MS, Kaka N, Sethi Y, Patel N, Chopra H, Cavalu S. Type 1 diabetes Mellitus and autoimmune diseases: a critical review of the association and the application of personalized medicine. J Personalized Med. 2023;13(3):422.

[6] Alam J, Jantan I, Bukhari SN. Rheumatoid arthritis: recent advances on its etiology, role of cytokines and pharmacotherapy. Biomed Pharmacother. 2017;92:615–633.

[7] Nkemdilim OC. Differential diagnosis and tests of rheumatoid arthritis and its implication for physiotherapy. International Journal of Innovative Research and Advanced Studies (IJIRAS). 2018;5(12):18–22.

[8] Fang Q, Zhou C, Nandakumar KS. Molecular and cellular pathways contributing to joint damage in rheumatoid arthritis. Mediat Inflamm. 2020;2020.

[9] Masoumi M, Bashiri H, Khorramdelazad H, et al. Destructive roles of fibroblast-like synoviocytes in chronic inflammation and joint damage in rheumatoid arthritis. Inflammation. 2021;44:466–479.

[10] Mizejewski GJ. Alpha-fetoprotein: immunomodulation in autoimmune diseases during pregnancy and puerperium stages. GSC Biological and Pharmaceutical Sciences. 2022;20(2):102–113.

[11] Fazal SA, Khan M, Nishi SE, et al. A clinical update and global economic burden of rheumatoid arthritis. Endocr Metab Immune Disord -Drug Targets. 2018;18(2):98–109.

[12] Joseph A, Brasington R, Kahl L, Ranganathan P, Cheng TP, Atkinson J. Immunologic rheumatic disorders. J Allergy Clin Immunol. 2010;125(2):S204–S215.

[13] Petersen SE, Aung N, Sanghvi MM, et al. Reference ranges for cardiac structure and function using cardiovascular magnetic resonance (CMR) in Caucasians from the UK Biobank population cohort. J Cardiovasc Magn Reson. 2017;(1):1–9.

[14] Burrack AL, Martinov T, Fife BT. T cell-mediated beta cell destruction: autoimmunity and alloimmunity in the context of type 1 diabetes. Front Endocrinol. 2017 Dec 5;8:343.

[15] Kahaly GJ, Hansen MP. Type 1 diabetes associated autoimmunity. Autoimmun Rev. 2016;15(7):644–648.

[16] Nasa P, Chaudhary S, Shrivastava PK, Singh A. Euglycemic diabetic ketoacidosis: a missed diagnosis. World J Diabetes. 2021;12(5):514.

[17] Mauricio D, Alonso N, Gratacòs M. Chronic diabetes complications: the need to move beyond classical concepts. Trends in Endocrinology & Metabolism. 2020;31(4):287–295.

[18] Barski L, Eshkoli T, Brandstaetter E, Jotkowitz A. Euglycemic diabetic ketoacidosis. Eur J Intern Med. 2019;63:9–14.

[19] Mobasseri M, Shirmohammadi M, Amiri T, Vahed N, Fard HH, Ghojazadeh M. Prevalence and incidence of type 1 diabetes in the world: a systematic review and meta-analysis. Health Promot Perspect. 2020;10(2):98.

[20] Chiang JL, Maahs DM, Garvey KC, et al. Type 1 diabetes in children and adolescents: a position statement by the American Diabetes Association. Diabetes Care. 2018;41(9):2026.

[21] Xia Y, Xie Z, Huang G, Zhou Z. Incidence and trend of type 1 diabetes and the underlying environmental determinants. Diabetes/metabolism research and reviews. 2019;35(1):e3075.

[22] Gregory GA, Robinson TI, Linklater SE, et al. Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinol. 2022;10(10):741–760.

[23] Saeedi P, Petersohn I, Salpea P, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the international diabetes federation diabetes atlas. Diabetes Res Clin Pract. 2019;157:107843.

[24] Davies TF, Andersen S, Latif R, et al. Graves’ disease. Nat Rev Dis Prim. 2020;6(1):52.

[25] Edache EI, Uzairu A, Mamza PA, et al. Combining docking, molecular dynamics simulations, AD-MET pharmacokinetics properties, and MMGBSA calculations to create specialized protocols for running effective virtual screening campaigns on the autoimmune disorder and SARS-CoV-2 main protease. Front Mol Biosci. 2023;10.

[26] Grubczak K, Starosz A, Stożek K, Bossowski F, Moniuszko M, Bossowski A. Regulatory B cells involvement in autoimmune phenomena occurring in pediatric graves’ disease patients. Int J Mol Sci. 2021;22(20):10926.

[27] DiCarlo EF, Kahn LB. Inflammatory diseases of the bones and joints. Semin Diagn Pathol. 2011;28(1):53–64.

[28] Edache EI, Uzairu A, Mamza PA, Shallangwa GA. Structure-based simulated scanning of Rheumatoid Arthritis inhibitors:2D-QSAR, 3D-QSAR, Docking, Molecular dynamics simulation, and Lipophilicity indices calculation. Scientific African. 2022;15:e01088.

[29] Edache EI, Uzairu A, Mamza PA, Shallangwa GA, Azam M, Min K. Methimazole and propylthiouracil design as a drug for anti-graves’ disease: structural studies, hirshfeld surface analysis, DFT calculations, molecular docking, molecular dynamics simulations, and design as a drug for anti-graves’ disease. J Mol Struct. 2023:135913.

[30] Alghamdi M, Alasmari D, Assiri A, et al. An overview of the intrinsic role of citrullination in autoimmune disorders. Journal of immunology research. 2019:7592851.

[31] Koushik S, Joshi N, Nagaraju S, et al. PAD4: pathophysiology, current therapeutics and future perspective in rheumatoid arthritis. Expert Opin Ther Targets. 2017;21(4):433–447.

[32] Willis VC, Banda NK, Cordova KN, et al. Protein arginine deiminase 4 inhibition is sufficient for the amelioration of collagen-induced arthritis. Clin Exp Immunol. 2017;188(2):263–274.

[33] Kotsias F, Cebrian I, Alloatti A. Antigen processing and presentation. International review of cell and molecular biology. 2019;348:69–121.

[34] Szkudlinski MW, Fremont V, Ronin C, Weintraub BD. Thyroid-stimulating hormone and thyroid-stimulating hormone receptor structure-function relationships. Physiol Rev. 2002;82(2):473–502.

[35] Chu YD, Yeh CT. The molecular function and clinical role of thyroid stimulating hormone receptor in cancer cells. Cells. 2020;9(7):1730.

[36] Burbelo PD, Iadarola MJ, Alevizos I, Sapio MR. Transcriptomic segregation of human autoantigens useful for the diagnosis of autoimmune diseases. Mol Diagn Ther. 2016;20:415–427.

[37] Ugbe FA, Shallangwa GA, Uzairu A, et al. Cheminformatics-based discovery of new organoselenium compounds with potential for the treatment of cutaneous and visceral leishmaniasis. J Biomol Struct Dyn. 2023:1–24. https://doi.org/10.1080/07391102.2023.2279269.

[38] Edache EI, Uzairu A, Mamza PA, Shallangwa GA. 2D-QSAR, docking, molecular dynamics simulations with the MM/GBSA approaches against graves’ disease and PTPN22. Borneo Journal of Pharmacy. 2023;6(3):229–248.

[39] Audu UF, Adamu SG, Adamu U, Ibrahim A. Theoretical modeling and design of some pyrazolopyrimidine derivatives as Wolbachia inhibitors, targeting lymphatic filariasis and onchocerciasis. Silico Pharmacology. 2022;10(1).

[40] Edache EI, Dawi HA, Ugbe FA. Dynamics simulations and structural studies of some selected inhibitors of the glycoprotein (GPC) of lassa virus. J. Appl. Organomet. Chem. 2023;3(3):224–244. https://doi.org/10.22034/JAOC.2023.410946.1103.

[41] Edache EI, Uzairu A, Mamza PA, Shallangwa GA. Theoretical investigation of the cooperation of iminoguanidine with the enzymes-binding domain of covid-19 and bacterial lysozyme inhibitors and their pharmacokinetic properties. Journal of the Mexican Chemical Society. 2022;66(4):513–542.

[42] Edache EI, Uzairu A, Abechi SE. Quantitative structure and activity relationship modeling study of anti-HIV-1 RT inhibitors: genetic function approximation and density function theory methods. J Comput Methods Mol Des. 2015;5(4):61–76.

[43] Hanwell MD, Curtis DE, Lonie DC, Vandermeersch T, Zurek E, Hutchison GR. Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. J Cheminf. 2012;4(1):17.

[44] Yap CW. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011 May;32(7):1466–1474.

[45] Gramatica P, Chirico N, Papa E, Cassani S, Kovarich S. QSARINS: a new software for the development, analysis, and validation of QSAR MLR models. J Comput Chem. 2013.

[46] Tosco P, Balle T, Shiri F. Open3DALIGN: an open-source software aimed at unsupervised ligand alignment. J Comput Aided Mol Des. 2011;25:777–783.

[47] Tosco P, Balle T. Open3DQSAR: a new open-source software aimed at high-throughput chemometric analysis of molecular interaction fields. J Mol Model. 2011:201–208.

[48] Wold S, Sjöström M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab Syst. 2001;58(2):109–130.

[49] Berman HM, Westbrook J, Feng Z, et al. The Protein Data Bank. 2006, 1999-.

[50] Morris GM, Huey R, Lindstrom W, et al. AutoDock4 and AutoDockToolS4: automated docking with selective receptor flexibility. J Comput Chem. 2009;30(16):2785–2791.

[51] Sanner MF. Python: a programming language for software integration and development. J Mol Graph Model. 1999 Feb 1;17(1):57–61.

[52] Eberhardt J, Santos-Martins D, Tillack AF, Forli S. AutoDock Vina 1.2. 0: new docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61(8):3891–3898.

[53] Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–461.

[54] Ravindranath PA, Forli S, Goodsell DS, Olson AJ, Sanner MF. AutoDockFR: advances in protein-ligand docking with explicitly specified binding site flexibility. PLoS Comput Biol. 2015;11(12):e1004586.

[55] Lipinski CA, Lombardo F, Dominy BW, Feeney PJ. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv Drug Deliv Rev. 1997;23(1–3):3–25.

[56] Pires DE, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066–4072.

[57] Kuriata A, Gierut AM, Oleniecki T, et al. CABS-flex 2.0: a web server for fast simulations of flexibility of protein structures. Nucleic Acids Res. 2018;46(W1): W338–W343.

[58] Jamroz M, Kolinski A, Kmiecik S. CABS-flex: server for fast simulation of protein structure fluctuations. Nucleic Acids Res. 2013;41(W1):W427–W431.

[59] Wang Z, Pan H, Sun H, et al. fastDRH: a webserver to predict and analyze proteinligand complexes based on molecular docking and MM/PB(GB)SA computation. Briefings Bioinf. 2022;23(5):bbac201.

[60] Yu Y, Wang Z, Wang L, Tian S, Hou T, Sun H. Predicting the mutation effects of protein–ligand interactions via end-point binding free energy calculations: strategies and analyses. J Cheminf. 2022 Dec;14(1):1–3.

[61] Gramatica P, Sangion A. A historical excursus on the statistical validation parameters for QSAR models: a clarification concerning metrics and terminology. J Chem Inf Model. 2016;56(6):1127–1131.

[62] Ouabane M, Tabti K, Hajji H, et al. Structure-odor relationship in pyrazines and derivatives: a physicochemical study using 3D-QSPR, HQSPR, Monte Carlo, molecular docking, ADME-Tox and molecular dynamics. Arab J Chem. 2023;16(11):105207.

[63] Tropsha A, Golbraikh A. Predictive quantitative structure-activity relationships modeling. Handbook of chemoinformatics algorithms. 2010;33:211.

[64] Edache EI, Arthur DE, Abdulfatai U. Quantitative structure-activity relationship analysis of the anti-tyrosine activity of some tetraketone and benzyl-benzoate derivatives based on genetic algorithm-multiple linear regression. J Chem Mater Res. 2016;6(1):3–13.

[65] Ugbe FA, Shallangwa GA, Uzairu A, Abdulkadir I. In-silico design and evaluation of the anti-wolbachia potential of boron-pleuromutilins. Borneo Journal of Pharmacy. 2023 May 31;6(2):168–181.

[66] Edache EI, Uzairu A, Mamza PA, Shallangwa GA. Prediction of HemO inhibitors based on iminoguanidine using QSAR, 3DQSAR study, molecular docking, molecular dynamic simulation, and ADMET. Journal of Drug Design and Discovery Research. 2020;1(2):36–52.

[67] Lipinski CA. Poor aqueous solubility—an industry wide problem in drug discovery. Am Pharmaceut Rev. 2002;5(3):82–85.

[68] Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD. Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem. 2002;45(12):2615–2623.

[69] Zrieq R, Ahmad I, Snoussi M, et al. Tomatidine and patchouli alcohol as inhibitors of SARS-CoV-2 enzymes (3CLpro, PLpro and NSP15) by molecular docking and molecular dynamics simulations. Int J Mol Sci. 2021;22(19):10693.

[70] Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7(1):42717.

[71] Zeiger E. The test that changed the world: the Ames test and the regulation of chemicals. Mutat Res Genet Toxicol Environ Mutagen. 2019;841:43–48.

[72] Mkhayar K, Elkhattabi K, Elkhalabi R, et al. Evaluation of dimedone-derived compounds as inhibitors against human colon cancer: insights from 2D-QSAR, ADMET prediction, Osiris, Molinspiration, and molecular modeling. Chin J Anal Chem. 2023;51(11):100330.

RIGHTS & PERMISSIONS

2024 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.