مطالعه کمّی فعالیت- ساختار، داکینگ و شبیه‌سازی دینامیک مولکولی تعدادی از ترکیبات هتروسیکل شامل نیتروژن اکسید به عنوان عامل‌های ضد سل جدید
محورهای موضوعی :
شیمی کاربردی
قاسم قاسمی
1
,
بابک مطهری
2
,
ربابه صیادی کردآبادی
3
1 - استادیار، گروه شیمی و مهندسی شیمی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
2 - دانشجوی دکتری، گروه مهندسی کامپیوتر، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
3 - استادیار، گروه شیمی و مهندسی شیمی، واحد رشت، دانشگاه آزاد اسلامی، رشت، ایران
تاریخ دریافت : 1401/11/09
تاریخ پذیرش : 1402/01/17
تاریخ انتشار : 1401/10/01
کلید واژه:
شبیه‌سازی دینامیک مولکولی,
QSAR,
داکینگ,
هتروسیکل,
سل,
نیتروژن اکسید,
چکیده مقاله :
هدف: عامل سل، مایکوباکتریوم توبرکلوزیس است. یک‌سری از ترکیبات جدید هتروسیکل شامل نیتروژن اکسید، به عنوان بازدارنده‌های مایکوباکتریوم توبرکلوزیس گزارش شده‌اند. در این راستا، هدف پژوهش حاضر مطالعه QSAR، داکینگ و شبیه‌سازی دینامیک مولکولی تعدادی از ترکیبات هتروسیکل شامل نیتروژن اکسید به عنوان عامل‌های ضد سل جدید است.مواد و روش‌ها: الگوریتم رقابت استعماری، PLS، PCR، LASSO و شبیه‌سازی مونته کارلو در محاسبات QSAR انجام گردید. همچنین محاسبات داکینگ و شبیه‌سازی دینامیک مولکولی میتوباکتریوم توبرکلوزیس با کد 4XGQ انجام شده است.یافته‌ها: جرم‌های اتمی، الکترونگانیویتی اتمی ساندرسون، شاخص Ghose–Viswanadhan-Wendoloski hyptonic-like و شاخص Ghose–Viswanadhan-Wendoloski antiinfective-like در این بررسی مهم بودند. در برنامه کورال سی، از فایل اسمایل استفاده شد. RMSE برای آموزش و تست در الگوریتم رقابت استعماری، به ترتیب 1395/0 و 2970/0 بودند. در روش مونته کارلو نتایج برای سری آموزش به‌صورت 7n=، 9931/0R²=، 9857/0 Q²=و 039/0 MAE=و برای سری تست به‌صورت 6n=، 9413/0R²=، 9107/0Q²=و 367/0 MAE=بدست آمد.نتیجه‌گیری: مولکول‌های 10 و 11 ترکیبات پایداری هستند که برای بررسی‌های بیشتر از جمله مطالعات آزمایشگاهی کلینیکی پیشنهاد می‌شوند.
چکیده انگلیسی:
Objectiv: Mycobacterium tuberculosis (Mtb), is agent of tuberculosis. A series of novel N‑Oxide-Containing Heterocycles have been reported as selective Mycobacterium tuberculosis inhibitors. QSAR, Docking, and Molecular Dynamics Simulation studies were investigated.Materials and Methods: Imperialist Competitive Algorithm (ICA), Partial least squares (PLS), Principle Component Regression (PCR), Least Absolute Shrinkage, Selection Operator (LASSO), and Monte-Carlo simulation were used to create QSAR models. The molecular docking and molecular dynamics simulation were carried out on Mycobacterium tuberculosis (Mtb) strain H37Rv (PDB: 4XGQ).Findings: Atomic masses, atomic sanderson electronegativities, Ghose–Viswanadhan-Wendoloski antiinfective-like index and Ghose –Viswanadhan-Wendoloski hyptonic-like index were important in our study. The SMILES files have been used with coralsea software. The root-mean square errors of the training set, and the test set for ICA model, were 0.2970, 0.1395 respectively. The results of the Monte-Carlo method were the following: n=7, R²=0.9931, Q²=0.9857, MAE=0.039 (Training set); n=6, R²=0.9413, Q²=0.9107, MAE=0.367 (Test set).Conclusion: Molecules 10 and 11 were presented as the most stable ones that may be introduced for further investigations, including clinical experiments.
منابع و مأخذ:
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Ravichandran V, Shalini S, Sokkalingam AD, Harish R & Suresh K. QSAR study of 7chloroquinoline derivatives as antitubercular agents. World J Pharm Pharmaceut Sci. 2014; 3: 1072–82.
Priyadarsini R, Tharani CB, Suganya S & Kavitha S. Pharmacophore modeling and 3DQSAR studies on substituted benzothiazole/benzimidazole analogues as DHFR inhibitors with antimycobacterial activity. J. Pharma. Sci. Res. 2012; 3: 441-450.
Adeniji SE, Uba S & Uzairu A. Theoretical modeling and molecular docking simulation for investigating and evaluating some active compounds as potent antitubercular agents against MTB CYP121 receptor. Future J. Pharm. Sci. 2018; 4: 284–95.
Yuanita E, Komang N, Dharmayani T, Ulfa M & Syahri J. Quantitative structure–activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents. Clin. Tuberc. Other Mycobact. Dis. 2020; 21: 100203.
Toropov AA, Toropova AP, Diaza RG, Benfenati E & Gini G. SMILES- based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation Struct. Chem. 2012; 23(2): 529-544.
Guilherme Felipe dos Santos, F & Paula Carolina de Souza E. Design, Synthesis, and Characterization of N‑Oxide-Containing Heterocycles with in Vivo Sterilizing Antitubercular Activity. Med. Chem. 2017; 60: 8647-8660.
Meng XY, Zhang HX, Mezei M & Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Comput. Aided Drug Des. 2011; 7: 146-157.
Ferreira LG, Dos Santos RN, Oliva G & Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015; 2: 13384-13421.
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Orme IM. Vaccines to prevent tuberculosis infection rather than disease: physiological and immunological aspects. Tuberculosis. 2016; 101: 210-216.
Dharmadhikari AS, Basaraba RJ & Van Der Walt ML. Natural infection of guinea pigs exposed to patients with highly drug-resistant tuberculosis. Tuberculosis. 2011; 91:
329-338.
Fernandes GFS, Jornada DH, Souza PC, Man Chin C, Pavan FR & Santos JL, Current Advances in Antitubercular Drug Discovery: Potent Prototypes and New Targets. Med. Chem. 2015; 22: 3133–3161.
Bloemberg GV, Keller PM, Stucki D, Trauner A, Borrell S & Latshang T. Acquired Resistance to Bedaquiline and Delamanid in Therapy for Tuberculosis. Engl. J. Med. 2015; 373: 1986–1988.
Zumla A, Chakaya J, Centis R, D’Ambrosio L, Mwaba P & Bates M. Tuberculosis Treatment and Management, an Update on Treatment Regimens, Trials, New Drugs, and Adjunct Therapies. Lancet Respir. Med. 2015; 3(3): 220−
Ma Z, Lienhardt C, McIlleron H, Nunn AJ & Wang X. Global Tuberculosis Drug Development Pipeline: The Need and the Reality. Lancet. 2010; 375(9731): 2100−
Global Tuberculosis Report 2014 (WHO/HTM/TB/2014.08). World Health Organization: Geneva, Switzerland, 2014.
Frydenberg AR & Graham SM. Toxicity of First-Line Drugs for Treatment of Tuberculosis in Children: Review. Med. Int. Health. 2009; 14(11): 1329−1337.
Gasco A, Fruttero R, Sorba G, Di Stilo A & Calvino R. NO Donors: Focus on Furoxan Derivatives. Pure Appl. Chem. 2004; 76(5): 973−
Castro D, Boiani L, Benitez D, Hernandez P, Merlino A & Gil C. Anti- Trypanosomatid Benzofuroxans and Deoxygenated Analogues: Synthesis Using Polymer-Supported Triphenylphosphine, Biological Evaluation and Mechanism of Action Studies. J. Med. Chem, 2009; 44(12): 5055−5065.
Olea-Azar C, Rigol C, Mendizabal F, Cerecetto H, Di Maio R & Gonzalez M. Novel Benzo[1,2-c]1,2,5-Oxadiazole N-Oxide Derivatives as Antichagasic Agents: Chemical and Biological Studies, Drug Des. Discovery. 2005; 2: 294−301.
Saxena AK & Singh A. Mycobacterial tuberculosis Enzyme Targets and their Curr. Top. Med. Chem. 2019; 19: 337–355.
Ravichandran V, Shalini S, Sokkalingam AD, Harish R & Suresh K. QSAR study of 7chloroquinoline derivatives as antitubercular agents. World J Pharm Pharmaceut Sci. 2014; 3: 1072–82.
Priyadarsini R, Tharani CB, Suganya S & Kavitha S. Pharmacophore modeling and 3DQSAR studies on substituted benzothiazole/benzimidazole analogues as DHFR inhibitors with antimycobacterial activity. J. Pharma. Sci. Res. 2012; 3: 441-450.
Adeniji SE, Uba S & Uzairu A. Theoretical modeling and molecular docking simulation for investigating and evaluating some active compounds as potent antitubercular agents against MTB CYP121 receptor. Future J. Pharm. Sci. 2018; 4: 284–95.
Yuanita E, Komang N, Dharmayani T, Ulfa M & Syahri J. Quantitative structure–activity relationship (QSAR) and molecular docking of xanthone derivatives as anti-tuberculosis agents. Clin. Tuberc. Other Mycobact. Dis. 2020; 21: 100203.
Toropov AA, Toropova AP, Diaza RG, Benfenati E & Gini G. SMILES- based optimal descriptors: QSAR modeling of estrogen receptor binding affinity by correlation Struct. Chem. 2012; 23(2): 529-544.
Guilherme Felipe dos Santos, F & Paula Carolina de Souza E. Design, Synthesis, and Characterization of N‑Oxide-Containing Heterocycles with in Vivo Sterilizing Antitubercular Activity. Med. Chem. 2017; 60: 8647-8660.
Meng XY, Zhang HX, Mezei M & Cui M. Molecular docking: a powerful approach for structure-based drug discovery. Comput. Aided Drug Des. 2011; 7: 146-157.
Ferreira LG, Dos Santos RN, Oliva G & Andricopulo AD. Molecular docking and structure-based drug design strategies. Molecules. 2015; 2: 13384-13421.
