Nghiên cứu QSAR về các chất đối kháng Nav1.7 bằng phương pháp hồi quy tuyến tính đa biến dựa trên thuật toán di truyền (GA–MLR)

Springer Science and Business Media LLC - Tập 23 - Trang 2264-2276 - 2013
Eslam Pourbasheer1, Reza Aalizadeh1, Mohammad Reza Ganjali2,3, Parviz Norouzi2,3, Javad Shadmanesh4, Constantinos Methenitis4
1Young Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, Iran
2Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, Tehran, Iran
3Biosensor Research Center, Endocrinology and Metabolism Research Center, Tehran University of Medical Sciences, Tehran, Iran
4Department of Chemistry, University of Athens, Athens Greece

Tóm tắt

Trong công trình này, một nghiên cứu về mối quan hệ cấu trúc-hoạt tính định lượng (QSAR) đã được phát triển để dự đoán hoạt tính đối kháng NaV1.7. Một bộ dữ liệu bao gồm 36 hợp chất với hoạt tính đối kháng NaV1.7 đã được chia thành hai tập con là tập huấn luyện và tập kiểm tra bằng cách sử dụng kỹ thuật phân cụm phân cấp. Để lựa chọn các đặc trưng phù hợp nhất từ nhóm đặc trưng, thuật toán di truyền đã được áp dụng. Mô hình dựa trên các đặc trưng đã được chọn thông qua thuật toán di truyền (GA) được xây dựng bằng phương pháp hồi quy tuyến tính đa biến (MLR). Hệ số tương quan bình phương (\( R_{\text{train}}^{2} \)) là 0.813, hệ số tương quan bình phương qua kiểm tra chéo cho phương pháp leave-one-out (\( Q_{\text{LOO}}^{2} \)) là 0.699 và sai số bình phương trung bình là 0.214 đã được tính cho các hợp chất trong tập huấn luyện bằng mô hình GA–MLR. Mô hình được đề xuất cho thấy khả năng dự đoán tốt khi được xác minh qua các bài kiểm tra xác thực nội bộ và bên ngoài. Kết quả của mô hình dự đoán có thể dẫn đến việc thiết kế các hợp chất tốt hơn với hoạt tính đối kháng NaV1.7 cao.

Từ khóa

#NaV1.7 #đối kháng #hoạt tính #nghiên cứu QSAR #thuật toán di truyền #hồi quy tuyến tính đa biến

Tài liệu tham khảo

Agrawal VK, Khadikar PV (2001) QSAR prediction of toxicity of nitrobenzenes. Bioorg Med Chem 9:3035–3040. doi:10.1016/S0968-0896(01)00211-5 Beheshti A, Pourbasheer E, Nekoei M, Vahdani S, (2012) QSAR modeling of antimalarial activity of urea derivatives using genetic algorithm–multiple linear regressions. J Saudi Chem Soc. doi:10.1016/j.jscs.2012.07.019 Bregman H, Nguyen HN, Feric E, Ligutti J, Liu D, McDermott JS, Wilenkin B, Zou A, Huang L, Li X, McDonough SI, DiMauro EF (2012) The discovery of aminopyrazines as novel, potent Nav1.7 antagonists: hit-to-lead identification and SAR. Bioorg Med Chem Lett 22:2033–2042. doi:10.1016/j.bmcl.2012.01.023 Catterall WA, Dib-Hajj S, Meisler MH, Pietrobon D (2008) Inherited neuronal ion channelopathies: new windows on complex neurological diseases. J Neurosci 28:11768–11777. doi:10.1523/jneurosci.3901-08.2008 Consonni V, Todeschini R, Pavan M (2002) Structure/response correlations and similarity/diversity analysis by GETAWAY descriptors. 1. Theory of the novel 3D molecular descriptors. J Chem Inf Comput Sci 42:682–692. doi:10.1021/ci015504a Dib-Hajj SD, Tyrrell L, Black JA, Waxman SG (1998) NaN, a novel voltage-gated Na channel, is expressed preferentially in peripheral sensory neurons and down-regulated after axotomy. Proc Nat Acad Sci 95:8963–8968 Djouhri L, Newton R, Levinson SR, Berry CM, Carruthers B, Lawson SN (2003) Sensory and electrophysiological properties of guinea-pig sensory neurones expressing Nav 1.7 (PN1) Na+ channel α subunit protein. J Physiol 546:565–576. doi:10.1113/jphysiol.2002.026559 Dubes R, Jain AK (1976) Clustering techniques: The user’s dilemma. Patt Recogn 8:247–260. doi:10.1016/0031-3203(76)90045-5 Eriksson L, Johansson E, Müller M, Wold S (2000) On the selection of the training set in environmental QSAR analysis when compounds are clustered. J Chemomet 14:599–616 Farrar JT, Portenoy RK, Berlin JA, Kinman JL, Strom BL (2000) Defining the clinically important difference in pain outcome measures. Pain 88:287–294 Felts PA, Yokoyama S, Dib-Hajj S, Black JA, Waxman SG (1997) Sodium channel α-subunit mRNAs I, II, III, NaG, Na6 and hNE (PN1): different expression patterns in developing rat nervous system. Mol Br Res 45:71–82. doi:10.1016/S0169-328X(96)00241-0 Fischer TZ, Waxman SG (2010) Familial pain syndromes from mutations of the Nav1.7 sodium channel. Anna New York Acad Sci 1184:196–207 Fischer TZ, Gilmore ES, Estacion M, Eastman E, Taylor S, Melanson M, Dib-Hajj SD, Waxman SG (2009) A novel Nav1.7 mutation producing carbamazepine-responsive erythromelalgia. Ann Neurol 65:733–741 George AL (2005) Inherited disorders of voltage-gated sodium channels. JCI 115:1990–1999 Goldberg YP, MacFarlane J, MacDonald ML, Thompson J, Dube MP, Mattice M, Fraser R, Young C, Hossain S, Pape T, Payne B, Radomski C, Donaldson G, Ives E, Cox J, Younghusband HB, Green R, Duff A, Boltshauser E, Grinspan GA, Dimon JH, Sibley BG, Andria G, Toscano E, Kerdraon J, Bowsher D, Pimstone SN, Samuels ME, Sherrington R, Hayden MR (2007) Loss-of-function mutations in the Nav1.7 gene underlie congenital indifference to pain in multiple human populations. Clin Genet 71:311–319 Habibi-Yangjeh A, Pourbasheer E, Danandeh-Jenagharad M (2008) Prediction of basicity constants of various pyridines in aqueous solution using a principal component-genetic algorithm-artificial neural network. Monatsh Chem 139:1423–1431 Habibi-Yangjeh A, Pourbasheer E, Danandeh-Jenagharad M (2009) Application of principal component-genetic algorithm-artificial neural network for prediction acidity constant of various nitrogen-containing compounds in water. Monatsh Chem 140:15–27 Hall LH, Kier LB (1995) Electrotopological state indices for atom types: a Novel combination of electronic, topological, and valence state information. J Chem Inf Comput Sci 35:1039–1045 Hansch C, Fujita T (1964) p-σ-π analysis. A method for the correlation of biological activity and chemical structure. J Am Chem Soc 86:1616–1626 Hocking RR (1976) A biometrics invited paper. The analysis and selection of variables in linear regression. Biometrics 32:1–49 Holland JH (1975) Adaptation in natural and artificial systems. University of Michigan Press, Michigan Holm AN, Rich A, Miller SM, Strege P, Ou Y, Gibbons SJ, Sarr MG, Szurszewski JH, Rae JL, Farrugia G (2002) Sodium current in human jejunal circular smooth muscle cells. Gastroenterology 122:178–187 HyperChem (2002) Molecular modeling system. Hypercube Inc., Gainesville Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:264–323 Khajehsharifi H, Sadeghi M, Pourbasheer E (2009) Spectrophotometric simultaneous determination of creatine, creatinine, and uric acid in real samples by orthogonal signal correction–partial least squares regression. Monatsh Chem 140:685–691 Leardi R, Boggia R, Terrile M (1992) Genetic algorithms as a strategy for feature selection. J Chemomet 6:267–281 Li W, Tang Y, Zheng Y-L, Qiu Z-B (2006) Molecular modeling and 3D-QSAR studies of indolomorphinan derivatives as kappa opioid antagonists. Bioorg Med Chem 14:601–610 Mathworks (2005) Genetic algorithm and direct search toolbox users guide. The Mathworks Inc., USA Nassar MA, Stirling LC, Forlani G, Baker MD, Matthews EA, Dickenson AH, Wood JN (2004) Nociceptor-specific gene deletion reveals a major role for Nav1.7 (PN1) in acute and inflammatory pain. Proc Nat Acad Sci USA 101:12706–12711 Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2009) Application of genetic algorithm-support vector machine (GA-SVM) for prediction of BK-channels activity. Eur J Med Chem 44:5023–5028 Pourbasheer E, Riahi S, Ganjali MR, Norouzi P (2010) QSAR study on melanocortin-4 receptors by support vector machine. Eur J Med Chem 45:1087–1093 Pourbasheer E, Beheshti A, Khajehsharifi H, Ganjali MR, Norouzi P (2012) QSAR study on hERG inhibitory effect of kappa opioid receptor antagonists by linear and non-linear methods. Med Chem Res. doi:10.1007/s00044-012-0412-4 Pourbasheer E, Aalizadeh R, Ganjali M, Norouzi P (2013) QSAR study of IKKβ inhibitors by the genetic algorithm: multiple linear regressions. Med Chem Res. doi:10.1007/s00044-013-0611-7 Riahi S, Ganjali M, Pourbasheer E, Norouzi P (2008) QSRR Study of GC retention indices of essential-oil compounds by multiple linear regression with a genetic algorithm. Chroma 67:917–922 Rush AM, Dib-Hajj SD, Liu S, Cummins TR, Black JA, Waxman SG (2006) A single sodium channel mutation produces hyper- or hypoexcitability in different types of neurons. Proc Nat Acad Sci 103:8245–8250 Saleh S, Yeung SYM, Prestwich S, Pucovský V, Greenwood I (2005) Electrophysiological and molecular identification of voltage-gated sodium channels in murine vascular myocytes. J Physiol 568:155–169 Shen Q, Lü Q-Z, Jiang J-H, Shen G-L, Yu R-Q (2003) Quantitative structure–activity relationships (QSAR): studies of inhibitors of tyrosine kinase. Eur J Pharm Sci 20:63–71 Timmerman H (1995) New developments and applications: QSAR and drug design. In: Fujita T (ed) Pharmacochemistry Library. Elsevier, Amsterdam, pp 413–450 Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley, Weinheim Todeschini R, Bettiol C, Giurin G, Gramatica P, Miana P, Argese E (1996) Modeling and prediction by using WHIM descriptors in QSAR studies: submitochondrial particles (SMP) as toxicity biosensors of chlorophenols. Chemosphere 33:71–79 Todeschini R, Consonni V, Mauri A, Pavan M (2005) DRAGON software for the calculation of molecular descriptors, 53 edn. Talete srl, Milan Toledo-Aral JJ, Moss BL, He Z-J, Koszowski AG, Whisenand T, Levinson SR, Wolf JJ, Silos-Santiago I, Halegoua S, Mandel G (1997) Identification of PN1, a predominant voltage-dependent sodium channel expressed principally in peripheral neurons. Proc Nat Acad Sci 94:1527–1532 Tropsha A, Gramatica P, Gombar VK (2003) The importance of being earnest: validation is the absolute essential for successful application and interpretation of QSPR models. QSAR Comb Sci 22:69–77 Vapnik V (1998) Statistical learning theory. Wiley, New York Waxman SG (2007) Nav1.7, its mutations, and the syndromes that they cause. Neurology 69:505–507 Zhou YX, Xu L, Wu YP, Liu BL (1999) A QSAR study of the antiallergic activities of substituted benzamides and their structures. Chemometr Intell Lab 45:95–100