Determination of species distribution and formation constants of complexes between ion Cu2+ and amino acids using multivariate regression analysis

In present work, the formation constants, logb110, logb120 and the concentration of [M] and [MLi ] in complex solutions of Cu2+ and the amino acids were determined by using the quantitative electron structure and properties relationships (QESPRs) and quantitative complex and complex relationships (QCCRs). The relative charge nets for complex structures were calculated by using molecular mechanics MM+ and semiempirical quantum chemistry calculations ZINDO/1. The QESPRs and QCCRs models were constructed by the atomic charge net on complex structures and the multivariate regression analysis. These were employed for approximate determination the formation constants logb110, logb120 and the distribution diagram of species [M], [MLi ] in various solutions. These results were compared with those from literature [[3]]. They were also validated by the statistical method ANOVA. The dissimilarities between these models and experimental data are insignificant.

Tạp chí Đại học Thủ Dầu Một, số - 2011 DETERMINATION OF SPECIES DISTRIBUTION AND FORMATION CONSTANTS OF COMPLEXES BETWEEN ION Cu2+ AND AMINO ACIDS USING MULTIVARIATE REGRESSION ANALYSIS Le Thi My Duyen(1) – Pham Van Tat (2) (1) University of Dalat – (2) University of Thu Dau Mot ABSTRACT In present work, the formation constants, logb110, logb120 and the concentration of [M] and [MLi] in complex solutions of Cu2+ and the amino acids were determined by using the quantitative electron structure and properties relationships (QESPRs) and quantitative complex and complex relationships (QCCRs) The relative charge nets for complex structures were calculated by using molecular mechanics MM+ and semiempirical quantum chemistry calculations ZINDO/1 The QESPRs and QCCRs models were constructed by the atomic charge net on complex structures and the multivariate regression analysis These were employed for approximate determination the formation constants logb110, logb120 and the distribution diagram of species [M], [MLi] in various solutions These results were compared with those from literature [[3]] They were also validated by the statistical method ANOVA The dissimilarities between these models and experimental data are insignificant Keyworks: formation constants, semiempirical quantum chemistry calculations ZINDO/1, multivariate regression analysis, quantitative complex and complex relationships * INTRODUCTION In recent years computer is becoming a helpful tool, an effective means of strong calculation in many different areas It is used in the inorganic chemistry, analytical chemistry, organic chemistry, physical chemistry, material simulation and data mining [[1],[2]] The multivariate analysis methods are becoming a convenient and an easy tool for building empirical and theoretical models The linear correlation relationships can be assessed from different characteristics of the system Formation constants of complexes are one of the most important factors to explain reaction mechanisms, chemical properties of biological systems in nature From the formation constants we can calculate the equilibrium concentration of components in a solution These can forecast the changes of complex electronic structure in solution from the initial concentration of the central ion and ligand In recent years the formation constants of the complexes can be determined by experimental ways using UV-Vis spectral data [[7]] and computational techniques The theoretical methods used for predicting stability constants of complexes based on the relationship between structural and topological descriptors were introduced [[8]] A few topological descriptors of complexes Cu2+ with amino acids were determined by molecular mechanics methods [[4],[5],[6]] In this work, the linear relationship between topological parameters and formation constants of the complexes is not done We focused only on constructing the quantitative electron structure and 57 Journal of Thu Dau Mot university, No1 - 2011 properties relationships (QESPRs) from the atomic charge nets and formation constants of complexes Cu2+ with amino acids These linear models were carried out by using principal component analysis The atomic charges are calculated using the semiempirical quantum chemical method ZINDO/1 SCF MO We also reported the quantitative complex and complex relationships (QCCRs) using the atomic charges The formation constants logb110 and logb120 of complexes Cu2+ and amino acids were predicted from these linear models Those were also compared to predictive ability of artificial neural networks The distribution diagram of ions in complex solution was built upon the predicted values of logb110 and logb120 All the results were also compared with experimental data from literature METHODS 2.1 Reaction equations In aqueous solution, amino acid dissociates into anion L2- then reacts with metal ion Cu2+: kCu 2+ + lL2− + mH + = [Cu k Ll H m ] (1) Ions Cu2+ participate in reactions with L2- ligands to form complexes [CukLlHm]: b klm =ng from PCR model and ANN I(5)-HL(2)-O(2) are not different 61 Journal of Thu Dau Mot university, No1 - 2011 3.2 Constructing models QCCRs Besides the regression constructing technique and artificial neural network based on the atomic charge distribution of the complex, in this work we also built the regression models using the complex structure relationships, as illustrated in following equation (6): m Com-i = ∑ b j Com-j + b with m = - (6) j =1 where Com-i and Com-j are target complex i and predicted complexes j; bj is the parameter for complex j; b0 is the constant The QCCRs models are constructed by the ordinary regression techniques Each complex in Table was selected as a target complex, and independent variables were chosen from remaining compounds The atomic net charge of complexes in Table are used to establish the regression models using forward and elimination technique The best models were found by this technique The selected complex models QCCRs consist of the predicted complexes with the similar structural properties Table The quantitative complex and complex relationships, and regression-statistical values Statistical values, predictive complex Com-1 Com-2 Com-3 Target complex Com-4 Com-5 Com-6 Com-7 Com-8 R2-training 99.999 100.000 99.994 99.537 99.978 99.996 99.883 99.996 R2 -adjusted Standard error, SE 99.998 0.002 99.999 0.001 99.992 0.003 99.486 0.022 99.973 0.005 99.995 0.002 99.833 0.013 99.994 0.002 R2 -prediction Constant Com-1 Com-2 Com-3 Com-4 99.995 -0.001 1.859 -0.910 -0.052 99.999 0.001 0.536 0.490 0.029 99.986 0.001 0.952 - 99.275 -0.004 - 99.956 -0.002 -1.381 2.399 - 99.991 -0.001 0.278 0.727 - 99.758 0.006 4.895 -8.563 4.649 - 99.992 0.001 0.557 0.105 - - - 0.344 - Com-5 Com-6 Com-7 Com-8 0.757 0.108 -0.057 - -0.706 0.974 The complex model (7) for the Com-1 complex is shown in Com-1 = -0.001 + 1.859(Com-2) – 0.910(Com-3) – 0.052(Com-4) + 0.108(Com-7) (7) The regression models between different complex structures with their statistical values depict the regression quality, shown in Table All R2-training and R2-prediction values are larger than 99% from the standard statistical values The complex structural models QCCRs were used to estimate the target complex properties using features of predicted complexes in the regression model In this work we used the formation constants of the complexes Cu2+ with amino acids, as a important properties for calculating the stability constant of target complex in the respective models The predicted results for logβ120 and logβ110 were validated by the values ARE% for the models, are given in Table 62 Tạp chí Đại học Thủ Dầu Một, số - 2011 Table The predicted formation constants by complex models QCCRs with values ARE,% Ref.[[3]] logβ110 8.380 7.940 7.300 7.340 6.880 7.250 7.320 6.700 Complex Com-1 Com-2 Com-3 Com-4 Com-5 Com-6 Com-7 Com-8 Models QCCRs logβ110 logβ120 8.524 15.535 7.861 14.675 7.822 14.482 6.523 12.125 7.475 13.321 8.104 14.976 6.978 14.973 7.804 14.589 logβ120 15.700 14.590 13.560 13.550 12.860 13.310 13.520 12.450 ARE,% logβ110 1.717 0.997 7.150 11.131 8.650 11.777 4.669 16.483 logβ120 1.050 0.583 6.796 10.520 3.582 12.515 10.746 17.182 The absolute values of relative errors ARE% are calculated by ARE,% = logb k ,l ,m −exp - logb k ,l ,m −cal logb k ,l ,m −exp 100 (8) Where logβk,l,m-exp and logβk,l,m-cal are the experimental and calculated formation constants From the obtained results for logβ120 logβ110 in Table 7, distribution diagram of ions is illustrated for the complex Cu(Gly)2 and Cu (GlyMe)2, as is shown in Figure Cu(Gly)2 10 pL 12 14 16 1E-01 1E-01 1E-02 1E-02 1E-04 1E-05 1E-03 Cu+2 log [c] 1E-03 log [c] L-2 CuL CuL2-2 1E-04 1E-05 1E-06 1E-06 1E-07 1E-07 1E-08 1E-08 1E-09 1E-09 Cu(GlyMe)2 10 pL 12 14 16 Cu+2 L-2 CuL CuL2-2 Figure Species distribution of the complex solution Cu(Gly)2 and Cu(GlyMe)2 The logβ120 logβ110 values in Table obtained from the ordinary regression techniques are in very good agreement with the reference values [[3]] The one-way ANOVA is used to evaluate logβ110 values (F = 0.705 < F0.05 = 4.600) and logβ120 (F = 1.473 < F0.05 = 4.600), and values ARE% (F = 0.0003 < F0.05 = 4.6001) Thus, PCR model for logβ110, logβ120 and QCCRs model fitted well with those from neural network I(5)-HL(2)-O(2) and literature [[3]] CONCLUSION This work has successfully built the quantitative electron structure and properties (QESPRs) and the quantitative complex and complex relationships (QCCRs) from complexes Cu2+ and amino acids using the atomic charge net The formation constant values and values ARE% were assessed by ANOVA 63 Journal of Thu Dau Mot university, No1 - 2011 Determination of formation constants of complexes Cu2+ and amino acids is one important direction to understand and to explain many biological properties This research can be applied in different ways as a potential method to quickly determine the formation constants of complexes between metal and amino acids combining theory and experimental way The ion H+ affects for complex formation, this will be carried out by next work * XÁC ĐỊNH PHÂN BỐ CÁC CẤU TỬ VÀ HẰNG SỐ TẠO THÀNH CỦA CÁC PHỨC GIỮA ION Cu2+ VÀ CÁC AXIT AMINO SỬ DỤNG PHƯƠNG PHÁP PHÂN TÍCH HỒI QUY ĐA BIẾN Lê Thị Mỹ Duyên(1) – Phạm Văn Tất(2) (1) Trường Đại học Đà Lạt - (2) Trường Đại học Thủ Dầu Một TĨM TẮT Trong cơng trình này, số tạo thành logb110, logb120 nồng độ [M] [MLi] dung dịch phức Cu2+ với acid amino xác định mối quan hệ định lượng cấu trúc điện tử tính chất (QESPRs) quan hệ định lượng phức chất phức chất (QCCRs) Mạng lưới điện tích tương đối cấu trúc phức tính tốn học phân tử MM+ hóa lượng tử bán kinh nghiệm ZINDO/1 Các mơ hình QESPRs QCCRs xây dựng mạng điện tích nguyên tử phức chất phân tích hồi quy đa biến số Những mơ hình dùng để xác định gần số tạo thành logb110, logb120 giản đồ phân bố cấu tử [M] [MLi] dung dịch Các kết so sánh với giá trị thực nghiệm tham khảo[[3]] đánh giá phương pháp thống kê ANOVA Sự khác phương pháp lý thuyết liệu thực nghiệm tham khảo ý nghĩa Từ khóa: số tạo thành, tính tốn lượng tử bán thực nghiệm ZINDO/1, phân tích hồi quy, quan hệ phức chất phức chất REFERENCES [1] E J Billo., Excel For Scientists And Engineers-Numerical Methods., Wiley, 2007 [2] D Harvey, Modern analytical Chemistry, Mc.Graw Hill, Boston, Toronto, 2000 [3] B Grgas, S Nikolic, N Paulic, and N Raos., Croatica Chemica Acta, 72, 885-895, 1999 [4] A Milicevic and N Raos., Acta Chim Slov, 56, 373-378, 2009 [5] M Ante and N Raos., Croatica Chemica Acta, 79, 281-290, 2006 [6] S Nikolic and N Raos., Croatica Chemica Acta, 74, 621-631, 2001 [7] N Raos., Croatica Chemica Acta, 75, 117-120, 2002 [8] Pham Van Tat., Development of QSAR and QSPR, Publisher of Natural sciences and Technique, HaNoi, 2009 [9] Ha Tan Loc, Pham Van Tat., J Analytical Sciences, Vol 15, No 4, 2010 [10] D D J.Werner, P R.Yeater, Essential Regression and Experimental Design for Chemists and Engineer, 2000 [11] MINITAB v 14 for Windows, Minitab Inc, Ltd, 2010 [12] HyperChem Release 7.5 for Windows, Hypercube Inc Getting Started., USA, 2002 [13] INForm v2.0, Intelligensys Ltd., UK, 2002 64 ... Thu Dau Mot university, No1 - 2011 Determination of formation constants of complexes Cu2+ and amino acids is one important direction to understand and to explain many biological properties This... method to quickly determine the formation constants of complexes between metal and amino acids combining theory and experimental way The ion H+ affects for complex formation, this will be carried out... quantitative complex and complex relationships (QCCRs) using the atomic charges The formation constants logb110 and logb120 of complexes Cu2+ and amino acids were predicted from these linear