锌螯合肽的两端排序法定量构效关系
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摘要
为了研究锌螯合肽结构与生物活性之间的关系,构建定量构效关系(quantitative structure-activity relationship,QSAR)模型。采取两端排序法将56条不同长度的合成肽规格化,采用18种氨基酸描述符,利用偏最小二乘法进行分析。发现5种氨基酸描述符对应的QSAR模型的相关系数达到建模要求,分别为描述符FASGAI、Z、HESH、C和ST,其中描述符FASGAI最优(R~2=0.827 3、Q~2=0.602 2、估计均方根误差=0.168 6、Q~2_(ext)=0.717 2、预测方根误差=0.255 8)。对描述符FASGAI所构建的模型进一步分析发现,多肽序列中的氨基酸位置对多肽锌螯合活性的影响力依次为C3>N3>C1>N1>N2>C2,同时,多肽各位置上氨基酸残基的立体属性会影响其螯合活性。该模型的成功建立为锌螯合肽定量构效关系的研究提供了探索性思路。
In order to explore the relationship between the structure and bioactivity of zinc-chelating peptides, quantitative structure-activity relationship(QSAR) models were established. A new method called two-terminal position numbering was proposed to describe the structures of 56 synthetic zinc-chelating peptides with different lengths. Then, these peptides were statistically analyzed using 18 amino acid descriptors and partial least squares regression. Results showed that the correlation coefficients of the QSAR models based on 5 amino acids descriptors FASGAI, Z, HESH, C and ST met the requirements. FASGAI was found to be the best among these descriptors(R~2 = 0.827 3, Q~2 = 0.602 2, root mean square error of estimation = 0.168 6, Q~2 _(ext)= 0.717 2, and root mean square error of prediction = 0.255 8). Further analysis of the FASGAIbased model revealed that the influence of amino acid positions in the peptides on their zinc-chelating activity was C3 > N3 > C1 > N1 > N2 > C2. Meanwhile, the bulky properties of amino acid residues influenced the zinc-chelating activity of the peptides. These models have the potential to provide new ideas to explore the quantitative structure-activity relationships of zinc-chelating peptides.
In order to explore the relationship between the structure and bioactivity of zinc-chelating peptides, quantitative structure-activity relationship(QSAR) models were established. A new method called two-terminal position numbering was proposed to describe the structures of 56 synthetic zinc-chelating peptides with different lengths. Then, these peptides were statistically analyzed using 18 amino acid descriptors and partial least squares regression. Results showed that the correlation coefficients of the QSAR models based on 5 amino acids descriptors FASGAI, Z, HESH, C and ST met the requirements. FASGAI was found to be the best among these descriptors(R~2 = 0.827 3, Q~2 = 0.602 2, root mean square error of estimation = 0.168 6, Q~2 _(ext)= 0.717 2, and root mean square error of prediction = 0.255 8). Further analysis of the FASGAIbased model revealed that the influence of amino acid positions in the peptides on their zinc-chelating activity was C3 > N3 > C1 > N1 > N2 > C2. Meanwhile, the bulky properties of amino acid residues influenced the zinc-chelating activity of the peptides. These models have the potential to provide new ideas to explore the quantitative structure-activity relationships of zinc-chelating peptides.
引文
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[20]梁桂兆,周鹏,周原,等.一组新氨基酸描述子用于肽定量构效关系研究[J].化学学报, 2006(5):393-396.
[21]LIANG G, LI Z. Factor analysis scale of generalized amino acid information as the source of a new set of descriptors for elucidating the structure and activity relationships of cationic antimicrobial peptides[J]. QSAR&Combinatorial Science, 2007, 26(3):754-763.DOI:10.1002/qsar.200630145.
[22]YANG L, SHU M, MA K, et al. ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues[J].Amino Acids, 2010, 38(2):805-816. DOI:10.1007/s00726-009-0287-y.
[23]TONG J, LIU S, ZHOU P, et al. A novel descriptor of amino acids and its application in peptide QSAR[J]. Journal of Theoretical Biology,2008, 253(3):90-97. DOI:10.1016/j.jtbi.2008.02.030.
[24]SNEATH P H. Relations between chemical structure and biological activity in peptides[J]. Journal of Theoretical Biology, 1966, 12(2):157-195. DOI:10.1016/0022-5193(66)90112-3.
[25]COLLANTES E R, DUNN W J. Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogs[J]. Journal of Medicinal Chemistry, 1995, 38:2705-2713.
[26]BRAVI G, GANCIA E, MASCAGNI P, et al. MS-WHIM, new 3D theoretical descriptors derived from molecular surface proper-ties:a comparative 3D QSAR study in a series of steroids[J]. Journal of Computer-Aided Molecular Design, 1997, 11:79-92. DOI:10.1023/A:1008079512289.
[27]苏满秀,王立峰,代志军,等.多肽一级结构表征与抗菌肽QSAM建模[J].高等学校化学学报, 2012, 33(11):2526-2531.
[28]汪婵,陈敏,李博.芝麻蛋白制备金属螯合肽的酶解工艺研究[J].食品科技, 2011, 36(9):184-189.
[29]TONG J B, ZHANG S W. A new 3D-descriptor of amino acid and its application in quantitative structure activity relationship of peptide drugs[J]. Acta Physico-chimica Sinica, 2007, 23(1):37-43.DOI:10.3866/PKU.WHXB20070108.
[30]丁俊杰,丁晓琴,赵立峰,等.新型三维氨基酸结构描述符的研究及其在多肽QSAR中的应用[J].药学学报, 2005, 40(4):340-346.
[31]LIN Z, LONG H, BO Z, et al. New descriptors of amino acids and their application to peptide QSAR study[J]. Peptides, 2008, 29(10):1798-1805. DOI:10.1016/j.peptides.2008.06.004.
[32]陈季旺,刘珊珊,蔡广霞,等.食物源血管紧张素转化酶抑制三肽的定量构效关系[J].食品科学, 2013, 34(9):19-23. DOI:10.7506/spkx1002-6630-201309005.
[2]DENG S G, HUO J C, XIE C. Preparation by enzymolysis and bioactivity of iron complex of fish protein hydrolysate(Fe-FPH)from low value fish[J]. Chinese Journal of Oceanology and Limnology,2008, 26(3):300-306. DOI:10.1007/s00343-008-0300-4.
[3]LüY, LIU Q, BAO X, et al. Identification and characteristics of ironchelating peptides from soybean protein hydrolysates using IMACFe3+[J]. Journal of Agricultural and Food Chemistry, 2009, 57(11):4593-4597. DOI:10.1021/jf9000204.
[4]CARRASCO-CASTILLAJ,HERNáNDEZ-áLVAREZ AJ,JIMéNEZ-MARTíNEZ C, et al. Antioxidant and metal chelating activities of peptide fractions from phaseolin and bean protein hydrolysates[J].FoodChemistry,2012,135(3):1789-1795.DOI:10.1016/j.foodchem.2012.06.016.
[5]WANG C, LI B, AO J. Separation and identification of zinc-chelating peptides from sesame protein hydrolysate using IMAC-Zn2+and LC-MS/MS[J].FoodChemistry,2012,134(2):1231-1238.DOI:10.1016/j.foodchem.2012.02.204.
[6]LV Y, BAO X, LIU H, et al. Purification and characterization of caclium-binding soybean protein hydrolysates by Ca2+/Fe3+immobilizedmetalaffinitychromatography(IMAC)[J].FoodChemistry,2013,141(3):1645-1650.DOI:10.1016/j.foodchem.2013.04.113.
[7]TORRES-FUENTES C, ALAIZ M, VIOQUE J. Affinity purification and characterisation of chelating peptides from chickpea protein hydrolysates[J]. Food Chemistry, 2011, 129(2):485-490. DOI:10.1016/j.foodchem.2011.04.103.
[8]DE LA HOZ L, PONEZI A N, MILANI R F, et al. Iron-binding properties of sugar cane yeast peptides[J]. Food Chemistry, 2014, 142:166-169. DOI:10.1016/j.foodchem.2013.06.133.
[9]LI Y W, LI B. Characterization of structure-antioxidant activity relationship of peptides in free radical systems using QSAR models:key sequence positions and their amino acid properties[J].Journal of Theoretical Biology, 2013, 318:29-43. DOI:10.1016/j.jtbi.2012.10.029.
[10]JING P, QIAN B, HE Y, et al. Screening milk-derived antihypertensive peptides using quantitative structure activity relationship(QSAR)modeling and in vitro/in vivo studies on their bioactivity[J].International Dairy Journal, 2014, 35(1):95-101. DOI:10.1016/j.idairyj.2013.10.009.
[11]卢永忠,康道乐.抗菌肽的定量构效关系研究进展[J].食品科学,2015, 36(15):299-304. DOI:10.7506/spkx1002-6630-201515055.
[12]KIDERA A, KONISHI Y, OKA M, et al. Statistical analysis of the physical properties of the 20 naturally occurring amino acids[J].Journal of Protein Chemistry, 1985, 4(1):23-55. DOI:10.1007/BF01025492.
[13]HELLBERG S, SJOESTROEM M, SKAGERBERG B, et al. Peptide quantitative structure-activity relationships, a multivariate approach[J].Journal of Medicinal Chemistry, 1987, 30:1126-1135. DOI:10.1021/jm00390a003.
[14]MEI H, LIAO Z H, ZHOU Y, et al. A new set of amino acid descriptors and its application in peptide QSARs[J]. Biopolymers,2005, 80:775-786. DOI:10.1002/bip.20296.
[15]TIAN F, YANG L, LüF, et al. In silico quantitative prediction of peptides binding affinity to human MHC molecule:an intuitive quantitative structure-activity relationship approach[J]. Amino Acids,2009, 36:535-554. DOI:10.1007/s00726-008-0116-8.
[16]TIAN F F, ZHOU P, LI Z L. T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides[J]. Journal of Molecular Structure, 2007, 830:106-115.DOI:10.1016/j.molstruc.2006.07.004.
[17]SHU M, MEI H, YANG S, et al. Structural parameter characterization and bioactivity simulation based on peptide sequence[J]. Qsar&Combinatorial Science, 2009, 28(2):27-35. DOI:10.1002/qsar.200710169.
[18]LI S Z, FU B. On structural parameterization and molecular modeling of peptide analogues by molecular electronegativity edge vector(VMEE):estimation and prediction for biological activity of dipeptides[J]. Chinese Journal of Chemistry, 2001, 48(1):937-944.DOI:10.1002/jccs.200100137.
[19]MEI H, ZHOU Y, SUN L L. A new descriptor of amino acids and its application in peptide QSAR[J]. Acta Physico-chimica Sinica, 2004,20(2):821-825. DOI:10.3866/PKU.WHXB20040808.
[20]梁桂兆,周鹏,周原,等.一组新氨基酸描述子用于肽定量构效关系研究[J].化学学报, 2006(5):393-396.
[21]LIANG G, LI Z. Factor analysis scale of generalized amino acid information as the source of a new set of descriptors for elucidating the structure and activity relationships of cationic antimicrobial peptides[J]. QSAR&Combinatorial Science, 2007, 26(3):754-763.DOI:10.1002/qsar.200630145.
[22]YANG L, SHU M, MA K, et al. ST-scale as a novel amino acid descriptor and its application in QSAM of peptides and analogues[J].Amino Acids, 2010, 38(2):805-816. DOI:10.1007/s00726-009-0287-y.
[23]TONG J, LIU S, ZHOU P, et al. A novel descriptor of amino acids and its application in peptide QSAR[J]. Journal of Theoretical Biology,2008, 253(3):90-97. DOI:10.1016/j.jtbi.2008.02.030.
[24]SNEATH P H. Relations between chemical structure and biological activity in peptides[J]. Journal of Theoretical Biology, 1966, 12(2):157-195. DOI:10.1016/0022-5193(66)90112-3.
[25]COLLANTES E R, DUNN W J. Amino acid side chain descriptors for quantitative structure-activity relationship studies of peptide analogs[J]. Journal of Medicinal Chemistry, 1995, 38:2705-2713.
[26]BRAVI G, GANCIA E, MASCAGNI P, et al. MS-WHIM, new 3D theoretical descriptors derived from molecular surface proper-ties:a comparative 3D QSAR study in a series of steroids[J]. Journal of Computer-Aided Molecular Design, 1997, 11:79-92. DOI:10.1023/A:1008079512289.
[27]苏满秀,王立峰,代志军,等.多肽一级结构表征与抗菌肽QSAM建模[J].高等学校化学学报, 2012, 33(11):2526-2531.
[28]汪婵,陈敏,李博.芝麻蛋白制备金属螯合肽的酶解工艺研究[J].食品科技, 2011, 36(9):184-189.
[29]TONG J B, ZHANG S W. A new 3D-descriptor of amino acid and its application in quantitative structure activity relationship of peptide drugs[J]. Acta Physico-chimica Sinica, 2007, 23(1):37-43.DOI:10.3866/PKU.WHXB20070108.
[30]丁俊杰,丁晓琴,赵立峰,等.新型三维氨基酸结构描述符的研究及其在多肽QSAR中的应用[J].药学学报, 2005, 40(4):340-346.
[31]LIN Z, LONG H, BO Z, et al. New descriptors of amino acids and their application to peptide QSAR study[J]. Peptides, 2008, 29(10):1798-1805. DOI:10.1016/j.peptides.2008.06.004.
[32]陈季旺,刘珊珊,蔡广霞,等.食物源血管紧张素转化酶抑制三肽的定量构效关系[J].食品科学, 2013, 34(9):19-23. DOI:10.7506/spkx1002-6630-201309005.