Zinc Chelating Activity and Quantitative Structure-activity Relationship of Tripeptides†

doi:10.7503/cjcu20170487

Abstract

Abstract:

51 Tripeptides were synthesized and the zinc chelating activities were determined, which formed the database. Then those tripeptides were statistically analysed using 18 amino acid descriptors and partial least squares method. Results showed that the correlation coefficients of the quantitative structure-activity relationship(QSAR) models based on 3 amino acids descriptors met the requirements, which were the descriptor FASGAI, Z and C. And the descriptor FASGAI was the best with R²=0.8225, Q²=0.5818, RMSEE=0.1628, $Q ext 2$ =0.6760, RMSEP=0.2499. Further analysis of the model on descriptor FASGAI revealed that the influence of amino acid position in polypeptide on the zinc chelating activity was N₃>N₂>N₁. Meanwhile, the bulky properties of amino acid residues influenced the zinc chelating activity of tripeptides.

Key words: Rapeseed protein, Zinc chelating peptide, Quantitative structure-activity relationship

CLC Number:

O629

TrendMD:

YU Min, HUANG Jingjing, MA Min, FU Ruiyan, YAN Yan, ZHANG Fusheng, YIN Junfeng, XIE Ningning. Zinc Chelating Activity and Quantitative Structure-activity Relationship of Tripeptides^†[J]. Chem. J. Chinese Universities, 2018, 39(2): 234.

Figures/Tables 8

Table 1 Amino acid descriptors

Amino acid descriptor	Kinds	Parameters	Ref.
Z	2D	Bulk, hydrophobicity, electrogenicity	[4]
ISA-ECI	3D	Isotropic surface area, electronic charge index	[5]
MS-WHIM	3D	Electrostatic potential	[18]
VSGE	3D	Amount of molecular geometry calculated by quantum chemical calculations	[6]
VSTV	3D	Structural and topological index	[11]
VHSE	2D	Bulk, hydrophobicity, electrogenicity	[7]
SZOTT	3D	Structural parameters of 1369 kinds of 0D—3D	[12]
C	3D	3D Dimensional electronic, structural, spatial, topological, graphical, and conformational parameters	[19]
SVG	3D	Geometric index	[16]
TEN	2D	Bulk, hydrophobicity, electrogenicity	[20]
HESH	2D	Bulk, hydrophobicity, electrogenicity, hydrogen bond property	[10]
FASGAI	2D	Bulk, hydrophobicity, electrogenicity	[13]
ST	3D	Constitutional, topological, geometrical, hydrophobic, electronic and steric properties	[14]
DPPS	2D	Bulk, hydrophobicity, electrogenicity, hydrogen bond property	[8]
WNH	2D	van der Waals volume, electrostatic charge index, hydrophobicity	[15]
VSW	2D	Molecular index	[7]
VTSA	3D	Structural and topological index	[15]
T	3D	Structural and topological index	[9]

Table 2 Database of zinc chelating peptides

Sample^a	Activity^b	Sample^a	Activity^b	Sample^a	Activity^b	Sample^a	Activity^b
ASC	1.022487	NSH	0.698404	ASE	0.555085	ASF	0.023164
ASV	0.740324	DSH	0.917523	CSC	1.026377	ATH	0.073816
ASM	0.638711	MSM	0.068054	ASW	0.414903	AGH	0.088551
ASK	0.18409	YSY	0.095352	AIH	0.005852	AYH	0.071237
ASD	0.683995	AFH	-0.03153	AWH	0.07118	CHS	1.087216
ASN	0.689764	KSK	0.094641	MSH	0.498983	SHC	1.062348
ASQ	0.183247	WSW	0.078503	ESE	0.92505	HSC	1.087498
QSH	0.478612	RSH	0.033771	ASI	0.021063	ASR	0.364256
YSH	0.459559	KSH	0.116798	LSH	-0.02551	ESH	0.777315
QSQ	0.159214	RSR	0.092023	ALH	0.000325	ACH	1.004871
WSH	0.384519	HCS	1.004917	ISH	.057932	VSV	0.075138
AMH	0.06195	FSH	0.033673	AVH	0.007564	SAC	1.013029
VSH	0.413429	AAH	0.013959	CSA	1.007763

Table 3 QSAR models based on eighteen amino acid descriptors*

Amino acid descriptor	M	R²	Q²	RMSEE	$Q ext 2$	RMSEP
Z	1	0.7293	0.6098	0.2011	0.6098	0.2414
ISA-ECI	1	0.3361	0.0399	0.3149	0.4070	0.3787
MS-WHIM	1	0.4203	0.1815	0.2942	0.3936	0.3669
VSGE	2	0.7614	0.3023	0.1888	0.3467	0.3228
VSTV	1	0.5971	0.3090	0.2591	0.5165	0.3371
VHSE	1	0.6638	0.4702	0.2241	0.4250	0.2813
SZOTT	1	0.7226	0.4648	0.2036	0.4570	0.2827
C	1	0.6796	0.5386	0.2187	0.6861	0.2625
SVG	1	0.4754	-0.5332	0.2835	-0.013	0.4846
TEN	1	0.8312	0.4268	0.1588	0.3854	0.2926
HESH	1	0.7023	0.4737	0.2109	0.5148	0.2804
FASGAI	1	0.8225	0.5818	0.1628	0.6760	0.2499
ST	1	0.6163	0.4237	0.2394	0.4570	0.2934
DPPS	1	0.6608	0.4675	0.2251	0.5480	0.2820
WNH	1	0.5644	0.2515	0.2558	0.4776	0.3354
VSW	1	0.7012	0.4309	0.2113	0.3838	0.2915
VTSA	1	0.5597	0.2157	0.2622	0.3273	0.3499
T	1	0.4595	0.2698	0.2812	0.4131	0.3269

Table 3 QSAR models based on eighteen amino acid descriptors*

Amino acid descriptor	M	R²	Q²	RMSEE	$Q ext 2$	RMSEP
Z	1	0.7293	0.6098	0.2011	0.6098	0.2414
ISA-ECI	1	0.3361	0.0399	0.3149	0.4070	0.3787
MS-WHIM	1	0.4203	0.1815	0.2942	0.3936	0.3669
VSGE	2	0.7614	0.3023	0.1888	0.3467	0.3228
VSTV	1	0.5971	0.3090	0.2591	0.5165	0.3371
VHSE	1	0.6638	0.4702	0.2241	0.4250	0.2813
SZOTT	1	0.7226	0.4648	0.2036	0.4570	0.2827
C	1	0.6796	0.5386	0.2187	0.6861	0.2625
SVG	1	0.4754	-0.5332	0.2835	-0.013	0.4846
TEN	1	0.8312	0.4268	0.1588	0.3854	0.2926
HESH	1	0.7023	0.4737	0.2109	0.5148	0.2804
FASGAI	1	0.8225	0.5818	0.1628	0.6760	0.2499
ST	1	0.6163	0.4237	0.2394	0.4570	0.2934
DPPS	1	0.6608	0.4675	0.2251	0.5480	0.2820
WNH	1	0.5644	0.2515	0.2558	0.4776	0.3354
VSW	1	0.7012	0.4309	0.2113	0.3838	0.2915
VTSA	1	0.5597	0.2157	0.2622	0.3273	0.3499
T	1	0.4595	0.2698	0.2812	0.4131	0.3269

Fig.1 Correlation between calculated values and observed values of a calibration set using FASGAI(A), Z(B) and C(C) descriptors

Fig.2 Correlation between calculated values and observed values of a prediction set using FASGAI(A), Z(B) and C(C) descriptors

Fig.3 VIP value of the QSAR model with FASGAI amino acid descriptors

Fig.4 Regression coefficient of QSAR model using FASGAI amino acid descriptor

Table 4 Important properties of peptide after PLSR analysis

Variable	Position	Properties	VIP value	Regression coefficient
X15	N₃	Steric	2.4278	-0.4864
X9	N₂	Steric	1.3839	-0.3301
X3	N₁	Steric	1.1505	-0.2351
X17	N₃	Local flexibility	1.0456	-0.1034

References 22

[1]	Prasad A. S., J. Trace Elem. Med. Biol., 2012, 26(2), 66—69
[2]	Sneath P. H., J. Theor. Biol., 1966, 12(2), 157—195
[3]	Hellberg S., Sjostrom M., Wold S., Acta Chem. Scand., 1986, 40, 135—140
[4]	Hellberg S., Sjostrom M., Skagerberg B., J. Med. Chem., 1987, 30, 1126—1135
[5]	Collantes E. R., Dunn W. J., J. Med. Chem., 1995, 38(1), 2705—2713
[6]	Tong J. B., Zhang S. W., Acta Phys.-Chim. Sinica, 2007, 23(1), 37—43
	(仝建波, 张生成. 物理化学学报, 2007,23(1), 37—43)
[7]	Mei H., Liao Z. H., Zhou Y., Li S. Z., J. Pept. Sci., 2005, 80, 775—786
[8]	Tian F., Yang L., Lv F., Yang Q., Zhou P., Amino Acids, 2009, 36, 535—554
[9]	Tian F., Zhou P., Li Z., J. Mol. Struct., 2007, 830, 106—115
[10]	Shu M., Mei H., Yang S., Liao L., Li Z., Qsar Comb. Sci., 2009, 28(2), 27—35
[11]	Mei H., Zhou Y., Sun L. L., Li Z. L., Acta Phys.-Chim. Sinica, 2004, 20(8), 821—825
	(梅虎, 周原, 孙立力, 李志良. 物理化学学报, 2004,20(8), 821—825)
[12]	Liang G. Z., Zhou P., Zhou Y., Zhang Q. X., Li Z. L., Acta Chim. Sinica, 2006, 64(5), 393—396
	(梁桂兆, 周鹏, 周原, 张巧霞, 李志良. 化学学报, 2006,64(5), 393—396)
[13]	Liang G., Li Z., QSAR Comb. Sci., 2007, 26(3), 754—763
[14]	Yang L., Shu M., Ma K., Amino Acids, 2010, 38(2), 805—816
[15]	Lin Z. H., Long H. X., Bo Z., Wang Y. Q., Wu Y. Z., Peptides,2008, 29(10), 1798—1805
[16]	Tong J. B., Liu S. L., Liu Y. T., Ma Y. M., Tong X. M., Fine Chemicals, 2008, 25(7), 655—659
	(仝建波, 刘淑玲, 刘玉婷, 马养民, 童晓梅. 精细化工, 2008,25(7), 655—659)
[17]	Tong J., Liu S., Zhou P., Wu B., Li Z., J. Theor. Biol., 2008, 253(3), 90—97
[18]	Bravi G., Gancia E., Mascagni P., Pegna M., Todeschini R., Zaliani A., J. Comput. Aid. Mol. Des., 1997, 11(2), 79—92
[19]	Ding J. J., Ding X. Q., Zhao L. F., Chen J. S., Acta Pharm. Sin., 2005, 40(4), 340—346
	(丁俊杰, 丁晓琴, 赵立峰, 陈翼胜. 药学学报, 2005,40(4), 340—346)
[20]	Kidera A., Konishi Y., Oka M., Ooi T., Scheraga H. A., J. Protein Chem., 1985, 4(1), 23—55
[21]	Wang C., Chen M., Li B., Food Sci. Tech., 2011, 35(9), 184—186
	(汪婵, 陈敏, 李博. 食品科技, 2011,35(9), 184—186)
[22]	Yin J. X., Li Z. H., Mu Y., Lü S. W., Luo G. M., Chem. J. Chinese Universities, 2016, 37(7), 1302—1306
	(尹居鑫, 李祖宏, 牟颖, 吕绍武, 罗贵民. 高等学校化学学报, 2016,37(7), 1302—1306)