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Chemometric analysis of the amino acid requirements of antioxidant food protein hydrolysates.

Udenigwe CC, Aluko RE - Int J Mol Sci (2011)

Bottom Line: Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H(2)O(2) radicals in addition to ferric reducing antioxidant power.In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H(2)O(2) radicals.We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification.

View Article: PubMed Central - PubMed

Affiliation: The Department of Human Nutritional Sciences and the Richardson Centre for Functional Foods and Nutraceuticals, University of Manitoba, Winnipeg, MB R3T 2N2, Canada; E-Mail: umudenig@cc.umanitoba.ca.

ABSTRACT
The contributions of individual amino acid residues or groups of amino acids to antioxidant activities of some food protein hydrolysates were investigated using partial least squares (PLS) regression method. PLS models were computed with amino acid composition and 3-z scale descriptors in the X-matrix and antioxidant activities of the samples in the Y-matrix; models were validated by cross-validation and permutation tests. Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H(2)O(2) radicals in addition to ferric reducing antioxidant power. For superoxide radicals, only lysine and leucine showed strong positive contributions while SCAA had strong negative contributions to scavenging by the protein hydrolysates. In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H(2)O(2) radicals. Therefore, food protein hydrolysates containing appropriate amounts of amino acids with strong contribution properties could be potential candidates for use as potent antioxidant agents. We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification.

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Related in: MedlinePlus

The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σzi) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging, and (D) superoxide radical-scavenging.
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f1-ijms-12-03148: The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σzi) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging, and (D) superoxide radical-scavenging.

Mentions: PLS modeling of amino acid parameters (X) and four different antioxidant activities (Y) of the food protein hydrolysates and fractions resulted in 12 models as shown in Table 1. The ferric reducing antioxidant property (FRAP) model (AA only) gave the best fit and predictive power whereas models for H2O2-scavenging displayed the lowest predictive powers (Figure 1 and Table 1). The R2 values (Table 1) indicated that the PLS models explained 30% to 73% of the sum of squares in Y-variance for all the oxidative systems with up to 66% predictive ability (derived from Q2cv). The 12 models were theoretically validated initially by cross-validation during modeling and their predictive power also validated by permutation, where the bioactivity data were each randomly permuted a number of times but with unaltered X-variable followed by modeling of each permutation [13]. As shown in Table 1, repeated (20) rounds of permutation yielded cumulative R2 (R2cum) intercept values of 0.006 to 0.314 and Q2cv intercept values of −0.223 to −0.008 for the 12 models, which are within suggested valid limits of R2cum intercept <0.4 and Q2cv intercept <0.05 for valid PLS models [25]. The t/u PLS score plots show relationships between X and Y variables in the AA + gAA + Σzi (antioxidant activity + amino acid group + sum of z values) models for the four oxidative systems (Figure 1A–D). With the exception of the weak fit for H2O2, the data showed good fit and must have contributed positively to obtaining valid models that we used to determine relationships between amino acids or groups of amino acids and contributions to antioxidant potential of the food protein hydrolysates.


Chemometric analysis of the amino acid requirements of antioxidant food protein hydrolysates.

Udenigwe CC, Aluko RE - Int J Mol Sci (2011)

The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σzi) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging, and (D) superoxide radical-scavenging.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC3116181&req=5

f1-ijms-12-03148: The t/u score plots of the partial least squares (PLS) models showing relationships between the antioxidant activity and amino acid descriptors (AA + gAA + Σzi) of the food protein hydrolysates and peptide fractions; (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging, and (D) superoxide radical-scavenging.
Mentions: PLS modeling of amino acid parameters (X) and four different antioxidant activities (Y) of the food protein hydrolysates and fractions resulted in 12 models as shown in Table 1. The ferric reducing antioxidant property (FRAP) model (AA only) gave the best fit and predictive power whereas models for H2O2-scavenging displayed the lowest predictive powers (Figure 1 and Table 1). The R2 values (Table 1) indicated that the PLS models explained 30% to 73% of the sum of squares in Y-variance for all the oxidative systems with up to 66% predictive ability (derived from Q2cv). The 12 models were theoretically validated initially by cross-validation during modeling and their predictive power also validated by permutation, where the bioactivity data were each randomly permuted a number of times but with unaltered X-variable followed by modeling of each permutation [13]. As shown in Table 1, repeated (20) rounds of permutation yielded cumulative R2 (R2cum) intercept values of 0.006 to 0.314 and Q2cv intercept values of −0.223 to −0.008 for the 12 models, which are within suggested valid limits of R2cum intercept <0.4 and Q2cv intercept <0.05 for valid PLS models [25]. The t/u PLS score plots show relationships between X and Y variables in the AA + gAA + Σzi (antioxidant activity + amino acid group + sum of z values) models for the four oxidative systems (Figure 1A–D). With the exception of the weak fit for H2O2, the data showed good fit and must have contributed positively to obtaining valid models that we used to determine relationships between amino acids or groups of amino acids and contributions to antioxidant potential of the food protein hydrolysates.

Bottom Line: Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H(2)O(2) radicals in addition to ferric reducing antioxidant power.In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H(2)O(2) radicals.We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification.

View Article: PubMed Central - PubMed

Affiliation: The Department of Human Nutritional Sciences and the Richardson Centre for Functional Foods and Nutraceuticals, University of Manitoba, Winnipeg, MB R3T 2N2, Canada; E-Mail: umudenig@cc.umanitoba.ca.

ABSTRACT
The contributions of individual amino acid residues or groups of amino acids to antioxidant activities of some food protein hydrolysates were investigated using partial least squares (PLS) regression method. PLS models were computed with amino acid composition and 3-z scale descriptors in the X-matrix and antioxidant activities of the samples in the Y-matrix; models were validated by cross-validation and permutation tests. Based on coefficients of the resulting models, it was observed that sulfur-containing (SCAA), acidic and hydrophobic amino acids had strong positive effects on scavenging of 2,2-diphenyl-1-picrylhydrazyl (DPPH) and H(2)O(2) radicals in addition to ferric reducing antioxidant power. For superoxide radicals, only lysine and leucine showed strong positive contributions while SCAA had strong negative contributions to scavenging by the protein hydrolysates. In contrast, positively-charged amino acids strongly contributed negatively to ferric reducing antioxidant power and scavenging of DPPH and H(2)O(2) radicals. Therefore, food protein hydrolysates containing appropriate amounts of amino acids with strong contribution properties could be potential candidates for use as potent antioxidant agents. We conclude that information presented in this work could support the development of low cost methods that will efficiently generate potent antioxidant peptide mixtures from food proteins without the need for costly peptide purification.

Show MeSH
Related in: MedlinePlus