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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

Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.
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f3-ijms-12-03148: Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.

Mentions: The importance or relative contribution of the amino acid descriptor variables (X) in PLS modeling was obtained from the VIP plots (Figure 2A–D). The X variables with VIP > 1.0 are regarded as important with above average contribution while those with VIP < 0.5 are unimportant; variables with 1.0 > VIP > 0.5 could be important or not depending on the size of the dataset [26]. For example, the sulphur-containing (SC) amino acids as a group as well the individual amino acids that include Met, Cys, Leu, and Lys are strong contributors (VIP values > 1.0) to the superoxide model (Figure 2D). In contrast, Gly, Ala, His, Asx, and Ile are poor contributors to the superoxide model due to VIP values of <0.5. For our present study, X-variables were regarded as important strong contributors only when their VIP > 1.0 and weak contributors with VIP of 0.5–1.0. The relative contribution of X-variables in the PLS models depends on the value of the coefficients relative to the origin in the loading space [13]; in other words, the higher the coefficients in both directions, the more the contribution of the X-variable in explaining or predicting Y [27]. The coefficients (Figure 3A–D) and VIP plots (Figure 2A–D) indicated that the specific contribution of each or groups of amino acid and physicochemical properties (3-z scales) depends on the oxidative assay system. As shown in Figure 3A and Table 2, a high percentage composition of Thr, Asx, hydrophobic amino acids, as well as ↑Σz3 (high electronic properties), ↓Σz1 (low hydrophilicity or high hydrophobicity) and ↓Σz2 (low bulk/molecular size) of the samples contributed positively to 2,2-diphenyl-1-picrylhydrazyl (DPPH)-scavenging. Interestingly, PCAA including His (highest VIP value) contributed negatively to the scavenging of DPPH. A similar pattern was also observed for H2O2-scavenging in addition to the strong positive contributions of Cys, Phe, Leu and Pro in this oxidative system (Figure 3C). Moreover, the SCAA (Cys + Met) were observed to be the strongest contributing amino acids for ferric reducing antioxidant power of the peptide mixtures whereas high amounts of Lys strongly reduced this activity (Figure 3B). In contrast, Lys and Leu composition supported superoxide radical scavenging by the samples while SCAA Met and Cys (highest VIP values) strongly reduced this activity. Interestingly, low z1 (high hydrophobicity) character seem to have strong positive contributions to scavenging of the free radicals like DPPH and superoxide anion radical (O2−) as well as H2O2 (a reactive oxygen species) but not FRAP. The results agree with previous reports that indicate hydrophobic amino acids are able to interact better (when compared to hydrophilic amino acids) with the lipophilic environments that contain these free radicals. Table 2 also shows other amino acid compositions and properties that weakly contributed positively or negatively to the antioxidant activities of the peptide samples in the four oxidative systems.


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

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

Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3-ijms-12-03148: Coefficient plots of scaled and centered data of the partial least square regression models: (A) DPPH radical scavenging; (B) ferric reducing antioxidant power (FRAP); (C) H2O2-scavenging; and (D) superoxide radical-scavenging. The importance of a given X-variable is proportional to its distance (coefficient value) from the origin (zero). Above zero values indicate positive contributions while values less than zero indicate negative contributions.
Mentions: The importance or relative contribution of the amino acid descriptor variables (X) in PLS modeling was obtained from the VIP plots (Figure 2A–D). The X variables with VIP > 1.0 are regarded as important with above average contribution while those with VIP < 0.5 are unimportant; variables with 1.0 > VIP > 0.5 could be important or not depending on the size of the dataset [26]. For example, the sulphur-containing (SC) amino acids as a group as well the individual amino acids that include Met, Cys, Leu, and Lys are strong contributors (VIP values > 1.0) to the superoxide model (Figure 2D). In contrast, Gly, Ala, His, Asx, and Ile are poor contributors to the superoxide model due to VIP values of <0.5. For our present study, X-variables were regarded as important strong contributors only when their VIP > 1.0 and weak contributors with VIP of 0.5–1.0. The relative contribution of X-variables in the PLS models depends on the value of the coefficients relative to the origin in the loading space [13]; in other words, the higher the coefficients in both directions, the more the contribution of the X-variable in explaining or predicting Y [27]. The coefficients (Figure 3A–D) and VIP plots (Figure 2A–D) indicated that the specific contribution of each or groups of amino acid and physicochemical properties (3-z scales) depends on the oxidative assay system. As shown in Figure 3A and Table 2, a high percentage composition of Thr, Asx, hydrophobic amino acids, as well as ↑Σz3 (high electronic properties), ↓Σz1 (low hydrophilicity or high hydrophobicity) and ↓Σz2 (low bulk/molecular size) of the samples contributed positively to 2,2-diphenyl-1-picrylhydrazyl (DPPH)-scavenging. Interestingly, PCAA including His (highest VIP value) contributed negatively to the scavenging of DPPH. A similar pattern was also observed for H2O2-scavenging in addition to the strong positive contributions of Cys, Phe, Leu and Pro in this oxidative system (Figure 3C). Moreover, the SCAA (Cys + Met) were observed to be the strongest contributing amino acids for ferric reducing antioxidant power of the peptide mixtures whereas high amounts of Lys strongly reduced this activity (Figure 3B). In contrast, Lys and Leu composition supported superoxide radical scavenging by the samples while SCAA Met and Cys (highest VIP values) strongly reduced this activity. Interestingly, low z1 (high hydrophobicity) character seem to have strong positive contributions to scavenging of the free radicals like DPPH and superoxide anion radical (O2−) as well as H2O2 (a reactive oxygen species) but not FRAP. The results agree with previous reports that indicate hydrophobic amino acids are able to interact better (when compared to hydrophilic amino acids) with the lipophilic environments that contain these free radicals. Table 2 also shows other amino acid compositions and properties that weakly contributed positively or negatively to the antioxidant activities of the peptide samples in the four oxidative systems.

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