health benefits and their demand has increased tremendously over
the past decade. The classical approaches for the discovery of foodderived bioactive peptides include (1) the selection of the appropriated food protein source, (2) their hydrolysis using enzymatic or
fermentation processes, (3) the fractionation of peptides released by
chromatography and their identification by mass spectrometry
(MS), (4) the in vitro or in vivo bioactivity screening of the purified
fractions of peptides, and (5) the production of synthetic peptides
to validate their bioactivity by different bioassays. Following this
traditional workflow, a substantial range of bioactivities have been
described for food-derived bioactive peptides which include antihypertensive, antimicrobial, antithrombotic, immunomodulating,
antioxidative, antilipemic, growth-promoting, and antitumoral
properties [8–10].
In most recent years, a considerable extent of research has been
focused on the liberation and characterization of food-derived
bioactive peptides using peptidomic, bioinformatic, and proteomic
approaches [11–15]. Proteomics, as the discipline for the largescale analysis of proteins of a particular biological system, has
greatly contributed to the assessment of quality, safety, and bioactivity of food products [16]. In a shotgun proteomic approach, a
mixture of proteins is digested with a protease (i.e., trypsin), and
the resulting mixture of peptides is then analyzed by liquid chromatography coupled with tandem mass spectrometry (LC–MS/
MS) [17]. Using database searching algorithms, such as Mascot
[18] and SEQUEST [19], fragmentation spectra are assigned to
putative peptide sequences, and the assignments are then validated
with programs such as Percolator [20] and PeptideProphet [21]. In
the context of food bioactive peptides, several food-derived bioactive peptide sequences have been identified using shotgun proteomics [11, 15, 22, 23]. Additionally, potential bioactive proteins and
peptides can be characterized by protein-based bioinformatic tools.
Such software includes programs to simulate in silico proteolysis
and to predict the physicochemical properties of the released peptides (i.e., antimicrobial, immunomodulatory, and antihypertensive). Several bioactive peptide databases are available online such
as APD3 [24], ACEpepDB [25], BIOPEP-UWM [26], BioPD
[27], BioPepDB [28], CAMP [29], FeptideDB [30], PPIP [31],
SpirPep [13], StraPep [32], and starPepDB [33].
In this chapter, a workflow for the characterization of food
bioactive peptides is presented (Fig. 1). The proposed workflow
integrates two consecutive steps: (a) Discovery phase: a shotgun
proteomic approach is used to create a reference data set for a
selected food proteome; (b) bioinformatic phase: the reference
proteome is subjected to an in silico human gastrointestinal digestion, and the released expected peptides are then analyzed using
several in silico protein-based bioinformatic analyses to predict and
characterize potential bioactive peptides. Using this workflow,
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Mo ´ nica Carrera et al.
the past decade. The classical approaches for the discovery of foodderived bioactive peptides include (1) the selection of the appropriated food protein source, (2) their hydrolysis using enzymatic or
fermentation processes, (3) the fractionation of peptides released by
chromatography and their identification by mass spectrometry
(MS), (4) the in vitro or in vivo bioactivity screening of the purified
fractions of peptides, and (5) the production of synthetic peptides
to validate their bioactivity by different bioassays. Following this
traditional workflow, a substantial range of bioactivities have been
described for food-derived bioactive peptides which include antihypertensive, antimicrobial, antithrombotic, immunomodulating,
antioxidative, antilipemic, growth-promoting, and antitumoral
properties [8–10].
In most recent years, a considerable extent of research has been
focused on the liberation and characterization of food-derived
bioactive peptides using peptidomic, bioinformatic, and proteomic
approaches [11–15]. Proteomics, as the discipline for the largescale analysis of proteins of a particular biological system, has
greatly contributed to the assessment of quality, safety, and bioactivity of food products [16]. In a shotgun proteomic approach, a
mixture of proteins is digested with a protease (i.e., trypsin), and
the resulting mixture of peptides is then analyzed by liquid chromatography coupled with tandem mass spectrometry (LC–MS/
MS) [17]. Using database searching algorithms, such as Mascot
[18] and SEQUEST [19], fragmentation spectra are assigned to
putative peptide sequences, and the assignments are then validated
with programs such as Percolator [20] and PeptideProphet [21]. In
the context of food bioactive peptides, several food-derived bioactive peptide sequences have been identified using shotgun proteomics [11, 15, 22, 23]. Additionally, potential bioactive proteins and
peptides can be characterized by protein-based bioinformatic tools.
Such software includes programs to simulate in silico proteolysis
and to predict the physicochemical properties of the released peptides (i.e., antimicrobial, immunomodulatory, and antihypertensive). Several bioactive peptide databases are available online such
as APD3 [24], ACEpepDB [25], BIOPEP-UWM [26], BioPD
[27], BioPepDB [28], CAMP [29], FeptideDB [30], PPIP [31],
SpirPep [13], StraPep [32], and starPepDB [33].
In this chapter, a workflow for the characterization of food
bioactive peptides is presented (Fig. 1). The proposed workflow
integrates two consecutive steps: (a) Discovery phase: a shotgun
proteomic approach is used to create a reference data set for a
selected food proteome; (b) bioinformatic phase: the reference
proteome is subjected to an in silico human gastrointestinal digestion, and the released expected peptides are then analyzed using
several in silico protein-based bioinformatic analyses to predict and
characterize potential bioactive peptides. Using this workflow,
216
Mo ´ nica Carrera et al.