Data on bioactive peptides derived from chicken hydrolysate with potential alcohol dehydrogenase stabilizing activity and in silico analysis of their potential activity and applicability

Bioactive peptides have attracted extensive attention worldwide as natural alternatives to promote human health and wellness. Previous studies have shown that chicken hydrolysates could enhance alcohol dehydrogenase, and subsequently they facilitate alcohol metabolism and ameliorate alcohol-induced liver injury. The data presented in this article support the accompanying research article “Isolation and identification of alcohol dehydrogenase stabilizing peptides from Alcalase digested chicken breast hydrolysates”. Present article details all 82 peptides identified from the most active fractions of chicken hydrolysates, and 154 peptides from in silico digestion of the 82 identified peptides, together with the prediction of their potential bioactivity and applicability using several in silico assays.


Data
The data in this article include a total 82 peptides from the most active fractions of a chicken hydrolysate after SEC and RP-HPLC separation. Raw data is showed in Table 1 of Supplementary material, including search parameters. Table 1 lists all these peptides together with the potential bioactivity and applicability, indicated by Peptide Ranker score, potential peptide allergenicity, toxicity and physicochemical properties (i.e., hydrophobicity, amphipathicity, steric hindrance, pI, and molecular weight). Also, as potential functional food ingredients, peptides must be digested and absorbed through the gastrointestinal tract to exert activity. Hence, all peptides were further subjected to in silico gastrointestinal digestion and a total of 154 peptides were generated. All the 154 peptides are listed in Table 2, as well as their potential bioactivity and applicability.
Specifications Table   Subject Food science, Food biochemistry Specific subject area Bioactive peptides Type of data Table  How data were acquired The Alcalase digested chicken hydrolysate was separated through size-exclusion chromatography (SEC) and reversed-phase high-performance liquid chromatography (RP-HPLC) before the identification using nano-LC and ESI-Q-ToF in tandem mass spectrometry. Peptides were sequenced through database searching using Mascot. The identified peptides were further analyzed using various in silico methods including ExPASy PeptideCutter tool, Peptide Ranker, AllerTOP, and ToxinPred. Data format Analyzed Parameters for data collection MS: Positive polarity mode. MS1 scan from 350 to 1250 m/z for 250 ms, MS2 scan from 100 to 1500 m/z for 50 ms. Ions charging 1 to 5. Mascot data analysis: Significance threshold p < 0.05, using Chordata taxonomy, none enzyme digestion, and Uniprot database. The tolerance on the mass measurement was 0.3 Da for MS1 and 100 ppm for MS2. Description of data collection Data analysis was done using ExPASy PeptideCutter tool (http://web.expasy.org/ peptide_cutter/) to simulate the gastrointestinl digestion. The potential bioactivity was predicted using the Peptide Ranker software (http://distilldeep.ucd.ie/ PeptideRanker/). The potential peptide allergenicity was predicted using the AllerTOP v. 2.0 software (http://www.ddg-pharmfac.net/AllerTOP/index.html). Peptide toxicity and physicochemical properties (i.e., hydrophobicity, amphipathicity, steric hindrance, and pI) were studied using the ToxinPred software (http://crdd.osdd.net/raghava/toxinpred/ Value of the Data The data in this article includes a series of bioactive peptides separated and identified from a chicken hydrolysate that has been reported to exert alcohol dehydrogenase stabilizing activity. The potential bioactivity and applicability have been assessed and listed in this article. This article can benefit those who are interested in the separation and identification of bioactive peptides from food protein, with potential protection against alcoholic liver injury. The data can provide some useful information about the amino acid composition of the potential bioactive peptides, which is important for the quantitative-structure activity relationship (QSAR) study of bioactive peptides. Also, some of the peptides are assessed to be neither bioactive nor physically stable, and this information could result useful in future research.

Preparation and separation of chicken peptides
Peptides were produced and separated according to our previous study with slight modification [1]. In brief, chicken breast was digested in tris-HCl buffer (50 mM, pH 8.0, 1:5 m/v) by Alcalase 2.4L (0.5% w/w, protein basis) for 8 h. The hydrolysates were then centrifuged (10000 rpm, 20 min, 4 C), and deproteinized (adding 3 volumes of ethanol, stored at 4 C, 20h before centrifugation). After centrifugation at 10000 rpm at 4 C for 20 min, the ethanol was removed in a rotatory evaporator, and the peptides were lyophilized and re-dissolved in distilled water for Sephadex G25 separation. The peptides were eluted using 0.01 N HCl at 4 C with a flow rate of 5 mL/20 min. Fractions were collected, lyophilized and re-dissolved in distilled water for analysis and separation.
The most active fraction obtained from G25 fractionation was further isolated using HPLC with a Symmetry C18 column. The mobile phases consisted of solvent A: 0.1% v/v trifluoroacetic acetic acid (TFA) and solvent B: 0.085% v/v TFA in acetonitrile (ACN). Peptides were diluted using the following gradient: 0% B from 0 to 2 min, linearly increasing to 30% B at 50 min, 60% B at 60 min and 100% B at 65 min under a flow rate of 1 mL/min. Fractions were collected, lyophilized, and re-dissolved for analysis and identification.

Identification of peptide sequences
Peptides were identified using nano-LC tandem nanoelectrospray ionization source-quadrupoletime-of-flight (nanoESI-Q-ToF) MS/MS (AB Sciex Instruments, MA, USA). The sample was concentrated using Zip-Tip C18 (Millipore Corporation, Bedford, MA) before loaded onto an Eksigen trap column (3 mm C18-CL, 350 mm Â 0.5 mm). Peptides were eluted using an analytical column (3 mm C18-CL, 75 mm Â 123 mm; Nikkyo Technos Co, Ltd. Japan) under a flow rate of 0.3 mL/min at 30 C. The mobile phases consisted of solvent A: 0.1% v/v formic acid (FA) in water and solvent B: 1% FA in acetonitrile (ACN). Peptides were eluted linearly from 5% to 35% solvent B over the first 20 min, and then from 35% to 65% solvent B for 10 min.
The flow from the LC was ionized applying 2.8 kV. The Q-ToF was operated in positive polarity and information-dependent acquisition mode. MS1 scan was acquired from 350 to 1250 m/z for 250 ms, while MS2 scan was required from 100 to 1500 m/z for 50 ms on 50 of the most intense ions charging from 1 to 5. Up to 25 ions were selected for fragmentation after each survey scan. Dynamic exclusion was set to 15 s.
The database searching of peptides was performed using the Mascot Distiller v2.4.2.0 software (Matrix Science, Inc., Boston, MA; http://www.matrixscience.com), and Mascot search engine with a significance threshold p < 0.05 using Chordata taxonomy, none enzyme digestion, and Uniprot database. The tolerance on the mass measurement was 0.3 Da for MS and 100 ppm for MS/MS.

In silico analysis of peptide bioactivity and applicability
In silico gastrointestinal digestion was assessed using the ExPASy PeptideCutter tool (http://web. expasy.org/peptide_cutter/). PeptideCutter predicts the potential cleavage sites by proteases in a given peptide sequence, according to the specific cleavage sites of proteases, and thus generate new peptides [2]. In present study, pepsin (pH 1.3 and pH > 2.0), trypsin, and chymotrypsin were chosen as digesting enzymes [3].
The potential bioactivity was predicted using the Peptide Ranker software (http://distilldeep.ucd.ie/ PeptideRanker/) [4]. The prediction of peptide bioactivity was focused on particular amino acid residues as certain classes of bioactive peptides have specific structure features and amino acid sequences [5]. Peptides were scored from 0 to 1 and higher value means higher probability to be bioactive.
The potential peptide allergenicity was predicted using the AllerTOP v. 2.0 software (http://www. ddg-pharmfac.net/AllerTOP/index.html) [6]. Peptides were classified by k-nearest neighbor algorithm based on training set containing 2427 known allergens from different species and 2427 nonallergens.