universal mathematical symbol for a number.

#### 2.1.1. Peptide Ranker

Peptide Ranker (http://distilldeep.ucd.ie/PeptideRanker/, accessed on 24 November 2022) is an open source software resource, which can be used to predict the potential bioactivity of peptides based on a novel N-to-1 neural network. Any user can submit peptides to Peptide Ranker, which will be returned to the user ranked by the probability that the peptide will be bioactive. It is important to note that this is not a prediction of the probability that the peptide has bioactivity [30].

Identified peptide IGNNPAKGGLF had a Peptide Ranker score of 0.82 which was the highest value obtained for any peptide identified using MS from the *L. digitata* 3 kDa permeate. This indicates that this peptide likely has bioactivity. Acceptable probability values for bioactivity are between 1.0–0.5. The peptide YIGNNPAKGGLF had a Peptide Ranker score of 0.81, indicating high potential bioactivity (Table 1). Peptides DAALDFGPAL and AFYDYIGNNPAKGGLF had Peptide ranker scores of 0.78. Peptide SDGKIFDPL had a score of 0.74 (Table 1).



**Table 2.** *Cont.* http://web.expasy.org/peptide\_cutter/,

software

 accessed on 10 December 2022.

#### 2.1.2. BIOPEP

A search of the BIOPEP database (https://biochemia.uwm.edu.pl/biopep-uwm/, accessed on 10 December 2022) [46] determined the novelty of the peptides identified and shown in Table 1. Of the ten peptides analyzed and listed in Table 2, their amino acid sequences were not identified in previously published papers concerning seaweed proteins and bioactive peptides [31–45].

#### 2.1.3. Simulated Digestion Using Peptide Cutter

Peptide cutter software (http://web.expasy.org/peptide\_cutter/, accessed on 10 December 2022) [47] was used to determine if the identified peptides could potentially survive GI digestion. Peptides shown in Table 1 underwent simulated digestion using the GI tract enzymes, pepsin (pH 1.3), trypsin, and chymotrypsin. All peptides were cleaved into shorter peptide fragments that in some instances had known bioactivities and are found in BIOPEP (Table 2). Simulated GI digestion of the 10 peptides shown in Table 1 produced smaller peptides such as the active peptide GGL (derived following GI simulated digestion from YIGNNPAKGGLF). GGL is an active fragment, is a known anti-microbial peptide found in BIOPEP and it also has alpha-glucosidase inhibitory activities seen previously in Iberian dry-cured ham [31]. Peptides associated with other bioactivities include DPP-IV inhibition for monopeptides I, L; ACE-1 inhibition for dipeptides GD, TF, DP [35,36,43–45]. The monopeptide F, is a hydrophobic aromatic, amino acid, and it is thought, to enhance anti-oxidant activity [31]. Peptide IGNNPAKGGLF was digested into 3 peptides with sequences of IGNNPAK; GG and F. When comparing the first two peptides sequences listed in Table 2, the monopeptide Y, was one of two differing peptides. This monopeptide, Y, is hydrophobic, an aromatic amino acid, with anti-oxidant and anti-microbial bioactivity [31–33].

Several bioactive peptides also result from simulated GI digestion of the peptide DAALDFGPAL. Following simulated GI digestion the peptides DAA and GPAL result. DAA is a known antimicrobial peptide sequence found in the peptide tenecin 1, an insect defensin peptide [32]. The dipeptide DY results from simulated digestion of AFYDYIGN-NPAKGGLF. This dipeptide is a known ACE-1 inhibitory peptide [34].

Simulated GI digestion of peptide SDGKIFDPL produces peptides SDGK and DP. The dipeptide DP identified previously from the dark muscle of tuna is a known ACE-1 inhibitor that also has anti-hypertensive activity shown in rat studies previously [36].

The peptide QGR occurs following simulated GI digestion of QGRVPGDIGFDPL. This tripeptide has known anti-microbial activity [37]. The peptide PL results following simulated GI digestion of YDYIGNNPAKGGLF. This tripeptide has known anti-microbial activity, and PL is also an ACE-1 inhibitor [37,38].

#### 2.1.4. Toxicity Assessment Using In Silico Analysis

All 130 peptides identified using MS were assessed for their potential to be toxic using ToxinPred (https://webs.iiitd.edu.in/raghava/toxinpred2/batch.html, accessed on 10 December 2022) [48]. Of the 130 peptides, tested results indicate that no peptides have potential toxicity.

#### 2.1.5. Peptide Synthesis and ACE-1 Inhibition

The peptides IGNNPAKGGLF and YIGNNPAKGGLF were synthesized and assessed in vitro for their ability to inhibit ACE-1. The peptide IGNNPAKGGLF was found to inhibit ACE-1 by 80% and YIGNNPAKGGLF inhibited ACE-1 by 91% when assayed at a concentration of 1 mg/mL compared to the control Captopril® assayed at a concentration of 0.05 mg/mL. The ACE-1 IC50 values determined for both peptides were 174.4 μg/mL and 133.1 μg/mL for IGNNPAKGGLF and YIGNNPAKGGLF, respectively.

#### **3. Discussion**

Ten different peptide sequences were identified from the *L. digitata* protein 3 kDa permeate using MS. The ACE-1 inhibitory activity of two of these peptides was confirmed using chemical synthesis and assessment in vitro for ACE-1 inhibition. Additionally, other bioactivities were predicted using in silico methods. The MS-sequenced peptides ranged in length from 9–15 amino acids. All identified peptides were novel based on a search of the BIOPEP database and the literature. Peptide Ranker values were obtained for all peptides and the peptides likely to have bioactivities are shown in Table 1. These peptides had Peptide Ranker values greater than 0.5.

Two peptides, with Peptide Ranker scores of 0.82 and 0.81 were selected for chemical synthesis. ACE-1 inhibition values were determined in vitro for these peptides with amino acid sequences IGNNPAKGGLF and YIGNNPAKGGLF. ACE-1 and IC50 results for these synthesized peptides were obtained. Peptide IGNNPAKGGLF inhibited ACE-1 by 80% and YIHNNPAKGGLF inhibited ACE-1 by 91% when assayed at 1 mg/mL. The ACE-1 IC50 value for IGNNPAKGGLF was 174.4 μg/mL (0.161 μM) ACE-1. Peptide YIGNNPAKGGLF had an IC50 value of 133.1 μg/mL (0.11 μM) compared to Captopril© with a documented ACE-1 IC50 value of 500 μg/mL (2.3 μM) [8]. Previous studies on marine cryptides, used Captopril© as a positive control with IC50 values of (1.79–15.1 nM) for ACE-1, and another drug Losartan was used as a negative control for ACE-II inhibition, and had IC50 values of (17.13–146 μM) [49]. The IC50 for Captopril© varies depending on application and extraction methods used, with an IC50 of 7.09 nM from visible spectrophotometric (VSP) and for high-performance liquid chromatography (HPLC), and an IC50 of 4.94 nM [50]. Common hypertensive drugs, using the ACE-1 mechanism of control include Captopril©, Enalapril, Tekturna and Rasilez [51].

Peptides with ACE-1 IC50 values ranging from 2.42–20.63 μM [52] were identified from protein hydrolysates generated from *Laminaria japonica* previously. The IC50 values obtained for our synthesized peptides are greater than ACE-1 IC50 values reported previously for peptides such as HR, extracted from a bovine hydrolysate with an ACE-1 IC50 of 0.19 mM [51]. The ACE-1 inhibitory activity of the synthesized peptides is greater than the value reported for the *L. digitata* hydrolysate and shows the potential of these peptides for potential use in the treatment of hypertension.

Simulated GI digestion increased the potential bioactivities of identified peptides and several peptides with alpha-glucosidase and anti-microbial activities were found. Inhibition of alpha-glucosidase reduces carbohydrate digestion, consequently decreasing carbohydrate content in blood, which improves human health outcomes regarding type 2 diabetes [31,53]. The dipeptide sequence SE, cleaved from the novel peptide SEFIGFPIK (shown in Table 2), has potential stimulating vasoactive substance release bioactivity, discovered in peptides sourced from casein and soy protein previously [43,53]. The antiinflammatory peptide sequence IGF also results from the GI digestion of SEFIGFPIK. This tripeptide is found in the pepsin hydrolysis of hempseed protein [42]. The peptide GNK that is cleaved from sequenced peptide GDFGNKDGKLTF is found in the Arietin peptide-A known as Fibrinogen interaction inhibitor. The dipeptide TF is also cleaved from the same sequenced peptide and is a known ACE-1 inhibitor [43–45].

This work identified two novel ACE-1 inhibitory peptides with pharmaceutically relevant ACE-1 IC50 values. In addition, the array of bioactive peptides that result following simulated GI digestion demonstrates the potential bioactivities still to be harnessed from brown seaweed proteins in *L. digitata.*

Additional bioactivities were also identified from cryptides identified following simulated gastrointestinal (GI) digestion. These bioactivities included Dipeptidyl peptidase IV (DPP-IV) inhibition potential for peptide sequences SDGK and alpha-glucosidase inhibition potential of peptides GGL and IGNNPAK. Future work will involve the synthesis of these peptides and determination of their in vitro inhibitory activities as well as the determination of their relevant IC50 values. Inhibitors of DPP-IV and alpha-glucosidase enzymes are

the key targets for the pharmaceutical sector for development of drugs to prevent or to control type 2 diabetes [T2D].

#### **4. Materials and Methods**

#### *4.1. Mass Spectrophotometry (MS) Characterisation of 3kDa Permeates*

Protein extraction and peptide enrichment using molecular weight cut-off (MWCO) filtration was performed prior to MS characterisation in accordance with the method outlined in [8].

Peptide fractions were prepared for MS characterisation using the Phoenix peptide clean-up kit 4X, manufactured by Peromics and following the clean-up method supplied by the manufacturer. Peptides were identified using a mass spectrometer nanoESI qQTOF (6600 plus TripleTOF, AB SCIEX, Framingham, MA, U.S.A.) using liquid chromatography and tandem mass spectrometry (LC–MS/MS). A total of 1 μL of microalgal permeate was loaded onto a trap column (3 <sup>μ</sup> C18-CL 120 A, 350 ˘ <sup>μ</sup><sup>M</sup> <sup>×</sup> 0.5 mm; Eksigent) and desalted with 0.1% TFA (trifluoroacetic acid) at 5 μL/min for 5 min. The peptides were then loaded onto an analytical column (3 <sup>μ</sup> C18-CL 120 A, 0.075 ˘ <sup>×</sup> 150 mm; Eksigent) equilibrated in 5% acetonitrile 0.1% FA (formic acid). Elution was carried out with a linear gradient from 7 to 45% B in A for 20 min, where solvent A was 0.1% FA and solvent B was ACN (acetonitrile) with 0.1% FA at a flow rate of 300 nL/min. The sample was ionized in an electrospray source Optiflow < 1 μL Nano applying 3.0 kV to the spray emitter at 200 ◦C. Analysis was carried out in a data-dependent mode. Survey MS1 scans were acquired from 350 to 1400 *m*/*z* for 250 ms. The quadrupole resolution was set to 'LOW' for MS2 experiments, which were acquired from 100 to 1500 *m*/*z* for 25 ms in 'high sensitivity' mode. Up to 50 ions were selected for fragmentation after each survey 400 scan. Dynamic exclusion was set to 15 s. The system sensitivity was controlled by analyz-401 ing 500 ng of K562 protein extract digest (SCIEX); in these conditions, 2260 proteins were identified (FDR < 1%) in a 45 min gradient. Peptides identified as having potential bioactivities were chemically synthesised by GenScript Biotech (Leiden, The Netherlands). GenScript also verified the purity of the peptides by analytical RP-HPLC–MS.

#### *4.2. In Silico Analysis of MS Sequenced Peptides*

Peptide Ranker was used to predict the bioactivity of peptide sequences and values of between 0.5 and 1 were taken as indicative of peptides having bioactivity.

Figure 1 shows the six steps used during in silico analysis. Of the 130 peptides identified using MS, only those with >95% confidence were selected for synthesis and in silico analysis. Selected peptides were input into the software programme Peptide Ranker (http://distilldeep.ucd.ie/PeptideRanker/, accessed on 15 December 2022). A value indicative of potential bioactivity was obtained for each peptide. Only peptides with Peptide Ranker scores greater than 0.5 were used in further analysis. Ten peptide sequences were identified as having potential bioactivities. The novelty of these peptides was determined following a search in the peptide database BIOPEP (http://www.uwm.edu.pl/biochemia/ index.php/pl/biopep, accessed on 12 December 2022). Active peptides were further assessed for their ability to survive simulated GI digestion using Expasy peptide cutter (http://web.expasy.org/peptide\_cutter/, accessed on 10 December 2022). The UniProt database was used to identify proteins containing the peptide sequences. Additionally, the potential toxicity of identified peptides was assessed using the software programme Toxin-Pred (https://webs.iiitd.edu.in/raghava/toxinpred2/batch.html, accessed on 10 December 2022).


**Figure 1.** In silico methodology based on the method by Lafarga et al. 2014, Hayes et al., 2018, and Hayes et al., 2021 [3,6,7] was used for the identification and generation of ACE-I inhibitory peptides from *L. digitata* proteins. Information including the structure, amino acid sequence and composition of the proteins was collected. Peptide Ranker; BIOPEP; Expasy PeptideCutter Tool; UniProt and ToxinPred were used on the peptide sequences. Peptide Ranker and BIOPEP ranked the potentially most bioactive sequences and identified the bioactivities of these peptides. Expasy PeptideCutter Tool was used to predict the probable cleavage sites of selected enzymes within the top ten sequences listed in Table 1. ToxinPred was used for predicting the toxicity of peptides identified in this project.

#### *4.3. ACE-1 Inhibitory Activity Assessment*

The peptides with the highest Peptide Ranker scores IGNNPAKGGLF, with a peptide ranker value of 0.82, and YIGNNPAKGGLF, with a value of 0.81,were selected for synthesis. Once made, peptides were re-tested using in vitro screening assays. ACE-1 activity was tested using an assay kit supplied by Cambridge BioSciences (Cambridge, UK) as described previously. Captopril© (a known ACE-1 inhibitor) dissolved in distilled water was used as a positive control.

#### **5. Conclusions**

In silico and in vitro methods are useful tools for selection of enzymes to generate bioactive peptides from protein containing biomass. Moreover, they are useful to determine potential bioactivities of peptides prior to chemical synthesis and can save time and money prior to animal studies to determine potential health benefits. A combination of these methods was used previously to identify and confirm the bioactivity of peptides derived from blood proteins [51] and microalgae previously [54]. However, limitations of this approach exist and specifically include limits concerning the folding of protein, which has an impact on how enzymes cut the protein and which in turn can impact production of the resulting peptides. One of the main barriers for entering the human functional foods market is unknown and unstable peptide product qualities. It is required to have analytical methods for characterising the peptide fraction. Today, research groups are using Fourier-transform Infrared (FTIR) fingerprints to gain new insight in quality variations of peptide products. These fingerprints can be related to raw material composition and processing factors [55]. The method used in this study has advantages over in vitro only methods as it can help to predict the best enzymes to use to generate bioactive peptide containing hydrolysates and additionally can predict the most bioactive peptides and those that may be toxic before any in vitro assays are performed.

Two novel ACE-1 inhibitory peptides with amino acid sequences corresponding to IGNNPAKGGLF and YIGNNPAKGGLF were identified from a 3 kDa permeate of a protein hydrolysate generated from the brown seaweed *L. digitata.* In silico methods also predicted the potential of this seaweed as a source of novel, bioactive peptides that may impart additional health benefits to the consumer including prevention of T2D and antimicrobial activities following GI digestion. Identified, chemically synthesized peptides had ACE-1 inhibition IC50 values of 174.4 μg/mL (0.161 μM) for peptide IGNNPAKGGLF and 133.1 μg/mL (0.107 μM) for peptide YIGNNPAKGGLF and both peptides were similar in terms of bioactivity to other ACE-1 inhibitory peptides identified from tuna and meat muscle previously. This study highlights the potential bioactivity of this brown seaweed. However, future work is required to confirm an anti-hypertensive effect of the seaweed hydrolysate and synthesized peptides in vivo. This work will involve assessment of the *L. digitata* hydrolysate and synthesized peptides in spontaneously hypertensive rats (SHRs) to assess if the ACE-1 inhibitory peptides have an anti-hypertensive effect in vivo.

**Author Contributions:** Conceptualization, M.H., D.P. and M.A.P.; methodology, M.H.; software, D.P.; validation, M.H., D.P. and M.A.P.; formal analysis, D.P.; investigation, D.P.; resources, D.P.; data curation, D.P.; writing—original draft preparation, D.P.; writing—review and editing, M.H., D.P. and M.A.P.; visualization, D.P.; supervision, M.H. and M.A.P.; project administration, D.P.; funding acquisition, D.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** Diane Purcell-Meyerink, also known as Diane Purcell, has received funding from the Teagasc Research Leaders 2025 programme co-funded by Teagasc and the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 754380.

**Data Availability Statement:** Data are available from the corresponding author.

**Acknowledgments:** The author would like to acknowledge the technical support of Karen Hussey at Teagasc for her assistance in sample analysis.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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