Next Article in Journal
Food Pathways of Salmonella and Its Ability to Cause Gastroenteritis in North Africa
Next Article in Special Issue
Unlocking the Functional and Nutritional Potential of Microalgae Proteins in Food Systems: A Narrative Review
Previous Article in Journal
A Review on Blockchain Applications in Operational Technology for Food and Agriculture Critical Infrastructure
Previous Article in Special Issue
Characteristics of Umami Taste of Soy Sauce Using Electronic Tongue, Amino Acid Analyzer, and MALDI−TOF MS
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Foods
Volume 14
Issue 2
10.3390/foods14020252
foods-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach

by
Samuel Paterson
1,
Laura Alonso-Pintre
1,
Esperanza Morato-López
2,
Sandra González de la Fuente
3,
Pilar Gómez-Cortés
1,* and
Blanca Hernández-Ledesma
1,*
1
Department of Bioactivity and Food Analysis, Institute of Food Science Research (CIAL, CSIC-UAM, CEI UAM+CSIC), Nicolás Cabrera 9, 28049 Madrid, Spain
2
Proteomics Core Facility, Centro de Biología Molecular Severo Ochoa (CBM), CSIC-Universidad Autónoma de Madrid, Nicolás Cabrera 1, 28049 Madrid, Spain
3
Biocomputational Core Facility, Centro de Biología Molecular Severo Ochoa (CBM), CSIC-Universidad Autónoma de Madrid, Nicolás Cabrera 1, 28049 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Foods 2025, 14(2), 252; https://doi.org/10.3390/foods14020252
Submission received: 20 December 2024 / Revised: 10 January 2025 / Accepted: 13 January 2025 / Published: 14 January 2025
(This article belongs to the Special Issue Proteins from Plants or Animals: Enriching the Characteristics of Food)
Browse Figures
Review Reports Versions Notes

Abstract

:
The impact of the world’s growing population on food systems and the role of dietary patterns in the management of non-communicable diseases underscore the need to explore sustainable and dietary protein sources. Although microalgae have stood out as alternative sources of proteins and bioactive peptides, some species such as Nannochloropsis gaditana remain unexplored. This study aimed to characterize N. gaditana’s proteome and evaluate its potential as a source of bioactive peptides by using an in silico approach. A total of 1955 proteins were identified and classified into functional groups of cellular components, molecular functions, and biological processes. In silico gastrointestinal digestion of identified proteins demonstrated that 202 hydrophobic and low-molecular-size peptides with potential bioactivity were released. Among them, 27 exhibited theorical antioxidant, antihypertensive, antidiabetic, anti-inflammatory, and/or antimicrobial activities. Seven of twenty-seven peptides showed ≥20% intestinal absorption, suggesting potential systemic effects, while the rest could act at local level. Molecular docking demonstrated strong affinities with key enzymes such as MPO, ACE, and DPPIV. Resistance to the digestion, capacity to be absorbed, and multifunctionality were demonstrated for peptide FIPGL. This study highlights N. gaditana’s potential as a sustainable source of novel potential bioactive peptides with promising local and systemic biological effects.

1. Introduction

In recent years, non-communicable diseases (NCDs) have been considered a serious disease burden recognized worldwide. In fact, with more than 50% of all deaths being attributable to them, NCDs such as stroke, cancer, ischemic heart disease, chronic kidney disease, and auto-immune and neurodegenerative conditions are now some of the biggest threats and challenges to human health [1]. The main risk factors of NCDs can be classified into the categories of self-management, genetics, medical conditions, and socio-demographic and environmental factors, including diet [2]. Therefore, the role of dietary patterns and evidence-based nutrition interventions in the prevention and management of NCDs should be a global health priority.
Predictive models of the global population have raised concerns about the sustainability of food systems. Among the myriad strategies, a gradual transition from meat-based to plant-based diets is supported and coordinated by the main health and environmental institutions is considered an effective approach [3]. Proteins derived from animal sources are highly valued for their nutritional and functional qualities; however, livestock production significantly impacts the environment, highlighting the importance of balancing all dietary protein sources to meet human needs [4]. Among the different alternative protein sources currently being studied, microalgae are considered one of the most promising due to their optimal nutrition profile, minimal environmental footprint, and significant biological potential [5].
Compared to dry plant seeds, microalgae have been reported to have remarkably higher protein contents, ranging from 30% to 70% of dry weight (with an average value of nitrogen-to-protein conversion factor of 4.78) [6]. In addition, microalgae offer significantly greater protein yield per unit area compared to wheat or beef, owing to their simplified growth processes, reduced energy expenditure, and superior photosynthetic efficiency [7]. Furthermore, microalgal proteins are recognized for their excellent promising functional properties, low antigenicity [8], and high nutritional quality, providing all 20 amino acids (AAs) and varying amounts of essential AAs (EAAs) [9]. The AA composition of many microalgae proteins such as Nannochloropsis gaditana is similar to that of typical plant protein sources, such as soybean [10], and closely aligns with the FAO/WHO/UN reference profile [11]. Thus, microalgal proteins can be regarded as an alternative protein source with nutritional and biological attributes after being broken down in the gastrointestinal tract, liberating EAAs and potential bioactive peptides [12].
Bioactive peptides are small fragments, between 2 and 20 AA, which do not exert any effect within their origin protein but can exert multiple biological properties once released. Different bioactivities have been reported to contain bioactive peptides such as antioxidant, anti-inflammatory, antidiabetic, and immunomodulatory activities, among others [13]. In the case of microalgal proteins, several studies have mentioned their ability to produce bioactive peptides after enzymatic hydrolysis. Thus, proteins from Chlorella vulgaris, Arthrospira platensis, and A. maxima have been demonstrated to generate bioactive peptides with anti-inflammatory and antioxidant activities after their hydrolysis with pepsin, trypsin, or the combination of alcalase and papain [14,15,16]. Similarly, N. oculata has been reported to generate protein hydrolyzates and peptides with angiotensin-converting enzyme (ACE)-1-inhibitory, antithrombotic, and calcium/calmodulin-dependent kinase II (CAMKII)-inhibitory activities [17,18]. Saleh et al. reported that a protein hydrolyzate from N. gaditana with alcalase exerted high in vitro antioxidant activity [19].
Proteases, such as pepsin in the stomach and trypsin and chymotrypsin in the small intestine, play a crucial role in breaking down proteins into smaller fragments. In the case of microalgae, several studies have investigated the release of bioactive peptides from microalgae under simulated gastrointestinal conditions. For example, Li et al. [20] described ACE- and dipeptidyl peptidase (DPP)-IV-inhibitory peptides contained in C. pyrenoidosa digests. The study of Pei et al. [21] identified the antioxidant and anti-inflammatory peptide EMFGTSSET released after simulating gastrointestinal digestion of Isochrysis zhanjiangensis. The susceptibility of other microalga species to the action of digestive enzymes has also been demonstrated, such as for N. oculata [22]. However, to our knowledge, no studies have demonstrated the potential of N. gaditana as a source of bioactive peptides after its gastrointestinal digestion.
The study of bioactive peptides using in vivo, cellular, or animal models is expensive, time-consuming, and might also have ethical considerations. Hence, in silico bioinformatic approaches, which cannot only help to predict the formation, breakdown, and absorption of bioactive peptides from any known protein source but can also help to analyze the structure/function relationship, have gained a new relevance in this scientific field, as they can serve as a screening tool to accelerate and complement the discovery process of potentially encrypted bioactive peptides [23]. Therefore, the goal of this work was to carry out a complete characterization of the microalga N. gaditana’s proteome, and to demonstrate its potential as a source of bioactive peptides through an in silico gastrointestinal digestion strategy, supporting the value of this microalga as a sustainable alternative protein with both nutritional and health-promoting properties.

2. Materials and Methods

2.1. Samples and Reagents

Commercial N. gaditana microalgae biomass was supplied by AlgaEnergy S.A. (Madrid, Spain). Acetonitrile (ACN), trifluoroacetic acid (TFA), calcium chloride (CaCl2), Tris-HCl, formic acid, dithiothreitol (DTT), and iodoacetamide were purchased from Sigma-Aldrich (St. Louis, MO, USA).

2.2. Proteomic Identification and Characterization

2.2.1. Protein Content and AA Profile

The protein concentration of N. gaditana biomass was measured via the bicinchoninic acid (BCA) method, which was performed using the Pierce BCA kit (Thermo Scientific, Waltham, MA, USA) [24]. The standard used was bovine serum albumin (BSA) at concentrations ranging from 50 to 1000 µg/mL. The AA content was determined in duplicate by using a Biochrom 30 series Amino Acid Analyser (Biochrom, Cambridge, UK), and following the previously described methodology [25].

2.2.2. In-Gel Digestion (Stacking Gel)

N. gaditana biomass was suspended in 50 µL of sample buffer and then applied onto 1.2 cm-wide wells of a conventional SDS-PAGE gel (0.75 mm thick, 4% stacking, and 10% resolving; Bio-Rad, Hercules, CA, USA) following the methodology previously reported [26]. The run was stopped when the front entered 3 mm into the resolving gel, so the whole proteome became concentrated in the stacking/resolving gel interface. The unseparated protein bands were visualized by Coomassie staining (Bio-Rad), excised, cut into cubes (2 × 2 mm), and placed in 0.5 mL microcentrifuge tubes [27]. The gel pieces were distained in ACN:H2O (1:1, v:v), reduced and alkylated (disulfide bonds from cysteinyl residues were reduced with 10 mM DTT for 1 h at 56 °C, and then thiol groups were alkylated with 10 mM iodoacetamide for 30 min at room temperature in darkness), and digested in situ with sequencing-grade trypsin (Promega, Madison, WI, USA), as described by Shevchenko et al. [28]. The gel pieces were shrunk by removing all liquid using sufficient ACN. ACN was pipetted out, and the gel pieces were dried in a speedvac and re-swollen in 100 mM Tris-HCl pH 8, 10 mM CaCl2 with 60 ng/mL trypsin at a 5:1 protein/enzyme (w/w) ratio. The tubes were kept in ice for 2 h and incubated at 37 °C for 12 h. Digestion was stopped by the addition of 1% TFA. Whole supernatants were dried down and then desalted using OMIX Pipette tips C18 (Agilent Technologies, Santa Clara, CA, USA) before the analysis. Digestion was conducted in the presence of 0.2% RapiGest (Waters, Milford, MA, USA).

2.2.3. Reverse-Phase Liquid Chromatography (RP-LC-MS/MS) Analysis (Dynamic Exclusion Mode)

The desalted protein digest was dried, resuspended in 10 µL of 0.1% formic acid, and analyzed by RP-LC-MS/MS in an Easy-nLC 1200 system coupled to an ion trap LTQ-Orbitrap-Velos-Pro hybrid mass spectrometer (Thermo Scientific, Waltham, MA, USA). The peptides were concentrated (on-line) by reverse-phase chromatography using a 0.1 mm × 20 mm C18 RP precolumn (Thermo Scientific) and then separated using a 0.075 mm × 250 mm bioZen 2.6 µm Peptide XB-C18 RP column (Phenomenex, Torrance, CA, USA) operating at 0.25 μL/min. Peptides were eluted using a 180 min dual gradient. The gradient profile was set as follows: 5–25% solvent B for 135 min, 25–40% solvent B for 45 min, 40–100% solvent B for 2 min, and 100% solvent B for 18 min (Solvent A: 0.1% formic acid in water; solvent B: 0.1% formic acid, 80% ACN in water). Electrospray ionization (ESI) was performed using a Nano-bore emitters Stainless Steel ID 30 μm (Proxeon, Odense, Denmark) interface at 2.1 kV spray voltage with an S-Lens of 60%. The Orbitrap resolution was set at 30,000 [29]. Peptides were detected in survey scans from 400 to 1600 amu (1 μscan), followed by twenty data-dependent MS/MS scans (Top 20), using an isolation width of 2 u (in mass-to-charge ratio units), normalized collision energy of 35%, and dynamic exclusion applied for 60 s periods. Charge-state screening was enabled to reject unassigned and singly charged protonated ions.

2.2.4. Data Processing

Peptide identification from raw data was carried out using PEAKS Studio v11.5 search engine (Bioinformatics Solutions Inc., Waterloo, ON, Canada). Database search was performed against UniProt-Nannochloropsis gaditana.fasta (15,363 entries); UniProt release 11/2023. The following constraints were used for the searches: tryptic cleavage (semi specific) after arginine (R) and Lysine (K), up to two missed cleavage sites, and tolerances of 20 ppm for precursor ions and 0.6 Da for MS/MS fragment ions. The searches were performed allowing for optional methionine (M) oxidation and cysteine (C) carbamidomethylation. False discovery rates (FDRs) for peptide–spectrum matches (PSMs) and proteins were limited to 0.01. Only those proteins with at least two unique peptides being discovered from LC/MS/MS analyses were considered reliably identified [27,30,31,32].

2.3. Proteomic Functional Analysis

The proteomic functional analysis of the identified proteins from N. gaditana biomass was carried out using the functional analysis module from OmicsBox 3.3.2 bioinformatic software (Biobam, Valencia, Spain) through the BLAST2GO methodology [33]. The identified proteins were classified into three different functional groups: cellular component, molecular function, and biological process. The database used to perform the alignment was Non-redundant protein sequences (nr v5), filtered by taxonomy: N. gaditana (code 72520). The specific workflow followed for the functional analysis is shown in Supplementary Figure S1.

2.4. In Silico Gastrointestinal Digestion of Microalga Proteins

The in silico gastrointestinal digestion of proteins was performed using the Rapid Peptides Generator (RPG) software v22.2.3 [34]. By using the Python (3.12) system, the complete data obtained from the proteomic identification were entered in fasta format, and firstly digested with pepsin (code 34) to simulate gastric digestion. Afterward, only peptides released from proteins that generated ≥100 peptides/protein after the gastric phase were selected to be concurrently digested with trypsin (code 42) and chymotrypsin high specificity (code 15). In all in silico gastric and gastrointestinal digests, free AAs and duplicate peptides were deleted, and the resulting peptides were classified based on their size and AA sequence.

2.5. Bioactivity Prediction by In Silico Analysis

For the prediction of the bioactivity, an in silico analysis of the peptides released during the gastric and gastrointestinal phases was conducted using the server based on a novel N-to-1 neural network Peptide Ranker [35]. Peptides with ≥0.8 value of the probability of being bioactive were selected to estimate their (i) antioxidant activity by using the Antioxidative peptide predictor (AnOxPP) database [36], (ii) anti-inflammatory activity using the PreAIP database [37], (iii) antimicrobial activity using the CAMPR4 database [38], (iv) antihypertensive activity using the AHTpin database [39], and (v) DPP-IV inhibitory activity by using the StackDPPIV database [40].

2.6. Physicochemical and Pharmacokinetic Analysis

The peptides with 4 or more predicted bioactivities were selected, and SwissADME molecular sketcher, based on ChemAxon’s Marvin JS 24.3.0 (re389bc4da912), 17 July 2024, was used to obtain the canonical SMILE for each peptide and to predict the physicochemical and pharmacokinetic properties such as molecular formula, weight, lipophilicity, water solubility, bioavailability score, AMES toxicity, and the percentage of human intestinal absorption through the SwissADME web tool (http://www.swissadme.ch/) [41,42].

2.7. Peptide Molecular Docking

The peptide molecular interactions with human angiotensin-converting enzyme (ACE), DPP-IV, and myeloperoxidase (MPO) enzymes were predicted using molecular docking. The crystal structure for ACE (2C6N), DPP-IV (1NU8), and MPO (3F9P) was obtained from a protein data bank (http://www.rcsb.org/) [43]. Peptides were designed using Marvin Sketch software (Chemaxon, version 19.22.0); flexible torsions, charges, and grid sizes were assigned using Autodock Tools; and docking calculations were performed using AutoDock Vina, where the binding pose with the lowest binding energy was selected as representative to visualize in the Discovery Studio 2016 Client (Dassault Systemes Biovia Corp R), as was previously described [44,45].
To clarify the experimental design, the workflow of the present research is summarized in Figure 1.

3. Results and Discussion

3.1. Proteome of the Microalga Nannochloropsis gaditana

The protein content of N. gaditana biomass was 21.69%. Values reported in the literature ranged from 19–24% for N. oculata [22] to 44.9% for N. gaditana [46]. Levasseur et al. [47] also compiled intermediate percentages for this genus. The protein content of Nannochloropsis sp. microalgae has been demonstrated to be affected by numerous biotic and abiotic factors and genomic variation among species [48]. These factors can also impact the AA profile and, consequently, the protein quality [49]. The AA profile of the N. gaditana used in the present research is shown in Table 1. All AAs were analyzed except tryptophan, as the methodology conditions degraded it. The measurement showed a high AA content, leucine being the major EAA, followed by lysine and phenylalanine. Among non-essential AAs (NEAAs), glutamic acid was the most predominant, followed by aspartic acid and proline. Comparing the results obtained with the recommendations of the FAO/WHO/United Nations University (UNU) report (2007), the requirements established for each AA were covered [50]. Recent studies have provided insights into the factors contributing to the high concentration of glutamic acid in microalgae, including Nannochloropsis sp. The findings suggest that glutamic acid’s predominance might be linked to its central role in nitrogen metabolism and assimilation through the glutamate–glutamine cycle; its function as a precursor of other amino acids like glutamine, proline, and arginine; its role in stress responses to environmental conditions by acting as an osmotic regulator that helps to stabilize cellular structures during such stress responses; and its role in the carbon and nitrogen balance of Nannochloropsis sp., which are high biomass producers that might optimize nitrogen assimilation through the accumulation of glutamic acid [51,52,53].
After the in-gel digestion and consequent RP-LC-MS/MS analysis of N. gaditana biomass, by using PEAKS and SPIDER search tools [54], a total of 13,097 peptide–spectrum matches (PSMs), 12,574 scans (which account for MS/MS scans that have a peptide spectrum associated with them), 6991 features identified with database search only, 6903 peptide sequences with modifications not including isoleucine/leucine differentiation, and 5820 features without modifications and isoleucine/leucine differentiation were identified.
A total of 1955 proteins were detected through PEAKS software. Since sequences constituted the key for homology-based protein identification, PEAKS works with protein groups that contain all the proteins that have been identified with the same peptides [26,55]. In our case, 1439 protein groups were identified. Of 1955 proteins, 1258 proteins were identified with two or more unique peptides. Their correspondent accession number, −10logP value, number of peptides and unique peptides, the type of modification present in the peptide (PTM), the average mass, and the description of all proteins detected in N. gaditana biomass were collected and made available in the Supplementary Table S1.
N. gaditana’s proteome was first studied by Fernández-Acero et al. [56], identifying 1950 proteins. The differences found in comparison with our results could be due to factors such as the strain and the growing conditions, which have been demonstrated to affect microalgae’s macronutrient distribution [57]. Hulatt et al. [58] identified 3423 proteins in N. gaditana biomass, where the abundance of 1543 and 113 of them was demonstrated to be influenced by the amount of nitrogen and phosphorous available in the culture conditions, respectively. In general, the limited count of proteins and peptides identified by the database search should be attributed to the incompleteness of the sequenced N. gaditana proteome and the differences in the experimental procedures. Thus, mapping the N. gaditana proteome may aid to understand variables affecting its proteins and to explore their properties as sources of bioactive peptides.

3.2. Functional Analysis of Nannochloropsis gaditana Proteome

To elucidate the functionality of the identified proteins in N. gaditana, an alignment of the protein sequences detected was carried out using the BLAST2GO methodology, classifying proteins with differentiated biological functions into three groups: biological process, cellular component, and molecular function. The functional distribution of the identified proteins at levels 1 and 2 of each functional group are shown in Figure 2, and the detailed complete level distribution of each function is available in Supplementary Figures S2–S4.
The distribution of proteins at level 1 was quite homogeneous, since the percentages associated with each function were similar (Figure 2A), with 1309 sequences for biological process (31.56%), 1424 sequences for molecular functions (34.44%), and 1415 sequences for cellular components (31.56%). However, within the sequences associated with cellular components at level 2 (Figure 2B), a noticeable difference was observed since the majority were found within cellular entities (74.13%) and not forming complexes (25.87%). Regarding cellular entities, intracellular anatomical structures and organelles were the most abundant groups (Supplementary Figure S1). Concerning the biological process group level 2 classification (Figure 2C), protein sequences related to cellular processes were the most abundant (66.24%), in contrast with proteins related to localization, response to stimulus, and regulation processes. Regarding those cellular processes, metabolic processes in which energy is obtained from the metabolization of organic substances were the most abundant. Finally, concerning molecular function level 2 classification (Figure 2D), the major percentage of sequences were associated with binding processes (47.30%) and catalytic activities (44.32%) which are related to proteins associated with cyclic organic compounds, necessary for photosynthesis, mobilizing metabolites, and building structural components, such as the cell wall, among other previously studied functions [59]. Our results were in tune with the GO classification conducted by Fernández-Acero et al. [56] and Hulatt et al. [49] as the major percentage of proteins that belonged to the biological process group where related to biosynthetic processes, carbon fixation, ATP synthesis, and AA regulation, and the major percentage of proteins who belonged to the molecular process group were related with ion binding processes.

3.3. In Silico Gastrointestinal Digestion

The in silico gastrointestinal digestion of the complete set of 1955 proteins detected through PEAKS software v11.5 was conducted. This allowed the inclusion of proteins that could be underrepresented or absent due to technical limitations, such as low abundance, suboptimal extraction methods, or inefficient ionization in mass spectrometry analysis [60]. In this way, we would be ensuring that even proteins with marginal or undetected peptides were considered, potentially uncovering biologically relevant or functional insights [61]. Moreover, the additional 697 proteins with fewer than two unique peptides may still hold critical roles in metabolic pathways or cellular processes but be overlooked in traditional proteomics workflows. Therefore, by considering the entire dataset, researchers can identify proteins with potential bioactivity, nutritional value, or industrial applications that might otherwise be disregarded. Still, it should be noted that many organisms produce bioactive peptides in cellulo prior to any further gastrointestinal digestion, which sometimes are even resistant to the action of digestive enzymes [62]. Given the experimental limitations of the present work, the inherent bioactive peptides of N. gaditana were not identified, although their potential contribution to the bioactive effects of this microalga should not be discarded.
Consequently, an in silico gastric digestion with pepsin was initially performed, generating a total of 164,503 sequences that, after removing free AAs and duplicate peptides, resulted in 47,982 sequences. Then, the number of peptides generated from each identified protein was counted, and 24 proteins were selected as the most peptide-generating proteins (with ≥100 peptides/protein) after the gastric phase (Table 2).
The average mass of the selected proteins ranged from 118 to 552 kDa. The three most peptide-generating proteins were zinc finger ZZ-type (which generated 376 peptides), acetyl CoA carboxylase (207 peptides), and bromodomain containing 1 (169 peptides), the average masses of which were slightly higher than the rest. These three proteins have been previously described in other microalgae species such as Schizochytrium sp., C. vulgaris, and Chlamydomonas reinhardtii, mainly exhibiting a regulatory role in microalgae’s lipid metabolism and biosynthesis [63,64,65,66]. In addition, Lin et al. [67] specifically highlighted the involvement of acetyl CoA carboxylase in the type II fatty acid synthesis pathway, which occurs in the plastids of the cells of N. gaditana. These proteins would be of great importance since the microalga N. gaditana has aroused great interest due to its high lipid content and its potential as a sustainable source of omega-3 fatty acids.
Proteins’ susceptibility to the action of pepsin can be influenced by several structural and chemical factors, such as protein molecular weight, AA composition, and acidic conditions, among others. Pepsin is a protease that specifically cleaves peptide bonds at the amino-terminal side of hydrophobic and aromatic residues sites such as phenylalanine, tyrosine, and leucine, breaking polypeptide chains into smaller peptides [68]. Generally, larger proteins commonly provide more cleavage sites for pepsin due to their longer polypeptide chains [69]. Therefore, the susceptibly of the three proteins with most peptides generated could be explained by their high average mass (552, 235, 245 kDa, respectively) and the number of hydrophobic AAs found in N. gaditana biomass, such as leucine or phenylalanine. In addition, in the RPG software v22.2.3, pepsin was selected at pH 1.3 (code 33) in attempt to confirm its high cleavage affinity and most optimal conditions. Nevertheless, information regarding N. gaditana protein susceptibility to pepsin is still scarce and should be further explored.
The selected proteins generated a total of 3160 peptides during the gastric phase. This number was experimentally affordable and representative of the total identified peptides, although the potential contribution of other peptides should also be taken into account. Among them, 104 were predicted by the Peptide Ranker software tool (http://distilldeep.ucd.ie/PeptideRanker/) to be active with a score ≥ 0.8. These peptides were analyzed by using the AnOxPP, PreAIP, CAMPR4, AHTpin, and StackDPPIV databases to predict their antioxidant, anti-inflammatory, antimicrobial, antihypertensive, and antidiabetic activities, respectively. Three peptides, FHPKF, FIPGL, and FRPLPPLA, were predicted to potentially exert the five bioactivities assayed, while two sequences, PLPAGRF and HHDCF, were predicted to have four of those bioactivities. In addition, a second phase simulating intestinal digestion was conducted on 3160 peptides generated during the gastric phase, using trypsin and chymotrypsin as pancreatic enzymes. A total of 6224 peptides were generated after eliminating free AAs and duplicates. Of these peptides, 54 were also present in the in silico gastric digestion, indicating their capacity to resist the action of trypsin and chymotrypsin. After being analyzed with the Peptide Ranker software tool, 202 of 6224 peptides showed a score ≥ 0.8. The peptide profile is shown in Figure 3.
Most of the potentially bioactive peptides released after the complete gastrointestinal digestion were tripeptides (88), followed by tetrapeptides (47) and pentapeptides (39) (Figure 3A). The major AAs present were phenylalanine (F) followed by proline (P), arginine (R), and glycine (G) (Figure 3B). The high presence of phenylalanine (F) and proline (P) observed in the in silico gastrointestinal digestion agreed with the high content of these AAs present in the N. gaditana biomass (Table 1). It has been previously described that numerous bioactive peptides present a structure–activity relation, being a key part of their potential biological effects [70,71]. In tune with our results, in general and especially in the case of the antioxidant and ACE inhibitory activities, it has been described that active fragments are either dipeptides or tripeptides containing hydrophobic AAs, such as phenylalanine or proline [72,73]. In the case of microalgae-derived bioactive peptides, several microalgae species like Chlorella sp., Navicula sp. and Arthorspira sp. have been described as sources of small bioactive peptides with hydrophobic AAs [74,75], which suggests that N. gaditana could also have the potential to become a source of bioactive peptides. After the in silico digestion and bioactivity prediction analysis, from 202 peptides, 2 sequences (FIPGL and YLPPR) were predicted to exert five activities, while 25 sequences showed four of five activities (Table 3).
Regarding the potentially bioactive dipeptides generated, a search of their sequences in the BIOPEP-UWM database [76] showed that the dipeptide FK was previously described in sweet potato hydrolyzates and associated with ACE inhibitory and anti-hyperuricemic properties [77,78]. The dipeptide FR has also been described to display ACE and DPPIV inhibitory actions [79,80], as it has been demonstrated for the GF sequence [80,81]. ACE inhibitory effects were also observed for RF peptide [82,83]. In the case of the tripeptide ARF, it has been recently described that this specific sequence can not only inhibit ACE, but also its homologue, ACE2 [84]. Regarding the rest of the peptides, a search in the Uniprot Database reveled that FIPGL could be found in the protein “Glycoprotein G” of human herpesvirus 2 (strain HG52; UniProtKB entry P10211) and that the peptides KSPGW, YLPPR, and FLPPAL were present in the protein “Putative uncharacterized protein” from Bacillus subtilis (strain 168; UniProtKB entry P37595). However, no data about their bioactivity have been reported yet, which reinforces the potential of N. gaditana as a sustainable source of both known and new bioactive peptides. The predicted bioactivity of peptides may be influenced by the positions of AAs within their sequence. For instance, the placement of hydrophobic or branched-chain AAs at the C-terminal or the presence of aliphatic AAs at the N-terminal can enhance radical scavenging of reactive oxygen species or the ACE-inhibitory activity of the peptides [85].
Table 3. Physicochemical and pharmacokinetic properties of peptides released from in silico gastrointestinal digestion of Nannochloropsis gaditana and predicted to have 4 or more bioactivities.
Table 3. Physicochemical and pharmacokinetic properties of peptides released from in silico gastrointestinal digestion of Nannochloropsis gaditana and predicted to have 4 or more bioactivities.
PeptideMolecular Weight (Da)Lipophilicity (MLogP) aBioavailability Score bWater Solubility
(log mol/L) c		% Intestinal Absorption dAMES
Toxicity e
FK293.360.60.55−2.81835.3No
FR321.370.150.55−2.64321.61No
GF222.240.340.55−1.8541.89No
RF321.370.140.55−2.61721.43No
ARF 160.06−1.60.550.82471.496No
DPMP458.53−1.110.11−2.270No
FHPR555.63−1.460.17−2.8756.011No
FSPR505.57−1.560.17−2.8350No
HPKF527.62−1.110.17−2.81517.04No
MPPR499.63−1.010.17−27846.78No
VPGF418.49−0.10.55−2.52428.49No
APMRP570.71−1.530.17−2.9220No
FIPGL 1,2545.670.150.17−3.17421.02No
GPGCG389.43−2.730.55−2.5162.96No
KAPPF558.67−0.60.17−2.83316.56No
KSPGW1573.64−2.170.17−2.8620.134No
PCMIR618.81−1.320.17−2.8890No
PFGNR589.64−2.490.17−2.8890No
PRPMR655.81−1.980.17−2.8890No
RRCLF 1693.86−1.220.17−2.8940No
WWGGV603.67−0.740.17−3.01519.76No
YLPPR2644.76−0.840.17−2.90419.333No
AVMPIF676.87−0.070.17−3.01211.265No
EFPMIR791.96−1.310.17−2.8940No
FARPGL 1659.78−1.510.17−2.9220No
FGPQGG 1561.59−2.770.17−2.9770.112No
FLPPAL 1656.810.120.17−3.2917.79No
a Lipophilicity expressed as the partition coefficient between octanol and water [86,87,88]. b Bioavailability score expressed as the probability of F > 10% in rats [89]. c Solubility of the molecule in water at 25 °C. d Percentage of the molecules that would be absorbed through the human intestine [90]. e Mutagenic potential of the molecule in bacteria [90]. 1 Peptides resistant to in silico intestinal digestion. 2 Peptides predicted to have all 5 tested bioactivities.
In our case, six peptides contained phenylalanine (FHPR, FSPR, FIPGL, FARPGL, FGPQGG, and FLPPAL) and three peptides contained proline (PCMIR, PFGNR, and PRPMR) at the N-terminal residue. Moreover, peptides with phenylalanine contained other hydrophobic AAs at the C-terminal, such as leucine (L) and glycine (G), suggesting that the positions of the AAs in the sequence could be a key parameter behind the predicted bioactivities.
In silico proteolysis does not guarantee that the resultant bioactive peptides are safe for use in food and pharmaceutical applications. For this reason, we conducted a physicochemical and pharmacokinetic analysis of the selected 27 peptides (Table 3). The SwissADME evaluation provides information on the likelihood that a molecule can be safely used as an oral drug. Ideally, the molecular mass of a potential drug should be less than 500 Da, its lipophilicity should range from −0.7 to 5.0, and its water solubility should not exceed 6 [42]. As can be observed in Table 3, all dipeptides met these criteria and presented a bioavailability score of 0.55, suggesting that these peptides could potentially be absorbed through the intestinal tract. In addition, the other three peptides (ARF, VPGF, and FIPGL) showed intestinal absorption values higher than 20%. One of them, ARF, showed the highest absorption value (71.49%), although its lipophilicity value was not within the ideal range. FIPGL was the potentially bioactive peptide with the largest sequence, and its intestinal absorption value was higher than 20%, meeting SwissADME of drug-likeness criteria. The rest of the peptides exhibited poor intestinal absorption values, and some were predicted to be incapable of being absorbed through the intestinal tract (Table 3). However, these peptides should not be discarded since they could display bioactive effects locally over the cells and tissues of the gastrointestinal tract without being absorbed into the bloodstream [91]. None of the 27 peptides analyzed showed any AMES toxicity.

3.4. Molecular Docking of Nannochloropsis gaditana Potential Bioactive Peptides

Since in silico analysis only predicted the potential bioactive peptides released after simulated gastrointestinal digestion of N. gaditana proteins, without analyzing their interactions with biological targets, we decided to evaluate the potential mechanism of action through molecular docking of the ACE, DPPIV, and MPO enzymes as a target-specific analysis for the antihypertensive, antidiabetic, and antioxidant activities. Seven peptides showing an intestinal absorption value higher than 20% (GF, FK, FR, RF, ARF, VPGF, and FIPGL) were selected. The Gibbs free energy (ΔG) quantifies the thermodynamic feasibility of a ligand binding to a target, and it is a central parameter related to binding affinity, where the interaction with the lowest ΔG is typically considered the most stable and biologically relevant position [92]. All the dipeptides and the ARF tripeptide showed high binding affinity with the three enzymes tested. Gibbs free energy values ranged from −10.7 to −7.9 kcal/mol for ACE, from −6.7 to −1.7 kcal/mol for DPPIV, and from −8 to −6.6 kcal/mol for MPO, suggesting that they might display their corresponding potential antioxidant, antidiabetic, and antihypertensive activities through the inhibition of these well-described enzymes at a systemic level. In the case of VPGF and FIPGL, both peptides showed high binding affinities for MPO and ACE. The molecular interactions of FIPGL with ACE and MPO enzymes are shown in Figure 4.
FIPGL peptide demonstrated to be resistant to the action of trypsin and chymotrypsin and potentially capable of exerting multifunctional properties, making it an interesting peptide to be further explored. In the case of MPO (Figure 4A,B), FIPGL showed high MPO binding affinity, with a Gibbs free energy value of −9.6 kcal/mol. The peptide–enzyme interaction would be through conventional hydrogen bonds (I160, N162), alkyl and Pi-alkyl interactions (I160, R31, A35), carbon hydrogen bonds (D321), as well as van de Walls forces (Figure 4B). In the human body, MPO normally interacts with hydrogen peroxide (H2O2) released by the phagocytic cells when they encounter foreign particles, forming a highly reactive enzyme–substrate complex [93]. The elevated MPO activity has been linked to various pathological conditions, increasing the risk of oxidative stress, and previous antioxidant peptides have been developed to use MPO as a therapeutic target [94]. Therefore, the demonstrated interaction between FIPGL and MPO might influence the catalytic turnover or substrate access of MPO, favoring a decrease in its oxidative capacity. This could be a possible mechanism of action behind FIPGL’s in silico predicted antioxidant activity. Nevertheless, other mechanisms of action such as neutralizing ROS, chelating pro-oxidant metal ions, or modulating endogenous antioxidant defense systems could be also behind the predicted antioxidant activity.
In relation to ACE (Figure 4C,D), FIPGL also showed high binding affinity with a Gibbs free energy value of −8.5 kcal/mol. In Figure 4D, FIPGL aminoacidic interactions with ACE were mainly mediated by hydrogen bonds (L375, E376, T301, T302, K449, S298, and Y287), alkyl and Pi-alkyl interactions (V379, L427, L433), and van de Walls forces.
ACE converts angiotensin-I into angiotensin-II, a vasoconstrictor, and its overproduction is one of the main causes of high blood pressure in patients with hypertension. Thus, suitable bioactive food-derived peptides have been studied as inhibitors of this enzyme and natural therapeutic alternatives [95]. The presence and positioning of hydrophobic AA residues such as isoleucine, phenylalanine, or proline would be a crucial structural feature for the ACE inhibitory potential [96]. A closer look regarding the structure of ACE has revealed that the active site consists of a zinc coordination site, several hydrogen bonding sub-sites, a proline binding pocket, and a hydrophobic pocket (with “F” residues) with a preference for aromatic AAs like phenylalanine [97]. Consequently, it is imperative for a potent peptide inhibitor to have a hydrophobic residue with an aromatic side chain, such as phenylalanine and proline in its sequence for effective binding. Therefore, FIPGL could be considered a potentially strong ACE inhibitory peptide. Our results were in tune with previous works carried out with other Nannochloropsis species like N. oculata that have been reported to generate protein hydrolyzates and peptides with ACE1 inhibitory properties [17,98]. Still, quantitative structure–activity relationship models of FIPGL and ACE should be developed to further explore the mechanisms of action behind the predicted antihypertensive activity. Finally, regarding the DPPIV, both peptides did not show any binding affinity, obtaining positive Gibbs free energy. This result suggests that the predicted antidiabetic activity of these peptides could be related to other pathways, such as enhancement of insulin secretion, mimicking of incretin hormones, and/or inhibition of key enzymes involved in carbohydrate digestion, such as α-amylase and α-glucosidase.
Furthermore, in addition to the bioactivities evaluated in the present work, the results underscore different potential applications of N. gaditana proteins across industries. While N. gaditana is primarily known for its potential as a source of biofuels, the peptides it produces could also play a role in environmental remediation, as peptides with potential antimicrobial or antioxidant properties could be used in bioremediation processes to clean up pollutants or degrade toxic substances in marine environments [99]. In addition to being valuable ingredients for human-health-promoting products, The antioxidant bioactive peptides of N. gaditana could also be considered interesting for the formulation of products in the cosmetics and skincare industry or to enhance the growth and health of fish and other marine organisms, highlighting a potential role in the animal feed and aquaculture industry, too [100]. Overall the multifaced nature of N. gaditana biomass demonstrates its importance not only as a sustainable biomass source but also as a valuable component in various industries aimed at improving health, sustainability, and environmental protection.

4. Conclusions

Our proteomic analysis was able to deepen the scarce knowledge available on the N. gaditana proteome. A total of 1955 proteins were identified, of which 1258 were identified with two or more unique peptides. The functional analysis of identified proteins demonstrated that most of them were related to cellular anatomic structures and cellular and binding processes. Furthermore, in silico gastrointestinal digestion and bioactivity prediction revealed the release of small-sized peptides mainly composed of hydrophobic AAs, like phenylalanine and proline, with multiple potential biological activities. The SwissADME analysis showed that these peptides could exert their predicted bioactivities at both local and/or systemic level. Additionally, the molecular docking of potentially absorbable peptides showed high molecular affinity with MPO, ACE, and DPPIV enzymes. It was especially remarkable in the case of the FIPGL peptide, which showed resistance to the action of digestive enzymes, the capability to be absorbed through the gastrointestinal tract, and multifunctionality. This peptide demonstrated strong affinities for MPO and ACE, potential targets of its predicted biological action. Overall, our proteomic and in silico approach suggests, for the first time, the potential of N. gaditana as a sustainable source of bioactive peptides with multiple health benefits and potential applications. Therefore, further in vitro assays are needed to confirm the release of the sequences identified by the in silico and docking approaches and their predicted bioactivities.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/foods14020252/s1, Figure S1: Complete Omicbox workflow followed for the proteomic functional analysis of Nannochloropsis gaditana. Figure S2: Complete functional distribution of detected proteins from Nannochloropsis gaditana biomass in cellular component functional group using gene ontology (GO). Figure S3: Complete functional distribution of detected proteins from Nannochloropsis gaditana biomass in biological process functional group using gene ontology (GO). Figure S4: Complete functional distribution of detected proteins from Nannochloropsis gaditana biomass in molecular function functional group using gene ontology (GO). Table S1. Identified proteins from Nannochloropsis gaditana.

Author Contributions

Conceptualization, P.G.-C. and B.H.-L.; methodology, S.P., L.A.-P. and E.M.-L.; software, S.G.d.l.F.; formal analysis, S.P. and L.A.-P.; investigation, S.P., L.A.-P. and E.M.-L.; writing—original draft preparation, S.P. and L.A.-P.; writing—review and editing, E.M.-L., P.G.-C. and B.H.-L.; supervision, P.G.-C. and B.H.-L.; funding acquisition, P.G.-C. and B.H.-L. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported through Project PID2021-122989OB-I00 financed by MICIU/AEI/10.13039/501100011033 and FEDER, EU. Samuel Paterson gratefully acknowledges the Comunidad Autónoma de Madrid for a predoctoral contract (PIPF-2022/BIO-24996).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Bennett, J.M.; Reeves, G.; Billman, G.E.; Sturmberg, J.P. Inflammation–Nature’s Way to Efficiently Respond to All Types of Challenges: Implications for Understanding and Managing “the Epidemic” of Chronic Diseases. Front. Med. 2018, 5, 316. [Google Scholar] [CrossRef] [PubMed]
  2. Budreviciute, A.; Damiati, S.; Sabir, D.K.; Onder, K.; Schuller-Goetzburg, P.; Plakys, G.; Katileviciute, A.; Khoja, S.; Kodzius, R. Management and Prevention Strategies for Non-Communicable Diseases (NCDs) and Their Risk Factors. Front. Public Health 2020, 8, 574111. [Google Scholar] [CrossRef] [PubMed]
  3. Floret, C.; Monnet, A.F.; Micard, V.; Walrand, S.; Michon, C. Replacement of Animal Proteins in Food: How to Take Advantage of Nutritional and Gelling Properties of Alternative Protein Sources. Crit. Rev. Food Sci. Nutr. 2023, 63, 920–946. [Google Scholar] [CrossRef] [PubMed]
  4. Calvez, J.; Azzout-Marniche, D.; Tomé, D. Protein Quality, Nutrition and Health. Front. Nutr. 2024, 11, 1406618. [Google Scholar] [CrossRef]
  5. Zhuang, D.; He, N.; Khoo, K.S.; Ng, E.P.; Chew, K.W.; Ling, T.C. Application Progress of Bioactive Compounds in Microalgae on Pharmaceutical and Cosmetics. Chemosphere 2022, 291, 132932. [Google Scholar] [CrossRef]
  6. Mosibo, O.K.; Ferrentino, G.; Udenigwe, C.C. Microalgae Proteins as Sustainable Ingredients in Novel Foods: Recent Developments and Challenges. Foods 2024, 13, 733. [Google Scholar] [CrossRef]
  7. Chronakis, I.S.; Madsen, M. Algal Proteins. In Handbook of Food Proteins; Woodhead Publishing: Cambridge, UK, 2011; pp. 353–394. [Google Scholar] [CrossRef]
  8. Rzymski, P.; Budzulak, J.; Niedzielski, P.; Klimaszyk, P.; Proch, J.; Kozak, L.; Poniedziałek, B. Essential and Toxic Elements in Commercial Microalgal Food Supplements. J. Appl. Phycol. 2019, 31, 3567–3579. [Google Scholar] [CrossRef]
  9. Spolaore, P.; Joannis-Cassan, C.; Duran, E.; Isambert, A. Commercial Applications of Microalgae. J. Biosci. Bioeng. 2006, 101, 87–96. [Google Scholar] [CrossRef]
  10. Guil-Guerrero, J.L.; Navarro-Juárez, R.; López-Martínez, J.C.; Campra-Madrid, P.; Rebolloso-Fuentes, M.M. Functional Properties of the Biomass of Three Microalgal Species. J. Food Eng. 2004, 65, 511–517. [Google Scholar] [CrossRef]
  11. Medina, C.; Rubilar, M.; Shene, C.; Torres, S.; Verdugo, M. Protein Fractions with Techno-Functional and Antioxidant Properties from Nannochloropsis Gaditana Microalgal Biomass. J. Biobased Mater. Bioenergy 2015, 9, 417–425. [Google Scholar] [CrossRef]
  12. Zaky, A.A.; Paterson, S.; Gómez-Cortés, P.; Hernández-Ledesma, B. Bioactive Peptides Released from Microalgae during Gastrointestinal Digestion. In Protein Digestion-Derived Peptides; Elsevier: Amsterdam, The Netherlands, 2024; pp. 335–352. [Google Scholar] [CrossRef]
  13. Du, C.; Gong, H.; Zhao, H.; Wang, P. Recent Progress in the Preparation of Bioactive Peptides Using Simulated Gastrointestinal Digestion Processes. Food Chem. 2024, 453, 139587. [Google Scholar] [CrossRef] [PubMed]
  14. Sheih, I.C.; Wu, T.K.; Fang, T.J. Antioxidant Properties of a New Antioxidative Peptide from Algae Protein Waste Hydrolysate in Different Oxidation Systems. Bioresour. Technol. 2009, 100, 3419–3425. [Google Scholar] [CrossRef] [PubMed]
  15. Wang, Z.; Zhang, X. Isolation and Identification of Anti-Proliferative Peptides from Spirulina Platensis Using Three-Step Hydrolysis. J. Sci. Food Agric. 2017, 97, 918–922. [Google Scholar] [CrossRef] [PubMed]
  16. Wang, X.; Zhang, X. Separation, Antitumor Activities, and Encapsulation of Polypeptide from Chlorella Pyrenoidosa. Biotechnol. Prog. 2013, 29, 681–687. [Google Scholar] [CrossRef]
  17. Hayes, M.; Mora, L.; Lucakova, S. Identification of Bioactive Peptides from Nannochloropsis Oculata Using a Combination of Enzymatic Treatment, in Silico Analysis and Chemical Synthesis. Biomolecules 2022, 12, 1806. [Google Scholar] [CrossRef]
  18. Vo, T.S.; Ryu, B.M.; Kim, S.K. Purification of Novel Anti-Inflammatory Peptides from Enzymatic Hydrolysate of the Edible Microalgal Spirulina Maxima. J. Funct. Foods 2013, 5, 1336–1346. [Google Scholar] [CrossRef]
  19. Saleh, N.I.M.; Ghani, W.A.K.; Azlina, W.; Mohd Razif, H.; Kamal, M.; Mazlina, S. Optimization of Enzymatic Hydrolysis for the Production of Antioxidative Peptide from Nannochloropsis gaditana using Response Surface Methodology. Pertanika J. Sci. Technol. 2019, 27, 41–55. [Google Scholar] [CrossRef]
  20. Li, Y.; Aiello, G.; Fassi, E.M.A.; Boschin, G.; Bartolomei, M.; Bollati, C.; Roda, G.; Arnoldi, A.; Grazioso, G.; Lammi, C. Investigation of Chlorella Pyrenoidosa Protein as a Source of Novel Angiotensin I-Converting Enzyme (Ace) and Dipeptidyl Peptidase-Iv (Dpp-Iv) Inhibitory Peptides. Nutrients 2021, 13, 1624. [Google Scholar] [CrossRef]
  21. Pei, Y.; Cai, S.; Ryu, B.; Zhou, C.; Hong, P.; Qian, Z.J. An ACE Inhibitory Peptide from Isochrysis Zhanjiangensis Exhibits Antihypertensive Effect via Anti-Inflammation and Anti-Apoptosis in HUVEC and Hypertensive Rats. J. Funct. Foods 2022, 92, 105061. [Google Scholar] [CrossRef]
  22. Cavonius, L.R.; Albers, E.; Undeland, I. In Vitro Bioaccessibility of Proteins and Lipids of PH-Shift Processed Nannochloropsis Oculata Microalga. Food Funct. 2016, 7, 2016–2024. [Google Scholar] [CrossRef]
  23. Peredo-Lovillo, A.; Hernández-Mendoza, A.; Vallejo-Cordoba, B.; Romero-Luna, H.E. Conventional and in Silico Approaches to Select Promising Food-Derived Bioactive Peptides: A Review. Food Chem. X 2021, 13, 100183. [Google Scholar] [CrossRef] [PubMed]
  24. Paterson, S.; Fernández-Tomé, S.; Galvez, A.; Hernández-Ledesma, B. Evaluation of the Multifunctionality of Soybean Proteins and Peptides in Immune Cell Models. Nutrients 2023, 15, 1220. [Google Scholar] [CrossRef] [PubMed]
  25. Official Methods of Analysis, 22nd Edition (2023)—Aoac International. Available online: https://www.aoac.org/official-methods-of-analysis/ (accessed on 23 February 2024).
  26. Torres-Sánchez, E.; Morato, E.; Hernández-Ledesma, B.; Gutiérrez, L.F. Proteomic Analysis of the Major Alkali-Soluble Inca Peanut (Plukenetia volubilis) Proteins. Foods 2024, 13, 3275. [Google Scholar] [CrossRef] [PubMed]
  27. Sanchiz, Á.; Morato, E.; Rastrojo, A.; Camacho, E.; la Fuente, S.G.; Marina, A.; Aguado, B.; Requena, J.M. The Experimental Proteome of Leishmania Infantum Promastigote and Its Usefulness for Improving Gene Annotations. Genes 2020, 11, 1036. [Google Scholar] [CrossRef]
  28. Shevchenko, A.; Wilm, M.; Vorm, O.; Mann, M. Mass Spectrometric Sequencing of Proteins Silver-Stained Polyacrylamide Gels. Anal. Chem. 1996, 68, 850–858. [Google Scholar] [CrossRef]
  29. Alonso, R.; Pisa, D.; Marina, A.I.; Morato, E.; Rábano, A.; Rodal, I.; Carrasco, L. Evidence for Fungal Infection in Cerebrospinal Fluid and Brain Tissue from Patients with Amyotrophic Lateral Sclerosis. Int. J. Biol. Sci. 2015, 11, 546. [Google Scholar] [CrossRef]
  30. Tran, N.H.; Qiao, R.; Xin, L.; Chen, X.; Liu, C.; Zhang, X.; Shan, B.; Ghodsi, A.; Li, M. Deep Learning Enables de Novo Peptide Sequencing from Data-Independent-Acquisition Mass Spectrometry. Nat. Methods 2019, 16, 63–66. [Google Scholar] [CrossRef]
  31. Tran, N.H.; Zhang, X.; Xin, L.; Shan, B.; Li, M. De Novo Peptide Sequencing by Deep Learning. Proc. Natl. Acad. Sci. USA 2017, 114, 8247–8252. [Google Scholar] [CrossRef]
  32. Tran, N.H.; Rahman, M.Z.; He, L.; Xin, L.; Shan, B.; Li, M. Complete De Novo Assembly of Monoclonal Antibody Sequences. Sci. Rep. 2016, 6, 31730. [Google Scholar] [CrossRef]
  33. Altschul, S.F.; Gish, W.; Miller, W.; Myers, E.W.; Lipman, D.J. Basic Local Alignment Search Tool. J. Mol. Biol. 1990, 215, 403–410. [Google Scholar] [CrossRef]
  34. Maillet, N. Rapid Peptides Generator: Fast and Efficient in Silico Protein Digestion. NAR Genom. Bioinform. 2020, 2, 1l. [Google Scholar] [CrossRef] [PubMed]
  35. Mooney, C.; Haslam, N.J.; Pollastri, G.; Shields, D.C. Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity. PLoS ONE 2012, 7, e45012. [Google Scholar] [CrossRef] [PubMed]
  36. Qin, D.; Jiao, L.; Wang, R.; Zhao, Y.; Hao, Y.; Liang, G. Prediction of Antioxidant Peptides Using a Quantitative Structure-Activity Relationship Predictor (AnOxPP) Based on Bidirectional Long Short-Term Memory Neural Network and Interpretable Amino Acid Descriptors. Comput. Biol. Med. 2023, 154, 106591. [Google Scholar] [CrossRef] [PubMed]
  37. Khatun, M.S.; Hasan, M.M.; Kurata, H. PreAIP: Computational Prediction of Anti-Inflammatory Peptides by Integrating Multiple Complementary Features. Front. Genet. 2019, 10, 129. [Google Scholar] [CrossRef]
  38. Gawde, U.; Chakraborty, S.; Waghu, F.H.; Barai, R.S.; Khanderkar, A.; Indraguru, R.; Shirsat, T.; Idicula-Thomas, S. CAMPR4: A Database of Natural and Synthetic Antimicrobial Peptides. Nucleic Acids Res. 2023, 51, D377–D383. [Google Scholar] [CrossRef]
  39. Kumar, R.; Chaudhary, K.; Singh Chauhan, J.; Nagpal, G.; Kumar, R.; Sharma, M.; Raghava, G.P.S. An in Silico Platform for Predicting, Screening and Designing of Antihypertensive Peptides. Sci. Rep. 2015, 5, 12512. [Google Scholar] [CrossRef]
  40. Charoenkwan, P.; Nantasenamat, C.; Hasan, M.M.; Moni, M.A.; Lio’, P.; Manavalan, B.; Shoombuatong, W. StackDPPIV: A Novel Computational Approach for Accurate Prediction of Dipeptidyl Peptidase IV (DPP-IV) Inhibitory Peptides. Methods 2022, 204, 189–198. [Google Scholar] [CrossRef]
  41. Marin, D.E.; Taranu, I. Using In Silico Approach for Metabolomic and Toxicity Prediction of Alternariol. Toxins 2023, 15, 421. [Google Scholar] [CrossRef]
  42. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A Free Web Tool to Evaluate Pharmacokinetics, Drug-Likeness and Medicinal Chemistry Friendliness of Small Molecules. Sci. Rep. 2017, 7, 42717. [Google Scholar] [CrossRef]
  43. Berman, H.M.; Battistuz, T.; Bhat, T.N.; Bluhm, W.F.; Bourne, P.E.; Burkhardt, K.; Feng, Z.; Gilliland, G.L.; Iype, L.; Jain, S.; et al. The Protein Data Bank. Acta Crystallogr. Sect. D Struct. Biol. 2002, 58, 899–907. [Google Scholar] [CrossRef]
  44. Luna-Vital, D.; Weiss, M.; Gonzalez de Mejia, E. Anthocyanins from Purple Corn Ameliorated Tumor Necrosis Factor-α-Induced Inflammation and Insulin Resistance in 3T3-L1 Adipocytes via Activation of Insulin Signaling and Enhanced GLUT4 Translocation. Mol. Nutr. Food Res. 2017, 61, 1700362. [Google Scholar] [CrossRef] [PubMed]
  45. Villaseñor, V.M.; Navat Enriquez-Vara, J.; Urías-Silva, J.E.; del Carmen Lugo-Cervantes, E.; Luna-Vital, D.A.; Mojica, L. Mexican Grasshopper (Sphenarium purpurascens) as Source of High Protein Flour: Techno-Functional Characterization, and in Silico and in Vitro Biological Potential. Food Res. Int. 2022, 162, 112048. [Google Scholar] [CrossRef] [PubMed]
  46. Vizcaíno, A.J.; Sáez, M.I.; Martínez, T.F.; Acién, F.G.; Alarcón, F.J. Differential Hydrolysis of Proteins of Four Microalgae by the Digestive Enzymes of Gilthead Sea Bream and Senegalese Sole. Algal Res. 2019, 37, 145–153. [Google Scholar] [CrossRef]
  47. Levasseur, W.; Perré, P.; Pozzobon, V. A Review of High Value-Added Molecules Production by Microalgae in Light of the Classification. Biotechnol. Adv. 2020, 41, 107545. [Google Scholar] [CrossRef] [PubMed]
  48. Paterson, S.; Gómez-Cortés, P.; de la Fuente, M.A.; Hernández-Ledesma, B. Bioactivity and Digestibility of Microalgae Tetraselmis Sp. and Nannochloropsis Sp. as Basis of Their Potential as Novel Functional Foods. Nutrients 2023, 15, 477. [Google Scholar] [CrossRef]
  49. Pereira, H.; Silva, J.; Santos, T.; Gangadhar, K.N.; Raposo, A.; Nunes, C.; Coimbra, M.A.; Gouveia, L.; Barreira, L.; Varela, J. Nutritional Potential and Toxicological Evaluation of Tetraselmis Sp. CtP4 Microalgal Biomass Produced in Industrial Photobioreactors. Molecules 2019, 24, 3192. [Google Scholar] [CrossRef]
  50. Millward, D.J.; Layman, D.K.; Tomé, D.; Schaafsma, G. Protein Quality Assessment: Impact of Expanding Understanding of Protein and Amino Acid Needs for Optimal Health. Am. J. Clin. Nutr. 2008, 87, 1576S–1581S. [Google Scholar] [CrossRef]
  51. Xie, W.; Li, X.; Xu, H.; Chen, F.; Cheng, K.W.; Liu, H.; Liu, B. Optimization of Heterotrophic Culture Conditions for the Microalgae Euglena gracilis to Produce Proteins. Mar. Drugs 2023, 21, 519. [Google Scholar] [CrossRef]
  52. Cai, Y.; Zhai, L.; Fang, X.; Wu, K.; Liu, Y.; Cui, X.; Wang, Y.; Yu, Z.; Ruan, R.; Liu, T.; et al. Effects of C/N Ratio on the Growth and Protein Accumulation of Heterotrophic Chlorella in Broken Rice Hydrolysate. Biotechnol. Biofuels Bioprod. 2022, 15, 102. [Google Scholar] [CrossRef]
  53. Pruteanu, L.L.; Bailey, D.S.; Grădinaru, A.C.; Jäntschi, L. The Biochemistry and Effectiveness of Antioxidants in Food, Fruits, and Marine Algae. Antioxidants 2023, 12, 860. [Google Scholar] [CrossRef]
  54. Han, Y.; Ma, B.; Zhang, K. Spider: Software for Protein Identification from Sequence Tags with de Novo Sequencing Error. J. Bioinform. Comput. Biol. 2011, 3, 697–716. [Google Scholar] [CrossRef] [PubMed]
  55. Rashid, M.H.U.; Yi, E.K.J.; Amin, N.D.M.; Ismail, M.N. An Empirical Analysis of Sacha Inchi (Plantae: Plukenetia volubilis L.) Seed Proteins and Their Applications in the Food and Biopharmaceutical Industries. Appl. Biochem. Biotechnol. 2024, 196, 4823–4836. [Google Scholar] [CrossRef] [PubMed]
  56. Fernández-Acero, F.J.; Amil-Ruiz, F.; Durán-Peña, M.J.; Carrasco, R.; Fajardo, C.; Guarnizo, P.; Fuentes-Almagro, C.; Vallejo, R.A. Valorisation of the Microalgae Nannochloropsis gaditana Biomass by Proteomic Approach in the Context of Circular Economy. J. Proteom. 2019, 193, 239–242. [Google Scholar] [CrossRef]
  57. Ferreira, M.; Teixeira, C.; Abreu, H.; Silva, J.; Costas, B.; Kiron, V.; Valente, L.M.P. Nutritional Value, Antimicrobial and Antioxidant Activities of Micro- and Macroalgae, Single or Blended, Unravel Their Potential Use for Aquafeeds. J. Appl. Phycol. 2021, 33, 3507–3518. [Google Scholar] [CrossRef]
  58. Hulatt, C.J.; Smolina, I.; Dowle, A.; Kopp, M.; Vasanth, G.K.; Hoarau, G.G.; Wijffels, R.H.; Kiron, V. Proteomic and Transcriptomic Patterns during Lipid Remodeling in Nannochloropsis gaditana. Int. J. Mol. Sci. 2020, 21, 6946. [Google Scholar] [CrossRef]
  59. Sun, D.; Wu, S.; Li, X.; Ge, B.; Zhou, C.; Yan, X.; Ruan, R.; Cheng, P. The Structure, Functions and Potential Medicinal Effects of Chlorophylls Derived from Microalgae. Mar. Drugs 2024, 22, 65. [Google Scholar] [CrossRef]
  60. Meyer, J.G. In Silico Proteome Cleavage Reveals Iterative Digestion Strategy for High Sequence Coverage. Int. Sch. Res. Not. 2014, 2014, 960902. [Google Scholar] [CrossRef]
  61. Kruchinin, A.G.; Bolshakova, E.I.; Barkovskaya, I.A. Bioinformatic Modeling (In Silico) of Obtaining Bioactive Peptides from the Protein Matrix of Various Types of Milk Whey. Fermentation 2023, 9, 380. [Google Scholar] [CrossRef]
  62. Akbarian, M.; Khani, A.; Eghbalpour, S.; Uversky, V.N. Bioactive Peptides: Synthesis, Sources, Applications, and Proposed Mechanisms of Action. Int. J. Mol. Sci. 2022, 23, 1445. [Google Scholar] [CrossRef]
  63. Han, X.; Liu, Y.; Chen, Z. Zinc Finger Protein LipR Represses Docosahexaenoic Acid and Lipid Biosynthesis in Schizochytrium sp. Appl. Environ. Microbiol. 2022, 88, 6. [Google Scholar] [CrossRef]
  64. Wang, R.; Li, J.; Zhang, F.; Miao, X. Non-Tandem CCCH-Type Zinc-Finger Protein CpZF_CCCH1 Improves Fatty Acid Desaturation and Stress Tolerance in Chlamydomonas reinhardtii. J. Agric. Food Chem. 2023, 71, 17188–17201. [Google Scholar] [CrossRef] [PubMed]
  65. Ashtiani, F.R.; Jalili, H.; Rahaie, M.; Sedighi, M.; Amrane, A. Effect of Mixed Culture of Yeast and Microalgae on Acetyl-CoA Carboxylase and Glycerol-3-Phosphate Acyltransferase Expression. J. Biosci. Bioeng. 2021, 131, 364–372. [Google Scholar] [CrossRef] [PubMed]
  66. Bardani, E.; Kallemi, P.; Tselika, M.; Katsarou, K.; Kalantidis, K. Spotlight on Plant Bromodomain Proteins. Biology 2023, 12, 1076. [Google Scholar] [CrossRef]
  67. Lin, Y.; Li, Y.; Wu, X.; Xu, W.; Zhang, Z.; Zhu, H.; Zhou, H. NgAP2a Targets KCS Gene to Promote Lipid Accumulation in Nannochloropsis gaditana. Int. J. Mol. Sci. 2024, 25, 10305. [Google Scholar] [CrossRef]
  68. Heda, R.; Toro, F.; Tombazzi, C.R. Physiology, Pepsin; StatPearls: Treasure Island, FL, USA, 2023. [Google Scholar]
  69. Salelles, L.; Floury, J.; Le Feunteun, S. Pepsin Activity as a Function of PH and Digestion Time on Caseins and Egg White Proteins under Static in Vitro Conditions. Food Funct. 2021, 12, 12468–12478. [Google Scholar] [CrossRef]
  70. Purohit, K.; Reddy, N.; Sunna, A. Exploring the Potential of Bioactive Peptides: From Natural Sources to Therapeutics. Int. J. Mol. Sci. 2024, 25, 1391. [Google Scholar] [CrossRef]
  71. Yao, S.; Agyei, D.; Udenigwe, C.C. Structural Basis of Bioactivity of Food Peptides in Promoting Metabolic Health. Adv. Food Nutr. Res. 2018, 84, 145–181. [Google Scholar] [CrossRef]
  72. Bhuyan, B.J.; Mugesh, G. Antioxidant Activity of Peptide-Based Angiotensin Converting Enzyme Inhibitors. Org. Biomol. Chem. 2012, 10, 2237–2247. [Google Scholar] [CrossRef]
  73. Li, Y.W.; Li, B.; He, J.; Qian, P. Quantitative Structure–Activity Relationship Study of Antioxidative Peptide by Using Different Sets of Amino Acids Descriptors. J. Mol. Struct. 2011, 998, 53–61. [Google Scholar] [CrossRef]
  74. Fernando, R.; Sun, X.; Rupasinghe, H.P.V. Production of Bioactive Peptides from Microalgae and Their Biological Properties Related to Cardiovascular Disease. Macromol 2024, 4, 582–596. [Google Scholar] [CrossRef]
  75. Pedroni, L.; Perugino, F.; Galaverna, G.; Dall’Asta, C.; Dellafiora, L. An In Silico Framework to Mine Bioactive Peptides from Annotated Proteomes: A Case Study on Pancreatic Alpha Amylase Inhibitory Peptides from Algae and Cyanobacteria. Nutrients 2022, 14, 4680. [Google Scholar] [CrossRef] [PubMed]
  76. Minkiewicz, P.; Iwaniak, A.; Darewicz, M. BIOPEP-UWM Database of Bioactive Peptides: Current Opportunities. Int. J. Mol. Sci. 2019, 20, 5978. [Google Scholar] [CrossRef]
  77. He, W.; Su, G.; Sun-Waterhouse, D.; Waterhouse, G.I.N.; Zhao, M.; Liu, Y. In Vivo Anti-Hyperuricemic and Xanthine Oxidase Inhibitory Properties of Tuna Protein Hydrolysates and Its Isolated Fractions. Food Chem. 2019, 272, 453–461. [Google Scholar] [CrossRef] [PubMed]
  78. Guan-Jhong, H.; Lu, T.; Chiu, C.; Chen, H.; Wu, C.; Lin, Y.; Hsieh, W.; Liao, J.; Sheu, M.; Lin, Y. Sweet potato storage root defensin and its tryptic hydrolysates exhibited angiotensin converting enzyme inhibitory activity in vitro. Bot. Stud. 2011, 52, 257–264. [Google Scholar]
  79. Peng, H.; Carretero, O.A.; Vuljaj, N.; Liao, T.D.; Motivala, A.; Peterson, E.L.; Rhaleb, N.E. Angiotensin-Converting Enzyme Inhibitors: A New Mechanism of Action. Circulation 2005, 112, 2436–2445. [Google Scholar] [CrossRef] [PubMed]
  80. Lan, V.T.T.; Ito, K.; Ohno, M.; Motoyama, T.; Ito, S.; Kawarasaki, Y. Analyzing a Dipeptide Library to Identify Human Dipeptidyl Peptidase IV Inhibitor. Food Chem. 2015, 175, 66–73. [Google Scholar] [CrossRef]
  81. Cheung, H.S.; Wang, F.L.; Ondetti, M.A.; Sabo, E.F.; Cushman, D.W. Binding of Peptide Substrates and Inhibitors of Angiotensin-Converting Enzyme. Importance of the COOH-Terminal Dipeptide Sequence. J. Biol. Chem. 1980, 255, 401–407. [Google Scholar] [CrossRef]
  82. Dhanda, S.; Singh, J.; Singh, H. Hydrolysis of Various Bioactive Peptides by Goat Brain Dipeptidylpeptidase-III Homologue. Cell Biochem. Funct. 2008, 26, 339–345. [Google Scholar] [CrossRef]
  83. Saito, Y.; Nakamura, K.W.; Kawato, A.; Imayasu, S. Structure and Activity of Angiotensin I Converting Enzyme Inhibitory Peptides from Sake and Sake Lees. Biosci. Biotechnol. Biochem. 1994, 58, 1767–1771. [Google Scholar] [CrossRef]
  84. Wang, Z.; Fan, H.; Bao, X.; Wu, J. Angiotensin-Converting Enzyme 2 Activation Is Not a Common Feature of Angiotensin-Converting Enzyme Inhibitory Peptides. J. Agric. Food Chem. 2023, 71, 8867–8876. [Google Scholar] [CrossRef]
  85. Senadheera, T.R.L.; Hossain, A.; Dave, D.; Shahidi, F. In Silico Analysis of Bioactive Peptides Produced from Underutilized Sea Cucumber By-Products—A Bioinformatics Approach. Mar. Drugs 2022, 20, 610. [Google Scholar] [CrossRef] [PubMed]
  86. Lipinski, C.A.; Lombardo, F.; Dominy, B.W.; Feeney, P.J. Experimental and Computational Approaches to Estimate Solubility and Permeability in Drug Discovery and Development Settings. Adv. Drug Deliv. Rev. 1997, 23, 3–25. [Google Scholar] [CrossRef]
  87. Moriguchi, I.; Hirano, H.; Nakagome, I. Comparison of Reliability of Log P Values for Drugs Calculated by Several Methods. Chem. Pharm. Bull. 1994, 42, 976–978. [Google Scholar] [CrossRef]
  88. Moriguchi, I.; Hirono, S.; Liu, Q.; Nakagome, I.; Matsushita, Y. Simple Method of Calculating Octanol/Water Partition Coefficient. Chem. Pharm. Bull. 1992, 40, 127–130. [Google Scholar] [CrossRef]
  89. Martin, Y.C. A Bioavailability Score. J. Med. Chem. 2005, 48, 3164–3170. [Google Scholar] [CrossRef]
  90. Ji, D.; Xu, M.; Udenigwe, C.C.; Agyei, D. Physicochemical Characterisation, Molecular Docking, and Drug-Likeness Evaluation of Hypotensive Peptides Encrypted in Flaxseed Proteome. Curr. Res. Food Sci. 2020, 3, 41–50. [Google Scholar] [CrossRef]
  91. Nong, N.T.P.; Hsu, J.L. Bioactive Peptides: An Understanding from Current Screening Methodology. Processes 2022, 10, 1114. [Google Scholar] [CrossRef]
  92. Ferreira, L.G.; Dos Santos, R.N.; Oliva, G.; Andricopulo, A.D. Molecular Docking and Structure-Based Drug Design Strategies. Molecules 2015, 20, 13384–13421. [Google Scholar] [CrossRef]
  93. Valadez-Cosmes, P.; Raftopoulou, S.; Mihalic, Z.N.; Marsche, G.; Kargl, J. Myeloperoxidase: Growing Importance in Cancer Pathogenesis and Potential Drug Target. Pharmacol. Ther. 2022, 236, 108052. [Google Scholar] [CrossRef]
  94. Vakhrusheva, T.V.; Sokolov, A.V.; Moroz, G.D.; Kostevich, V.A.; Gorbunov, N.P.; Smirnov, I.P.; Grafskaia, E.N.; Latsis, I.A.; Panasenko, O.M.; Lazarev, V.N. Effects of Synthetic Short Cationic Antimicrobial Peptides on the Catalytic Activity of Myeloperoxidase, Reducing Its Oxidative Capacity. Antioxidants 2022, 11, 2419. [Google Scholar] [CrossRef]
  95. Majumder, K.; Chakrabarti, S.; Morton, J.S.; Panahi, S.; Kaufman, S.; Davidge, S.T.; Wu, J. Egg-Derived ACE-Inhibitory Peptides IQW and LKP Reduce Blood Pressure in Spontaneously Hypertensive Rats. J. Funct. Foods 2015, 13, 50–60. [Google Scholar] [CrossRef]
  96. Wu, J.; Aluko, R.E.; Nakai, S. Structural Requirements of Angiotensin I-Converting Enzyme Inhibitory Peptides: Quantitative Structure-Activity Relationship Modeling of Peptides Containing 4-10 Amino Acid Residues. QSAR Comb. Sci. 2006, 25, 873–880. [Google Scholar] [CrossRef]
  97. Natesh, R.; Schwager, S.L.U.; Sturrock, E.D.; Acharya, K.R. Crystal Structure of the Human Angiotensin-Converting Enzyme-Lisinopril Complex. Nature 2003, 421, 551–554. [Google Scholar] [CrossRef] [PubMed]
  98. Garcia-Vaquero, M.; Mora, L.; Hayes, M. In Vitro and in Silico Approaches to Generating and Identifying Angiotensin-Converting Enzyme I Inhibitory Peptides from Green Macroalga Ulva Lactuca. Mar. Drugs 2019, 17, 204. [Google Scholar] [CrossRef]
  99. Hayes, M.; Bastiaens, L.; Gouveia, L.; Gkelis, S.; Skomedal, H.; Skjanes, K.; Murray, P.; García-Vaquero, M.; Hosoglu, M.I.; Dodd, J.; et al. Microalgal Bioactive Compounds Including Protein, Peptides, and Pigments: Applications, Opportunities, and Challenges During Biorefinery Processes. Nov. Proteins Food Pharm. Agric. 2018, 12, 239–255. [Google Scholar] [CrossRef]
  100. Lucakova, S.; Branyikova, I.; Hayes, M. Microalgal Proteins and Bioactives for Food, Feed, and Other Applications. Appl. Sci. 2022, 12, 4402. [Google Scholar] [CrossRef]
Foods 14 00252 g001
Figure 1. Graphical representation of the workflow followed in the present study.
Figure 1. Graphical representation of the workflow followed in the present study.
Foods 14 00252 g001
Foods 14 00252 g002
Figure 2. Proteomic functional distribution of identified proteins from Nannochloropsis gaditana biomass using gene ontology (GO). (A) Proteomic percentage distribution at GO level 1 classification. (B) Cellular component GO level 2 distribution. (C) Biological process GO level 2 distribution. (D) Molecular function GO level 2 distribution.
Figure 2. Proteomic functional distribution of identified proteins from Nannochloropsis gaditana biomass using gene ontology (GO). (A) Proteomic percentage distribution at GO level 1 classification. (B) Cellular component GO level 2 distribution. (C) Biological process GO level 2 distribution. (D) Molecular function GO level 2 distribution.
Foods 14 00252 g002
Foods 14 00252 g003
Figure 3. Peptide profile of the peptides released after in silico gastrointestinal digestion from Nannochloropsis gaditana biomass and predicted as bioactive using the Peptide Ranker software tool. (A) Size distribution expressed as number of amino acids; (B) amino acid distribution.
Figure 3. Peptide profile of the peptides released after in silico gastrointestinal digestion from Nannochloropsis gaditana biomass and predicted as bioactive using the Peptide Ranker software tool. (A) Size distribution expressed as number of amino acids; (B) amino acid distribution.
Foods 14 00252 g003
Foods 14 00252 g004
Figure 4. Molecular docking of the potential bioactive peptide FIPGL from Nannochloropsis gaditana digest with the human myeloperoxidase (MPO) and the angiotensin converting enzyme (ACE). (A) 3D molecular docking representation of FIPGL with MPO; Red: alpha-helices; Blue: beta-strands; Green: loops connecting alpha-helices and beta-strands; Grey: protein backbone or coil regions that do not form distinct secondary structural elements (B) interaction type between FIPGL and MPO. Bright green dotted lines: conventional hydrogen bonds; Pink dotted lines: alkyl and Pi-alkyl bonds (C) 3D molecular docking representation of FIPGL with ACE; Red: alpha-helices; Blue: beta-strands; Green: loops connecting alpha-helices and beta-strands; Grey: protein backbone or coil regions that do not form distinct secondary structural elements (D) interaction type between FIPGL and ACE. Bright green dots lines: conventional hydrogen bonds; Light green dotted lines: Pi-donor hydrogen bonds; Pink dotted lines: alkyl and Pi-alkyl bonds.
Figure 4. Molecular docking of the potential bioactive peptide FIPGL from Nannochloropsis gaditana digest with the human myeloperoxidase (MPO) and the angiotensin converting enzyme (ACE). (A) 3D molecular docking representation of FIPGL with MPO; Red: alpha-helices; Blue: beta-strands; Green: loops connecting alpha-helices and beta-strands; Grey: protein backbone or coil regions that do not form distinct secondary structural elements (B) interaction type between FIPGL and MPO. Bright green dotted lines: conventional hydrogen bonds; Pink dotted lines: alkyl and Pi-alkyl bonds (C) 3D molecular docking representation of FIPGL with ACE; Red: alpha-helices; Blue: beta-strands; Green: loops connecting alpha-helices and beta-strands; Grey: protein backbone or coil regions that do not form distinct secondary structural elements (D) interaction type between FIPGL and ACE. Bright green dots lines: conventional hydrogen bonds; Light green dotted lines: Pi-donor hydrogen bonds; Pink dotted lines: alkyl and Pi-alkyl bonds.
Foods 14 00252 g004
Table 1. Amino acid profile (g/100 g protein and g/100 g biomass) of Nannochloropsis gaditana.
Table 1. Amino acid profile (g/100 g protein and g/100 g biomass) of Nannochloropsis gaditana.
Amino AcidContentFAO Recommendation (g/100 g Protein)
g/100 g Proteing/100 g Biomass
Essential
Lysine (K)4.53 ± 0.092.01 ± 0.045.20
Tryptophan (W)n.d.n.d.0.70
Phenylalanine (F)3.62 ± 0.171.61 ± 0.084.60 a
Tyrosine (Y)2.42 ± 0.071.07 ± 0.03
Methionine (M)1.57 ± 0.010.70 ± 0.012.60 b
Cysteine (C)0.87 ± 0.120.38 ± 0.05
Threonine (T)3.40± 0.151.51 ± 0.062.70
Leucine (L)5.97 ± 0.012.65 ± 0.006.30
Isoleucine (I)2.53 ± 0.031.12 ± 0.013.10
Valine (V)3.40 ± 0.021.51 ± 0014.20
Non-essential
Aspartic acid + Asparragine (D + N)6.44 ± 0.002.85 ± 0.00
Glutamic acid + Glutamine (E + Q)8.81 ± 0.093.91 ± 0.04
Serine (S)3.29 ± 0.151.46 ± 0.07
Histidine (H)1.38 ± 0.010.61 ± 0.00
Arginine (R)4.17 ± 0.001.85 ± 0.00
Alanine (A)5.02 ± 0.012.22 ± 0.01
Proline (P)6.44 ± 0.012.85 ± 0.00
Glycine (G)3.55 ± 0.021.57 ± 0.01
EAA28.3114.13
NEAA39.0117.28
TAA67.4131.41
EAA × 100/TAA (%)42.00
EAA × 100/NEAA (%)72.57
HAA × 100/TAA (%)47.23
AAA × 100/TAA (%)8.96
n.d.: not detected; a: F + Y; b: M + C; EAA: essential amino acid; NEAA: non-essential amino acid; TAA: total amino acid; HAA: hydrophobic amino acid (A + V + I + L + Y + F + W + M + P + C); AAA: aromatic amino acid (F + W + Y).
Table 2. Nannochloropsis gaditana proteins that generated more than 100 peptides/protein after in silico digestion with pepsin.
Table 2. Nannochloropsis gaditana proteins that generated more than 100 peptides/protein after in silico digestion with pepsin.
Accession a−10logP bDescription cAverage Mass (KDa) Peptides
Generated After In Silico Gastric Digestion
I2CQP5426.87Acetyl-CoA carboxylase235,196207
W7U8G3380.26ATP-citrate synthase120,317102
W7TN63348.14Choline dehydrogenase138,839113
K8YSL1287.43Aminopeptidase N137,581102
W7TQD1264.44Pyruvate dehydrogenase E1 component subunit alpha120,314103
W7TTR4192.91P-type atpase129,122103
W7UC18192.19Phosphoribosylformylglycinamidine synthase145,158113
W7TNH0182.55Pyruvate carboxilase134,055102
K8Z9A0174.11Uncharacterized protein123,187107
W7TRK7118.17Coatomer subunit alpha141,192119
W7UBG5106.82Ubiquitin-activating enzyme e1136,993111
W7U8K2105.69Clathrin heavy chain196,113163
W7TM993.66Pentatricopeptide repeat containing protein171,849132
W7UBN087.72WD40-repeat-containing protein138,346110
W7U2D580.11Peptidase M16143,228109
W7U0Z179.24Hydantoin utilization protein146,652128
W7U5E465.46Carbamoyl-phosphate synthase168,186140
W7U2B962.39Zinc finger. ZZ-type552,820376
K8YRI361.47DUF2428 domain-containing protein (Fragment)124,306102
W7TVB156.86Bromodomain containing 1243,436169
W7TYI746.56Cytochrome p450118,277117
W7U1T946.41Nuclear receptor corepressor 1-like protein164,110122
W7U7L845.19Protease-associated domain PA142,381101
W7TPR444.49Tubulin-specific chaperone d147,094109
TOTAL 3160
a Accession number of the protein as obtained from the FASTA database. b PEAKS protein score calculated as the weighted sum of the −10log P scores of the proteins supporting peptides. c Description of proteins as obtained from the FASTA database.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Paterson, S.; Alonso-Pintre, L.; Morato-López, E.; González de la Fuente, S.; Gómez-Cortés, P.; Hernández-Ledesma, B. Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach. Foods 2025, 14, 252. https://doi.org/10.3390/foods14020252

AMA Style

Paterson S, Alonso-Pintre L, Morato-López E, González de la Fuente S, Gómez-Cortés P, Hernández-Ledesma B. Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach. Foods. 2025; 14(2):252. https://doi.org/10.3390/foods14020252

Chicago/Turabian Style

Paterson, Samuel, Laura Alonso-Pintre, Esperanza Morato-López, Sandra González de la Fuente, Pilar Gómez-Cortés, and Blanca Hernández-Ledesma. 2025. "Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach" Foods 14, no. 2: 252. https://doi.org/10.3390/foods14020252

APA Style

Paterson, S., Alonso-Pintre, L., Morato-López, E., González de la Fuente, S., Gómez-Cortés, P., & Hernández-Ledesma, B. (2025). Microalga Nannochloropsis gaditana as a Sustainable Source of Bioactive Peptides: A Proteomic and In Silico Approach. Foods, 14(2), 252. https://doi.org/10.3390/foods14020252

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop