The Identification and Characterisation of Novel Bioactive Peptides Derived from Porcine Liver and Placenta

Bioactive peptides are short (2-50 residues) sequences of amino acids that exhibit a single or multiple activities that can have an impact on biological systems. They can be derived from a variety of sources that include dietary proteins, which release peptides during digestion via digestive proteases in-vivo, or they can be isolated from various sources ex vivo such as plant-based proteins, animal proteins, or generated as a waste by-product from the meat industry. Many peptide sequences that could be bioactive are found in inactive states in larger proteins.

The objectives of this study were twofold; firstly, the physicochemical properties and bioactivity of porcine liver and placenta hydrolysates derived from waste material generated from the meat industry were investigated. The hydrolysates were manufactured and supplied by Biofac A/C (Denmark). Secondly, a method was developed to predict the presence of bioactive peptides, which was used in the generation of novel synthetic peptides, using the Biofac hydrolysates as the source.

The introduction and literature review (chapter 1) begins with general background information on bioactive peptides, including how peptides have been developed as pharmaceutical agents, nutraceutical peptides, and the types of bioactivities that can be demonstrated in peptides. Bioactive peptides that have been investigated in the literature generally are isolated from hydrolysates of proteins from the above sources, and analysed for activities such as antioxidant activity, regulation of blood pressure, wound healing, antimicrobial, and immunomodulation. Chapter 1 discusses the use of in silico methods used in the literature to characterise bioactive peptides that have been isolated. The use of online resources such as databases of known bioactive peptides, and where they were isolated from, is widely used in the literature. These databases, used in conjunction with in silico enzyme digestion tools, can be used to generate lists of peptides that theoretically exhibit bioactivity, which can then be run through algorithms that predict the likelihood of any given sequence being bioactive. Finally, the source of the hydrolysates used in this study, and how the work undertaken can be used by Biofac to increase the value of their product is discussed.

The first results chapter (chapter 3) investigated the physicochemical properties of the Biofac hydrolysates. These factors were analysed to provide information that could be utilised in the manufacturing process, and on how the product could be further processed, such as formulation. The morphology and flow properties of the powders were analysed using microscopy, particle size analysis, and particle flow analysis. Information on the molecular properties and composition of the powders were studied by analysing the solubility and saturation point, and the determination of the amount of carbohydrates present in the samples. Finally, the molecular weight (MW) of the constituent peptides were investigated using SEC-MALS. The placenta (P1) demonstrated much greater solubility than liver (L1), followed by the heart hydrolysate (H1). The composition of L2 hydrolysate showed the highest proportion of carbohydrate (4.32%), and P1 demonstrated the least (0.62).

Chapter 4 focused on the bioactivity of the hydrolysates (L1, L2, L3, P1, and P2), and the activity of fractions derived from the L2 sample after preparative HPLC. The bioactivities investigated in this chapter were antioxidant activity (using the DPPH assay, ORAC assay, lipid peroxidation assay, and a cellular antioxidant activity assay), cell proliferation (cell counts and MTT assay) and wound healing (scratch wound assay), and an ACE inhibition assay. It was found that the liver hydrolysates performed better overall as antioxidants, while placenta performed better as anti-ACE agents. Neither sets of hydrolysates were found to influence cell proliferation or wound healing. Furthermore, the peptides, which constitute the hydrolysates, were assessed using HPLC and an MTT assay over a seven-day period and found to be stable in solution.

The final results chapter (chapter 5) addressed the development of a high throughput screening method for bioactive peptides primarily using the online resources presented by the BIOPEP database, and the bioactivity prediction algorithm, Peptide Ranker. This set of work involved the isolation of individual peptides from the hydrolysates (L2 and P2) using reverse-phase HPLC so the amino acid sequence could be discovered using tandem mass spectrometry with de-novo sequencing. The sequences generated from this process were then used to discover the host protein they were from. These proteins were digested in silico using the enzyme papain, with the resulting peptides crosschecked against databases of known bioactive peptides. Unreported peptides were then analysed using Peptide Ranker, and subsequently, four peptides that indicated a high probability of activity were selected to be synthesised. The synthesised peptides (FWG, MFLF, FFNDA, and SDPPLVFVG) were analysed using the same techniques as in chapter 4. It was found that FWG exhibited the greatest antioxidant activity followed by MFLG with FFNDA and SDPPLVFVG not demonstrating antioxidant activity at detectable levels. MFLG was found to be the most potent ACE inhibitor, followed by FWG, then FFNDA and SDPPLVFVG. None of the peptides exhibited any wound healing or cell proliferation activity.

Chapter 6 covers the general conclusions, and future work related to the study as well as analysing the usefulness of the screening method as a tool in predicting the presence of bioactive peptides before, and after the production of hydrolysates. The use of different enzymes to generate the peptides was explored along with utilising different algorithms for the prediction of specific bioactivity to increase the chance of identifying a peptide, and characterising the bioactivity. The future work section incorporates the theoretical use of the method to identify a possible anti-ACE peptide which could potentially be found in chickpeas.