Cell-Penetrating Milk-Derived Peptides with a Non-Inflammatory Profile.

Milk-derived peptides are known to confer anti-inflammatory effects. We hypothesised that milk-derived cell-penetrating peptides might modulate inflammation in useful ways. Using computational techniques, we identified and synthesised peptides from the milk protein Alpha-S1-casein that were predicted to be cell-penetrating using a machine learning predictor. We modified the interpretation of the prediction results to consider the effects of histidine. Peptides were then selected for testing to determine their cell penetrability and anti-inflammatory effects using HeLa cells and J774.2 mouse macrophage cell lines. The selected peptides all showed cell penetrating behaviour, as judged using confocal microscopy of fluorescently labelled peptides. None of the peptides had an effect on either the NF-κB transcription factor or TNFα and IL-1ß secretion. Thus, the identified milk-derived sequences have the ability to be internalised into the cell without affecting cell homeostatic mechanisms such as NF-κB activation. These peptides are worthy of further investigation for other potential bioactivities or as a naturally derived carrier to promote the cellular internalisation of other active peptides.

An Artificial Intelligence Characterised Functional Ingredient, Derived from Rice, Inhibits TNF-α and Significantly Improves Physical Strength in an Inflammaging Population.

Food-derived bioactive peptides offer great potential for the treatment and maintenance of various health conditions, including chronic inflammation. Using in vitro testing in human macrophages, a rice derived functional ingredient natural peptide network (NPN) significantly reduced Tumour Necrosis Factor (TNF)-α secretion in response to lipopolysaccharides (LPS). Using artificial intelligence (AI) to characterize rice NPNs lead to the identification of seven potentially active peptides, the presence of which was confirmed by liquid chromatography tandem mass spectrometry (LC-MS/MS). Characterization of this network revealed the constituent peptides displayed anti-inflammatory properties as predicted in vitro. The rice NPN was then tested in an elderly "inflammaging" population with a view to subjectively assess symptoms of digestive discomfort through a questionnaire. While the primary subjective endpoint was not achieved, analysis of objectively measured physiological and physical secondary readouts showed clear significant benefits on the ability to carry out physical challenges such as a chair stand test that correlated with a decrease in blood circulating TNF-α. Importantly, the changes observed were without additional exercise or specific dietary alterations. Further health benefits were reported such as significant improvement in glucose control, a decrease in serum LDL concentration, and an increase in HDL concentration; however, this was compliance dependent. Here we provide in vitro and human efficacy data for a safe immunomodulatory functional ingredient characterized by AI.

Human milk and infant formulae: Peptide differences and the opportunity to address the functional gap.

Bovine-derived formula milk (FM) is a common substitute to human milk (HM), but lacks key functional benefits associated with HM. Accordingly, there have been significant efforts to humanise FM. Recent research has demonstrated that HM-derived peptides convey an array of beneficial bioactivities. Given that peptides serve as important signalling molecules offering high specificity and potency, they represent a prime opportunity to humanise FM. To further understand how HM-derived peptides contribute to infant health, we used peptidomics and bioinformatics to compare the peptide profile of HM to commercially available FM. We found clear and substantial differences between HM and FM in terms of peptide physicochemical properties, protein coverage and abundance. We additionally identified 618 peptides specific to HM that represent an important untapped source to be explored for novel bioactivities. While further study is required, our findings highlight the potential of a peptide-based approach to address the functional gap in FM.

Casein Hydrolysate with Glycemic Control Properties: Evidence from Cells, Animal Models, and Humans.

Evidence exists to support the role of dairy derived proteins whey and casein in glycemic management. The objective of the present study was to use a cell screening method to identify a suitable casein hydrolysate and to examine its ability to impact glycemia related parameters in an animal model and in humans. Following screening for the ability to stimulate insulin secretion in pancreatic beta cells, a casein hydrolysate was selected and further studied in the ob/ob mouse model. An acute postprandial study was performed in 62 overweight and obese adults. Acute and long-term supplementation with the casein hydrolysate in in vivo studies in mice revealed a glucose lowering effect and a lipid reducing effect of the hydrolysate (43% reduction in overall liver fat). The postprandial human study revealed a significant increase in insulin secretion ( p = 0.04) concomitant with a reduction in glucose ( p = 0.03). The area under the curve for the change in glucose decreased from 181.84 ± 14.6 to 153.87 ± 13.02 ( p = 0.009). Overall, the data supports further work on the hydrolysate to develop into a functional food product.

Peptidomic screening of bitter and nonbitter casein hydrolysate fractions for insulinogenic peptides.

Sodium caseinate hydrolysates (NaCaH) contain biologically active peptides that can positively influence human health. However, their intense bitterness hinders their inclusion in food products. To our knowledge, no studies have investigated whether a correlation between bitterness and bioactivity exists in NaCaH, so it is not yet known what effect selective removal of bitterness has on NaCaH bioactivity. A deeper understanding of the physicochemical characteristics affecting both bitterness and bioactivity is therefore needed. The aim of this study was to use in silico analysis to elucidate the relationship between bitterness and bioactivity of the insulinogenic NaCaH. The NaCaH fractions were generated by membrane filtration and flash chromatography and were subsequently evaluated for bitterness by a sensory panel. In this present study, peptidomic and bioinformatic processing of these NaCaH fractions allowed for the identification of insulinogenic peptides as well as other literature-identified peptides in each of the fractions. The results showed that the most bitter fraction contained the highest abundance of insulinogenic peptides, whereas another bitter fraction contained the highest abundance of other literature-identified bioactive peptides exhibiting angiotensin-converting enzyme-inhibition activity. Although some bioactive peptides were identified in the least bitter fractions, the abundance of these peptides was very low. These observations show a correlation between bitter taste and bioactivity, highlighting potential complications in removing bitterness while maintaining bioactivity. However, as the most bitter fraction contained the highest abundance of insulinogenic peptides, there is potential for using a lower dose of this enriched bioactive fraction to exert health benefits. The second most bitter fraction contained a very low abundance of insulinogenic peptides and other bioactive peptides. Therefore, removal of this fraction could reduce the NaCaH product's bitterness without significantly altering overall bioactive potential.

Peptigram: A Web-Based Application for Peptidomics Data Visualization.

Tandem mass spectrometry (MS/MS) techniques, developed for protein identification, are increasingly being applied in the field of peptidomics. Using this approach, the set of protein fragments observed in a sample of interest can be determined to gain insights into important biological processes such as signaling and other bioactivities. As the peptidomics era progresses, there is a need for robust and convenient methods to inspect and analyze MS/MS derived data. Here, we present Peptigram, a novel tool dedicated to the visualization and comparison of peptides detected by MS/MS. The principal advantage of Peptigram is that it provides visualizations at both the protein and peptide level, allowing users to simultaneously visualize the peptide distributions of one or more samples of interest, mapped to their parent proteins. In this way rapid comparisons between samples can be made in terms of their peptide coverage and abundance. Moreover, Peptigram integrates and displays key sequence features from external databases and links with peptide analysis tools to offer the user a comprehensive peptide discovery resource. Here, we illustrate the use of Peptigram on a data set of milk hydrolysates. For convenience, Peptigram is implemented as a web application, and is freely available for academic use at http://bioware.ucd.ie/peptigram .

Following the digestion of milk proteins from mother to baby.

Little is known about the digestive process in infants. In particular, the chronological activity of enzymes across the course of digestion in the infant remains largely unknown. To create a temporal picture of how milk proteins are digested, enzyme activity was compared between intact human milk samples from three mothers and the gastric samples from each of their 4-12 day postpartum infants, 2 h after breast milk ingestion. The activities of 7 distinct enzymes are predicted in the infant stomach based on their observed cleavage pattern in peptidomics data. We found that the same patterns of cleavage were evident in both intact human milk and gastric milk samples, demonstrating that the enzyme activities that begin in milk persist in the infant stomach. However, the extent of enzyme activity is found to vary greatly between the intact milk and gastric samples. Overall, we observe that milk-specific proteins are cleaved at higher levels in the stomach compared to human milk. Notably, the enzymes we predict here only explain 78% of the cleavages uniquely observed in the gastric samples, highlighting that further investigation of the specific enzyme activities associated with digestion in infants is warranted.

Amino acid enrichment and compositional changes among mammalian milk proteins and the resulting nutritional consequences.

Milk is a hallmark of mammalian evolution: a unique food that has evolved with mammals. Despite the importance of this food, it is not known if variation in AA composition between different species is important to milk proteins or how it might affect the nutritional value of milk. As milk is the only food source for newborn mammals, it has long been speculated that milk proteins should be enriched in essential AA. However, no systematic analysis supports this assumption. Although many factors influence the overall nutritional value of milk, including total protein concentration, we focused here on the AA composition of milk proteins and investigated the possibility that selection drives compositional changes. We identified 9 major milk proteins present in 13 mammalian species and compared them with a large group of nonmilk proteins. Our results indicate heterogeneity in the AA composition of milk proteins, showing significant enrichment and depletion of certain AA in milk-specific proteins. Although high levels of particular AA appear to be consistently maintained, orthologous milk proteins display significant differences in AA composition across species, most notably among the caseins. Interspecies variation of milk composition is thought to be indicative of nutritional optimization to the requirements of the species. In accordance with this, our observations indicate that milk proteins may have adapted to the species-specific nutritional needs of the neonate.

CPPpred: prediction of cell penetrating peptides.

Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.

PeptideLocator: prediction of bioactive peptides in protein sequences.

MOTIVATION: Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence. RESULTS: We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins. AVAILABILITY AND IMPLEMENTATION: PeptideLocator is freely available for academic users at http://bioware.ucd.ie/.

Deep genomic-scale analyses of the metazoa reject Coelomata: evidence from single- and multigene families analyzed under a supertree and supermatrix paradigm.

Solving the phylogeny of the animals with bilateral symmetry has proven difficult. Morphological studies have suggested a variety of alternative hypotheses, of which, Hyman's Coelomata hypothesis has become the most established. Studies based on 18S rRNA have failed to endorse Coelomata, supporting instead the rearrangement of the protostomes into two new clades: the Lophotrochozoa (including, e.g., the molluscs and the annelids) and the Ecdysozoa (including the Panarthropoda and most pseudocoelomates, such as the nematodes and priapulids). Support for this new animal phylogeny has been attained from expressed sequence tag studies, although these generally have a limited gene sampling. In contrast, deep genomic-scale analyses have often supported Coelomata. However, these studies are problematic due to their limited taxonomic sampling, which could exacerbate tree reconstruction artifacts. Here, we address both of these sampling limitations; we study the effect of long-branch attraction (LBA) in deep genomic-scale analyses and provide convincing evidence, using both single- and multigene families, that Coelomata is an artifact. We show that optimal outgroup selection is key in avoiding LBA and identify the use of inadequate outgroups as the reason previous deep genomic-scale analyses found strong support for Coelomata.