The equine alpha(s1)- and beta-caseins (CN) were purified by chromatography on DEAE-cellulose and by reversed-phase HPLC. The alpha(s1)-, beta-, and kappa-CN were characterized either by monodimensional urea-PAGE or sodium dodecylsulfate (SDS)-PAGE or by bidimensional electrophoresis. Kappa-casein was characterized after electrophoresis by glycoprotein-specific staining. To identify alpha(s1)-CN without ambiguity, internal sequences were determined after trypsin or chymosin digestion of purified alpha(s1)-CN. These sequences, that could be estimated to correspond to 62% of the full protein, presented strong identities with regions of alpha(s1)-CN primary structures of other species. In particular, 51, 48, 43, and 40% identities were obtained with corresponding regions of sow, dromedary, cow, and human alpha(s1)-CN, respectively. On the other hand, trace amounts of equine gamma-CN-like and proteose peptone component 5-like peptides were found in the whole CN. They were identified by microsequencing and corresponded to beta-CN peptides generated by plasmin action on the whole CN. The equine alpha(s1), beta-, and kappa-CN were separated by bidimensional electrophoresis in numerous isoelectric variants with apparent isoelectric points distributed between pH 4.4 to 6.3, 4.4 to 5.9, and 3.5 to 5.5, respectively. The beta- and kappa-CN displayed a more acidic character in the mare than in the cow.

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.

Background: The polyproline II helix (PPIIH) is an extended protein left-handed secondary structure that usually but not necessarily involves prolines. Short PPIIHs are frequently, but not exclusively, found in disordered protein regions, where they may interact with peptide-binding domains. However, no readily usable software is available to predict this state. Results: We developed PPIIPRED to predict polyproline II helix secondary structure from protein sequences, using bidirectional recurrent neural networks trained on known three-dimensional structures with dihedral angle filtering. The performance of the method was evaluated in an external validation set. In addition to proline, PPIIPRED favours amino acids whose side chains extend from the backbone (Leu, Met, Lys, Arg, Glu, Gln), as well as Ala and Val. Utility for individual residue predictions is restricted by the rarity of the PPIIH feature compared to structurally common features. Conclusion: The software, available at http://bioware.ucd.ie/PPIIPRED , is useful in large-scale studies, such as evolutionary analyses of PPIIH, or computationally reducing large datasets of candidate binding peptides for further experimental validation.

les explants de peau humaine. Enfin, dans notre etude clinique de preuve de concept, l'application de pep_RTE626 sur 28 jours a d emontr e un potentiel stimulant anti-rides et collag ene. CONCLUSION: pep_RTE62G repr esente un peptide naturel, non modifi e avec des propri et es anti-âge pr edites par l'IA et valid ees exp erimentalement. Nos r esultats confirment l'utilit e de l'IA dans la d ecouverte de nouveaux ingr edients topiques fonctionnels.

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