In silico and in vivo discovery of antioxidant sea cucumber peptides with antineurodegenerative properties.

Qiangqiang Wang,Ming Liang,Yue Xiao, Zhenhua Li, Xiaohe Chen, Peng Cheng, Bing Qi,Yi Yu, Tao Lei,Zebo Huang

Food & function(2024)

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摘要
Since oxidative stress is often associated with neurodegenerative diseases, antioxidants are likely to confer protection against neurodegeneration. Despite an increasing number of food-derived peptides being identified as antioxidants, their antineurodegenerative potentials remain largely unexplored. Here, a sea cucumber peptide preparation - the peptide-rich fraction of <3 kDa (UF<3K) obtained by ultrafiltration from Apostichopus japonicus protein hydrolyzate - was found to protect PC12 cells and Caenorhabditis elegans from neurodegeneration by reducing oxidative stress and apoptosis, demonstrating its in vitro and in vivo neuroprotective effects. As many food-originated peptides are cryptides (cryptic peptides - short amino acid sequences encrypted in parent proteins) released in quantities by protein hydrolysis, UF<3K was subjected to sequencing analysis. As expected, a large repertoire of peptides were identified in UF<3K, establishing a sea cucumber cryptome (1238 peptides in total). Then 134 peptides were randomly selected from the cryptome (>10%) and analyzed for their antioxidant activities using a number of in silico bioinformatic programs as well as in vivo experimental assays in C. elegans. From these results, a novel antioxidant peptide - HoloPep#362 (FETLMPLWGNK) - was shown to not only inhibit aggregation of neurodegeneration-associated polygluatmine proteins but also ameliorate behavioral deficits in proteotoxicity nematodes. Proteomic analysis revealed an increased expression of several lysosomal proteases by HoloPep#362, suggesting proteostasis maintenance as a mechanism for its antineurodegenerative action. These findings provide an insight into the health-promoting potential of sea cucumber peptides as neuroprotective nutraceuticals and also into the importance of training in silico peptide bioactivity prediction programs with in vivo experimental data.
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