Strategic Preparations of DPP-IV Inhibitory Peptides from Val-Pro-Xaa and Ile-Pro-Xaa Peptide Mixtures

Screening of novel DPP-IV inhibitory peptides derived from bovine milk proteins using a peptide array platform

Dipeptidyl peptidase IV (DPP-IV) has become an important target in the prevention and treatment of diabetes. Although many DPP-IV inhibitory peptides have been identified by a general approach involving the repeated fractionation of food protein hydrolysates, the obtained results have been dependent on the content of each peptide and fractionation conditions. In the present study, a peptide array that provides comprehensive assays of peptide sequences was used to identify novel DPP-IV inhibitory peptides derived from bovine milk proteins; these peptides were then compared with those identified using the general approach. While the general approach identified only known peptides that were abundant in the hydrolysate, the peptide array-based approach identified 10 novel DPP-IV inhibitory peptides, all of which had proline at the second residue from the N-terminus. The proper or combined use of these two approaches, which have different advantages, will enable the efficient development of novel bioactive foods and drugs.

Exploration of DPP-IV Inhibitory Peptide Design Rules Assisted by the Deep Learning Pipeline That Identifies the Restriction Enzyme Cutting Site

The mining of antidiabetic dipeptidyl peptidase IV (DPP-IV) inhibitory peptides (DPP-IV-IPs) is currently a costly and laborious process. Due to the absence of rational peptide design rules, it relies on cumbersome screening of unknown enzyme hydrolysates. Here, we present an enhanced deep learning model called bidirectional encoder representation (BERT)-DPPIV, specifically designed to classify DPP-IV-IPs and explore their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned BERT architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in the benchmark data set showed BERT-DPPIV yielded state-of-the-art accuracy and MCC of 0.894 and 0.790, surpassing the 0.797 and 0.594 obtained by the sequence-feature model. Furthermore, we leveraged the attention mechanism to uncover that our model could recognize the restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules for DPP-IV inhibitory tripeptides and pentapeptides were validated, and they can be used to screen potent DPP-IV-IPs.

Mining and Validation of Novel Hemp Seed-Derived DPP-IV-Inhibiting Peptides Using a Combination of Multi-omics and Molecular Docking

Hemp seed-derived inhibitors of dipeptidyl peptidase IV (DPP-IV) demonstrate potential as novel therapeutics for diabetes; however, their proteome and genome remain uncharacterized. We used multi-omics technology to mine peptides capable of inhibiting DPP-IV. First, 1261 and 1184 proteins were identified in fresh and dry hemp seeds, respectively. Simulated protease cleavage of dry seed proteins yielded 185,446 peptides for virtual screening to select the potential DPP-IV-inhibiting peptides. Sixteen novel peptides were selected according to their DPP-IV-binding affinity determined via molecular docking. In vitro DPP-IV inhibition assays identified the peptides LPQNIPPL, YPYY, YPW, LPYPY, WWW, YPY, YPF, and WS with half-maximal inhibitory concentration (IC₅₀) values lower than 0.5 mM, which were 0.08 ± 0.01, 0.18 ± 0.03, 0.18 ± 0.01, 0.20 ± 0.03, 0.22 ± 0.03, 0.29 ± 0.02, 0.42 ± 0.03, and 0.44 ± 0.09 mM, respectively. The dissociation constants (K_D) of the 16 peptides ranged from 1.50 × 10^-4 to 1.82 × 10^-7 M. Furthermore, Caco2 and INS-1 cell assays showed that all 16 peptides could efficiently inhibit DPP-IV activity and increase insulin and glucagon-like peptide-1 concentrations. These results demonstrate a well-established and efficient method to isolate food-derived therapeutic DPP-IV-inhibiting peptides.

Anti-Alzheimer's disease potential of traditional chinese medicinal herbs as inhibitors of BACE1 and AChE enzymes

Alzheimer's disease (AD) is a common neurodegenerative disease that often occurs in the elderly population. At present, most drugs for AD on the market are single-target drugs, which have achieved certain success in the treatment of AD. However, the efficacy and safety of single-target drugs have not achieved the expected results because AD is a multifactorial disease. Multi-targeted drugs act on multiple factors of the disease network to improve efficacy and reduce adverse reactions. Therefore, the search for effective dual-target or even multi-target drugs has become a new research trend. Many of results found that the dual-target inhibitors of the beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) and acetylcholinesterase (AChE) found from traditional Chinese medicine have a good inhibitory effect on AD with fewer side effects. This article reviews sixty-six compounds extracted from Chinese medicinal herbs, which have inhibitory activity on BACE1 and AChE. This provides a theoretical basis for the further development of these compounds as dual-target inhibitors for the treatment of AD.