Exploring novel ANGICon-EIPs through ameliorated peptidomics techniques: Can deep learning strategies as a core...

Area of Science:

• Biochemistry and Molecular Biology
• Nutritional Science
• Bioinformatics

Background:

• Dairy-derived angiotensin-I-converting enzyme inhibitory peptides (ANGICon-EIPs) offer a safe dietary approach for hypertension management.
• Short-chain peptides, particularly endogenous ones from sources like goat milk, exhibit significant antihypertensive potential due to enhanced intestinal absorption.
• Current research on endogenous ANGICon-EIPs is limited by challenges in peptide extraction and enrichment.

Purpose of the Study:

• To review and outline advanced pre-treatment strategies for discovering novel endogenous ANGICon-EIPs.
• To explore data acquisition methods and computational tools for predicting peptide structure and function.
• To highlight the potential of deep learning in accelerating the identification of novel ANGICon-EIPs.

Main Methods:

• Review of ameliorated pre-treatment strategies for peptide extraction and enrichment.
• Analysis of data acquisition techniques for bioactive peptides.
• Evaluation of deep learning algorithms, including Convolutional Neural Networks (CNN) and multi-label deep learning (MLBP), for predicting peptide function and structure (e.g., APPTEST).

Main Results:

• Deep learning models demonstrate high accuracy in predicting multiple peptide functions, with MLBP achieving 0.708 accuracy.
• CNN models also show strong performance in predicting peptide functionalities.
• The APPTEST model accurately predicts peptide structures, achieving an average backbone root mean square deviation (RMSD) of 1.96 Å for peptides aged 5-40 amino acids.

Conclusions:

• Deep learning represents a critical advancement for the cost-effective and efficient discovery of novel endogenous ANGICon-EIPs.
• Further exploration of diverse neural network architectures will enhance the identification of antihypertensive peptides.
• This review provides a framework for future research into endogenous ANGICon-EIPs from dietary sources.