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Exploration of DPP-IV inhibitory peptide design rules assisted by deep learning pipeline that identifies restriction enzyme cutting site
Open accessPreprint — published as 10.1021/acsomega.3c05571 · Paper page
Abstract
Mining of anti-diabetic 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 (DL) model called BERT-DPPIV, specifically designed to classify DPP-IV-IPs and exploring their design rules to discover potent candidates. The end-to-end model utilizes a fine-tuned bidirectional encoder representations (BERT) architecture to extract structural/functional information from input peptides and accurately identify DPP-IV-Ips from input peptides. Experimental results in benchmark dataset showed BERT-DPPIV yielded state-of-the-art accuracy of 0.894, surpassing the 0.797 obtained by sequence-feature model. Furthermore, we leverage the attention mechanism to uncover that our model could recognize restriction enzyme cutting site and specific residues that contribute to the inhibition of DPP-IV. Moreover, guided by BERT-DPPIV, proposed design rules of DPP-IV inhibitory tripeptides and pentapeptides were validated and they can be used to screen potent DPP-IV-IPs.
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Metadata
- Authors
- Guan, C., Luo, J., Li, S., Tan, Z. L., Wang, Y., Chen, H., Yamamoto, N., Zhang, C., Lu, Y., Chen, J., & Xing, X.-H.
- Affiliations
- Tsinghua University (CN)
- Harbin Institute of Technology (CN)
- Tokyo Institute of Technology (JP)
- Shenzhen Bay Laboratory (CN)
- Corresponding author
- Guan, Changge
- Xing, Xin-Hui
- ORCID
- Venue
- ACS Omega
- Publisher
- openRxiv
- Published
- 2022-06-16
- Volume
- 8
- Issue
- 42
- Pages
- 39662-39672
- ISSN
- 2470-1343 (ISSN-L)
- PMC
- PMC10601436
- OpenAlex
- W4387608279
- Cited by
- 13
- References
- 1
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 6
- FWCI
- 1.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- ACS Omega (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (62102118)
- Science, Technology and Innovation Commission of Shenzhen Municipality (KCXFZ2020 1221173207022)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Life Sciences › Agricultural and Biological Sciences › Food Science › Protein Hydrolysis and Bioactive Peptides
- Keywords
- DPP-IV inhibitory peptides, dipeptidyl peptidase IV, BERT fine-tuning, attention mechanism, antidiabetic peptides, tripeptides, pentapeptide, peptide screening
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