Back to Search Start Over

Introducing enzymatic cleavage features and transfer learning realizes accurate peptide half-life prediction across species and organs.

Authors :
Tan, Xiaorong
Liu, Qianhui
Fang, Yanpeng
Yang, Sen
Chen, Fei
Wang, Jianmin
Ouyang, Defang
Dong, Jie
Zeng, Wenbin
Source :
Briefings in Bioinformatics. Jul2024, Vol. 25 Issue 4, p1-12. 12p.
Publication Year :
2024

Abstract

Peptide drugs are becoming star drug agents with high efficiency and selectivity which open up new therapeutic avenues for various diseases. However, the sensitivity to hydrolase and the relatively short half-life have severely hindered their development. In this study, a new generation artificial intelligence-based system for accurate prediction of peptide half-life was proposed, which realized the half-life prediction of both natural and modified peptides and successfully bridged the evaluation possibility between two important species (human, mouse) and two organs (blood, intestine). To achieve this, enzymatic cleavage descriptors were integrated with traditional peptide descriptors to construct a better representation. Then, robust models with accurate performance were established by comparing traditional machine learning and transfer learning, systematically. Results indicated that enzymatic cleavage features could certainly enhance model performance. The deep learning model integrating transfer learning significantly improved predictive accuracy, achieving remarkable R 2 values: 0.84 for natural peptides and 0.90 for modified peptides in human blood, 0.984 for natural peptides and 0.93 for modified peptides in mouse blood, and 0.94 for modified peptides in mouse intestine on the test set, respectively. These models not only successfully composed the above-mentioned system but also improved by approximately 15% in terms of correlation compared to related works. This study is expected to provide powerful solutions for peptide half-life evaluation and boost peptide drug development. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14675463
Volume :
25
Issue :
4
Database :
Academic Search Index
Journal :
Briefings in Bioinformatics
Publication Type :
Academic Journal
Accession number :
178650426
Full Text :
https://doi.org/10.1093/bib/bbae350