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

Autor:	Tan X; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Liu Q; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Fang Y; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Yang S; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Chen F; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Wang J; The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, 214, Veritas A Hall, Yonsei Univeristy, 85 Songdogwahak-ro, Incheon 21983, Republic of Korea., Ouyang D; Institute of Chinese Medical Sciences (ICMS), State Key Laboratory of Quality Research in Chinese Medicine, University of Macau, Avenida da Universidade, Taipa, Macau 999078, China., Dong J; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China., Zeng W; Xiangya School of Pharmaceutical Sciences, Central South University, No. 172 Tongzipo Road, Yuelu District, Changsha 410083, P.R. China.
Jazyk:	angličtina
Zdroj:	Briefings in bioinformatics [Brief Bioinform] 2024 May 23; Vol. 25 (4).
DOI:	10.1093/bib/bbae350
Abstrakt:	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 R2 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. (© The Author(s) 2024. Published by Oxford University Press.)
Databáze:	MEDLINE
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