PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction

Shi, Kexin1,2; Xiong, Yuanpeng1; Wang, Yu1; Deng, Yifan1; Wang, Wenjia3; Jing, Bingyi4; Gao, Xin1,5,6

2024-02-01

10.1093/bioinformatics/btae058

BIOINFORMATICS   影响因子和分区

1367-4803

1367-4811

Motivation Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances.Results To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements.Availability and implementation The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

SCI

英语

通讯

NSFC[12371290] ; King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA)["URF/1/ 4352-01-01","FCC/1/1976-44-01","FCC/1/1976-45-01","REI/ 1/5234-01-01","REI/1/5414-01-01","REI/1/5289-01-01","REI/ 1/5404-01-01"]

Biochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics

Biochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability

WOS:001163271600006

OXFORD UNIV PRESS

BIOLOGY & BIOCHEMISTRY

Web of Science

1.Syneron Technol, Guangzhou 510000, Peoples R China
2.Hong Kong Univ Sci & Technol, Individualized Interdisciplinary Program Data Sci, Hong Kong, Peoples R China
3.Hong Kong Univ Sci & Technol Guangzhou, Data Sci & Analyt Thrust, Guangzhou 511400, Guangdong, Peoples R China
4.Southern Univ Sci & Technol, Dept Stat & Data Sci, Shenzhen 518000, Peoples R China
5.King Abdullah Univ Sci & Technol KAUST, Comp Sci Program, Comp Elect & Math Sci & Engn Div, Thuwal 23955, Saudi Arabia
6.King Abdullah Univ Sci & Technol KAUST, Computat Biosci Res Ctr, Thuwal 23955, Saudi Arabia

Shi, Kexin,Xiong, Yuanpeng,Wang, Yu,et al. PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction[J]. BIOINFORMATICS,2024,40(2).

Shi, Kexin.,Xiong, Yuanpeng.,Wang, Yu.,Deng, Yifan.,Wang, Wenjia.,...&Gao, Xin.(2024).PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.BIOINFORMATICS,40(2).

Shi, Kexin,et al."PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction".BIOINFORMATICS 40.2(2024).