HyPepTox-Fuse:An interpretable hybrid framework for accurate peptide toxicity prediction fusing protein language model-based embeddings with conventional descriptors
10.1016/j.jpha.2025.101410
- Author:
Thanh-Tran DUONG
1
;
Truong-Pham NHAT
;
Leyi WEI
;
Balachandran MANAVALAN
Author Information
1. Department of Integrative Biotechnology,College of Biotechnology and Bioengineering,Sungkyunkwan University,Suwon,16419,Republic of Korea
- Collective Name:Nguyen Doan Hieu Nguyen
- Publication Type:Journal Article
- Keywords:
Peptide toxicity;
Hybrid framework;
Multi-head attention;
Transformer;
Deep learning;
Machine learning;
Protein language model
- From:
Journal of Pharmaceutical Analysis
2025;15(8):1873-1886
- CountryChina
- Language:English
-
Abstract:
Peptide-based therapeutics hold great promise for the treatment of various diseases;however,their clinical application is often hindered by toxicity challenges.The accurate prediction of peptide toxicity is crucial for designing safe peptide-based therapeutics.While traditional experimental approaches are time-consuming and expensive,computational methods have emerged as viable alternatives,including similarity-based and machine learning(ML)-/deep learning(DL)-based methods.However,existing methods often struggle with robustness and generalizability.To address these challenges,we propose HyPepTox-Fuse,a novel framework that fuses protein language model(PLM)-based embeddings with conventional descriptors.HyPepTox-Fuse integrates ensemble PLM-based embeddings to achieve richer peptide representations by leveraging a cross-modal multi-head attention mechanism and Transformer architecture.A robust feature ranking and selection pipeline further refines conventional descriptors,thus enhancing prediction performance.Our framework outperforms state-of-the-art methods in cross-validation and independent evaluations,offering a scalable and reliable tool for peptide toxicity pre-diction.Moreover,we conducted a case study to validate the robustness and generalizability of HyPepTox-Fuse,highlighting its effectiveness in enhancing model performance.Furthermore,the HyPepTox-Fuse server is freely accessible at https://balalab-skku.org/HyPepTox-Fuse/and the source code is publicly available at https://github.com/cbbl-skku-org/HyPepTox-Fuse/.The study thus presents an intuitive platform for predicting peptide toxicity and supports reproducibility through openly available datasets.
