Authors
Jingya Fang, Wenqi Shen, Yanru Li, Yu Jin, Haodong Li, Yuanyuan Chen, Zhihui Xu, Jinhu Huang, Cong Pian, Zutan Li
Published in
Briefings in bioinformatics. Volume 27. Issue 5. Sep 01, 2026.
Abstract
Peptide therapeutics offer great promise in drug discovery, yet the accurate identification of peptide toxicity remains essential for both safety assessment and biodefense. We developed ToxMamba, a toxic peptide prediction framework that integrates Implicit Structure Model (ISM)-derived pretrained embeddings, state space models, multi-scale feature learning, and ensemble inference. Built upon the Mamba architecture, ToxMamba enables efficient sequence modeling and captures contextual patterns across multiple receptive fields from parallel branches. On the independent test set, ToxMamba consistently surpasses existing methods, improving the F1 score and Matthews Correlation Coefficient by 3.9-18.3 and 7.5-46.9 percentage points, respectively. Ablation analyses further showed that Mamba-based models generally achieved stronger overall performance than Transformer-, Convolutional Neural Network-Long Short-Term Memory-Attention-, and Kolmogorov-Arnold Network-based variants. Feature visualization showed that ISM embeddings yielded clearer separation between toxic and non-toxic peptides in the latent space. Intermediate-layer feature analysis highlighted residue-level regions associated with toxicity-related sequence motifs, supporting the biological interpretability of ToxMamba. Overall, this study provides an efficient and interpretable computational framework for high-throughput toxic peptide screening.
PMID:
42842783
Bibliographic data and abstract were imported from PubMed on 08 Oct 2026.
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