ToxiFusion：结合上下文与组成序列表示预测毒性肽
ToxiFusion: Integrating Contextual and Compositional Sequence Representations for Toxic Peptide Prediction
ToxiFusion 是一个防泄漏的毒性肽预测框架，将蛋白语言模型 ProtBERT 的上下文嵌入与氨基酸组成、二肽组成特征结合，用 XGBoost 分类器完成预测。研究在预处理、模型选择和阈值优化中只使用训练集信息，在锁定的 ToxTeller 独立测试集上 Matthews 相关系数为 0.718；在经重叠审计、剔除与训练序列完全重叠样本的 ToxIBTL 外部队列上，Matthews 相关系数为 0.835。消融分析显示各表示模块均有增量贡献，特征归因分析显示上下文特征与组成特征在不同评测队列中模式大体一致。
来源：Bioinformatics Advances · doi.org
Motivation
Accurate prediction of toxic peptides from sequence information is important for peptide safety assessment and therapeutic development. However, reported performance of computational predictors can be affected by representation limitations and evaluation bias.
Results
We present ToxiFusion, a leakage-controlled framework integrating protein language model-derived contextual representations with interpretable sequence descriptors for toxic peptide prediction. ToxiFusion combines ProtBERT embeddings with amino acid composition and dipeptide composition features using an XGBoost classifier. Preprocessing, model selection, and threshold optimization used training-derived information only. On the locked ToxTeller independent test set, ToxiFusion achieved a Matthews correlation coefficient of 0.718. External evaluation on an overlap-audited ToxIBTL cohort achieved a Matthews correlation coefficient of 0.835 after removing exact training-sequence overlaps. Ablation analyses supported incremental contributions from the representation blocks, while feature-attribution analyses showed broadly consistent patterns for contextual and compositional features across evaluation cohorts. ToxiFusion provides a reproducible framework for toxic peptide prediction and highlights the importance of leakage-controlled validation.
Availability and implementation
Source code, frozen model artifacts, processed data, and analysis outputs are available at https://github.com/ameneayyoubi-hub/ToxFusion.