Zhao Huajian, Song Gengshen
Beijing Youcare Kechuang Pharmaceutical Technology Co., Ltd., Beijing 100176, China.
Viruses. 2024 Dec 25;17(1):14. doi: 10.3390/v17010014.
Human respiratory syncytial virus (RSV) remains a significant global health threat, particularly for vulnerable populations. Despite extensive research, effective antiviral therapies are still limited. To address this urgent need, we present AVP-GPT2, a deep-learning model that significantly outperforms its predecessor, AVP-GPT, in designing and screening antiviral peptides. Trained on a significantly expanded dataset, AVP-GPT2 employs a transformer-based architecture to generate diverse peptide sequences. A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. SHAP analysis further enhances interpretability by explaining the underlying mechanisms of peptide activity. Our in vitro experiments confirmed the antiviral efficacy of peptides generated by AVP-GPT2, with some exhibiting EC50 values as low as 0.01 μM and CC50 values > 30 μM. This represents a substantial improvement over AVP-GPT and traditional methods. AVP-GPT2 has the potential to significantly impact antiviral drug discovery by accelerating the identification of novel therapeutic agents. Future research will explore its application to other viral targets and its integration into existing drug development pipelines.
人呼吸道合胞病毒（RSV）仍然是全球重大的健康威胁，对弱势群体尤为如此。尽管进行了广泛研究，但有效的抗病毒疗法仍然有限。为满足这一迫切需求，我们提出了AVP-GPT2，这是一种深度学习模型，在设计和筛选抗病毒肽方面显著优于其前身AVP-GPT。AVP-GPT2在大幅扩充的数据集上进行训练，采用基于Transformer的架构来生成多样的肽序列。一种结合了Star-Transformer和视觉Transformer的多模态筛选方法，能够准确预测抗病毒活性和毒性，从而识别出强效且安全的候选药物。SHAP分析通过解释肽活性的潜在机制进一步增强了可解释性。我们的体外实验证实了AVP-GPT2生成的肽的抗病毒功效，其中一些肽的半数有效浓度（EC50）低至0.01 μM，半数细胞毒性浓度（CC50）> 30 μM。这相较于AVP-GPT和传统方法有了显著改进。AVP-GPT2有潜力通过加速新型治疗药物的识别，对抗病毒药物研发产生重大影响。未来的研究将探索其在其他病毒靶点上的应用以及与现有药物开发流程的整合。
