PDF(Pubmed)
Abstract：
Bioactive peptide therapeutics has been a long-standing research topic. Notably, the antimicrobial peptides (AMPs) have been extensively studied for its therapeutic potential. Meanwhile, the demand for annotating other therapeutic peptides, such as antiviral peptides (AVPs) and anticancer peptides (ACPs), also witnessed an increase in recent years. However, we conceive that the structure of peptide chains and the intrinsic information between the amino acids is not fully investigated among the existing protocols. Therefore, we develop a new graph deep learning model, namely TP-LMMSG, which offers lightweight and easy-to-deploy advantages while improving the annotation performance in a generalizable manner. The results indicate that our model can accurately predict the properties of different peptides. The model surpasses the other state-of-the-art models on AMP, AVP and ACP prediction across multiple experimental validated datasets. Moreover, TP-LMMSG also addresses the challenges of time-consuming pre-processing in graph neural network frameworks. With its flexibility in integrating heterogeneous peptide features, our model can provide substantial impacts on the screening and discovery of therapeutic peptides. The source code is available at https://github.com/NanjunChen37/TP_LMMSG.
摘要：
生物活性肽疗法一直是一个长期的研究课题。值得注意的是,抗菌肽（AMP）的治疗潜力已被广泛研究。同时,对注释其他治疗肽的需求，如抗病毒肽（AVPs）和抗癌肽（ACP），近年来也有所增加。然而,我们认为，肽链的结构和氨基酸之间的内在信息在现有的方案中没有得到充分的研究。因此,我们开发了一个新的图形深度学习模型，即TP-LMMSG，它提供了轻量级和易于部署的优势，同时以可概括的方式提高了注释性能。结果表明，我们的模型可以准确地预测不同肽的性质。该模型超越了AMP上其他最先进的模型，跨多个实验验证数据集的AVP和ACP预测。此外，TP-LMMSG还解决了图神经网络框架中耗时的预处理的挑战。凭借其在整合异质肽特征方面的灵活性，我们的模型可以为筛选和发现治疗性肽提供实质性的影响.源代码可在https://github.com/NanjunChen37/TP_LMMSG获得。
