Dong Benzhi, Li Mengna, Jiang Bei, Gao Bo, Li Dan, Zhang Tianjiao
College of Information and Computer Engineering, Northeast Forestry University, Harbin, China.
Tianjin Second People's Hospital, Tianjin Institute of Hepatology, Tianjin, China.
Front Genet. 2022 Nov 17;13:1069558. doi: 10.3389/fgene.2022.1069558. eCollection 2022.
Antimicrobial peptides (AMPs) are alkaline substances with efficient bactericidal activity produced in living organisms. As the best substitute for antibiotics, they have been paid more and more attention in scientific research and clinical application. AMPs can be produced from almost all organisms and are capable of killing a wide variety of pathogenic microorganisms. In addition to being antibacterial, natural AMPs have many other therapeutically important activities, such as wound healing, antioxidant and immunomodulatory effects. To discover new AMPs, the use of wet experimental methods is expensive and difficult, and bioinformatics technology can effectively solve this problem. Recently, some deep learning methods have been applied to the prediction of AMPs and achieved good results. To further improve the prediction accuracy of AMPs, this paper designs a new deep learning method based on sequence multidimensional representation. By encoding and embedding sequence features, and then inputting the model to identify AMPs, high-precision classification of AMPs and Non-AMPs with lengths of 10-200 is achieved. The results show that our method improved accuracy by 1.05% compared to the most advanced model in independent data validation without decreasing other indicators.
抗菌肽（AMPs）是生物体产生的具有高效杀菌活性的碱性物质。作为抗生素的最佳替代品，它们在科研和临床应用中受到越来越多的关注。AMPs几乎可以由所有生物体产生，并且能够杀死多种致病微生物。除了具有抗菌作用外，天然AMPs还具有许多其他重要的治疗活性，如伤口愈合、抗氧化和免疫调节作用。为了发现新的AMPs，使用湿实验方法既昂贵又困难，而生物信息学技术可以有效解决这个问题。最近，一些深度学习方法已被应用于AMPs的预测并取得了良好的效果。为了进一步提高AMPs的预测准确性，本文设计了一种基于序列多维表示的新型深度学习方法。通过对序列特征进行编码和嵌入，然后将模型输入以识别AMPs，实现了对长度为10 - 200的AMPs和非AMPs的高精度分类。结果表明，在独立数据验证中，我们的方法与最先进的模型相比，准确率提高了1.05%，且其他指标未降低。
