PDF(Pubmed)
Abstract：
Antibiotic resistance is recognized as an imminent and growing global health threat. New antimicrobial drugs are urgently needed due to the decreasing effectiveness of conventional small-molecule antibiotics. Antimicrobial peptides (AMPs), a class of host defense peptides, are emerging as promising candidates to address this need. The potential sequence space of amino acids is combinatorially vast, making it possible to extend the current arsenal of antimicrobial agents with a practically infinite number of new peptide-based candidates. However, mining naturally occurring AMPs, whether directly by wet lab screening methods or aided by bioinformatics prediction tools, has its theoretical limit regarding the number of samples or genomic/transcriptomic resources researchers have access to. Further, manually designing novel synthetic AMPs requires prior field knowledge, restricting its throughput. In silico sequence generation methods are gaining interest as a high-throughput solution to the problem. Here, we introduce AMPd-Up, a recurrent neural network based tool for de novo AMP design, and demonstrate its utility over existing methods. Validation of candidates designed by AMPd-Up through antimicrobial susceptibility testing revealed that 40 of the 58 generated sequences possessed antimicrobial activity against Escherichia coli and/or Staphylococcus aureus. These results illustrate that AMPd-Up can be used to design novel synthetic AMPs with potent activities.
摘要：
抗生素耐药性被认为是一个迫在眉睫且日益严重的全球健康威胁。由于常规小分子抗生素的有效性降低,迫切需要新的抗微生物药物。抗菌肽（AMP），一类宿主防御肽，正在成为解决这一需求的有希望的候选人。氨基酸的潜在序列空间组合是巨大的，使得有可能用几乎无限数量的新的基于肽的候选物扩展当前的抗菌剂库。然而,开采天然存在的AMP，无论是直接通过湿实验室筛选方法还是借助生物信息学预测工具，关于研究人员可以访问的样本或基因组/转录组资源的数量有其理论限制。Further,手动设计新型合成AMP需要先验的领域知识，限制其吞吐量。作为该问题的高通量解决方案，计算机序列生成方法正在引起人们的兴趣。这里,我们介绍AMPd-Up，用于从头AMP设计的基于递归神经网络的工具，并演示其相对于现有方法的实用性。通过抗微生物药敏试验验证由AMPd-Up设计的候选物表明，58个产生的序列中有40个具有抗大肠杆菌和/或金黄色葡萄球菌的抗微生物活性。这些结果表明AMPd-Up可用于设计具有有效活性的新型合成AMP。
