论文标题
与潜在结构的加速抗菌肽发现
Accelerating Antimicrobial Peptide Discovery with Latent Structure
论文作者
论文摘要
抗菌肽（AMP）是针对耐药病原体的有希望的治疗方法。最近，深层生成模型用于发现新的放大器。但是，先前的研究主要集中于肽序列属性，并且不考虑至关重要的结构信息。在本文中，我们提出了一个用于设计AMP（LSSAMP）的潜在序列结构模型。 LSSAMP利用潜在空间中的多尺度矢量量化，以表示二级结构（例如Alpha Helix和Beta表）。通过在潜在空间中取样，LSSAMP可以同时生成具有理想序列属性和二级结构的肽。实验结果表明，LSSAMP产生的肽具有抗菌活性的概率。我们的湿实验室实验证实了21个候选者中有2名表现出强大的抗菌活性。该代码在https://github.com/dqwang122/lssamp上发布。
Antimicrobial peptides (AMPs) are promising therapeutic approaches against drug-resistant pathogens. Recently, deep generative models are used to discover new AMPs. However, previous studies mainly focus on peptide sequence attributes and do not consider crucial structure information. In this paper, we propose a latent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploits multi-scale vector quantization in the latent space to represent secondary structures (e.g. alpha helix and beta sheet). By sampling in the latent space, LSSAMP can simultaneously generate peptides with ideal sequence attributes and secondary structures. Experimental results show that the peptides generated by LSSAMP have a high probability of antimicrobial activity. Our wet laboratory experiments verified that two of the 21 candidates exhibit strong antimicrobial activity. The code is released at https://github.com/dqwang122/LSSAMP.
