Accelerating Antimicrobial Peptide Discovery with Latent Structure
This work addresses the challenge of designing effective AMPs against drug-resistant pathogens, representing an incremental improvement by incorporating structure information into generative models.
The paper tackles the problem of discovering antimicrobial peptides (AMPs) by proposing a latent sequence-structure model (LSSAMP) that generates peptides with both ideal sequence attributes and secondary structures, resulting in two out of 21 candidates showing strong antimicrobial activity in wet lab experiments.
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.