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Antimicrobial peptides (AMPs) are promising therapeutic approaches againstdrug-resistant pathogens. Recently, deep generative models are used to discovernew AMPs. However, previous studies mainly focus on peptide sequence attributesand do not consider crucial structure information. In this paper, we propose alatent sequence-structure model for designing AMPs (LSSAMP). LSSAMP exploitsmulti-scale vector quantization in the latent space to represent secondarystructures (e.g. alpha helix and beta sheet). By sampling in the latent space,LSSAMP can simultaneously generate peptides with ideal sequence attributes andsecondary structures. Experimental results show that the peptides generated byLSSAMP have a high probability of antimicrobial activity. Our wet laboratoryexperiments verified that two of the 21 candidates exhibit strong antimicrobialactivity. The code is released at https://github.com/dqwang122/LSSAMP.

Accelerating Antimicrobial Peptide Discovery with Latent Structure