Directed evolution of antimicrobial peptides using multi-objective zeroth-order optimization

Antimicrobial peptides (AMPs) emerge as a type of promising therapeutic compounds that exhibit broad spectrum antimicrobial activity with high specificity and good tolerability. Natural AMPs usually need further rational design for improving antimicrobial activity and decreasing toxicity to human cells. Although several algorithms have been developed to optimize AMPs with desired properties, they explored the variations of AMPs in a discrete amino acid sequence space, usually suffering from low efficiency, lack diversity, and local optimum. In this work, we propose a novel directed evolution method, named PepZOO, for optimizing multi-properties of AMPs in a continuous representation space guided by multi-objective zeroth-order optimization. PepZOO projects AMPs from a discrete amino acid sequence space into continuous latent representation space by a variational autoencoder. Subsequently, the latent embeddings of prototype AMPs are taken as start points and iteratively updated according to the guidance of multi-objective zeroth-order optimization. Experimental results demonstrate PepZOO outperforms state-of-the-art methods on improving the multi-properties in terms of antimicrobial function, activity, toxicity, and binding affinity to the targets. Molecular docking and molecular dynamics simulations are further employed to validate the effectiveness of our method. Moreover, PepZOO can reveal important motifs which are required to maintain a particular property during the evolution by aligning the evolutionary sequences. PepZOO provides a novel research paradigm that optimizes AMPs by exploring property change instead of exploring sequence mutations, accelerating the discovery of potential therapeutic peptides.