Fig. 1: The workflow for de novo-development of AMPs via deep learning and cell-free biosynthesis. | Nature Communications

Fig. 1: The workflow for de novo-development of AMPs via deep learning and cell-free biosynthesis.

From: Cell-free biosynthesis combined with deep learning accelerates de novo-development of antimicrobial peptides

Fig. 1: The workflow for de novo-development of AMPs via deep learning and cell-free biosynthesis.

a Generative variational autoencoders (VAE) for de novo-design of AMPs after being trained on known AMP sequences. b Predictive convolutional or recurrent neural networks as regressors for the MIC prediction after being trained on known AMPs and their MIC. c Trained generative and predictive models are used for sampling from the latent space (de novo-design of AMPs) and prioritization of AMPs (predicting their MIC), respectively. d Experimental pipeline for rapid cell-free biosynthesis of the designed AMPs from synthetic DNA fragments and direct testing of produced AMPs in the cell-free mix to bacterial cultures followed by overnight continuous growth assay. Created with BioRender.com.

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