Engineering short-sequence elements for condensate-like assemblies by de novo design

Biomolecular condensates form through phase separation, playing a crucial role in cellular organization and gene regulation. However, native condensate elements often suffer from high molecular weight and sequence redundancy, limiting their use in prokaryotic systems. To address this, we established an integrated framework combining generative artificial intelligence, computational prediction, and experimental validation. We began by constructing a benchmark dataset of eukaryotic-derived phase-separation elements in Escherichia coli. This dataset was used to build the PSVAE model for de novo sequence design and the LMPsPred classifier for high-throughput screening. Finally, we identified 13 short-sequence elements with low molecular weight (16-26 kDa) and minimal redundancy. Experimental validation confirmed that these elements formed condensates in E. coli, with enhanced protein recruitment compared to native elements. This study highlights the potential of AI-guided design to generate condensate-like assemblies and expands the repertoire of candidate phase-separation elements for prokaryotic systems.