Generative design of programmable asymmetric β-barrel nanopores

Native homo-oligomeric transmembrane {beta}-barrels (TMBs) have been widely explored for molecular sensing, sequencing, and separation applications, but their uniform lumen limits spatial resolution and the localized interactions required for both analyte discrimination and filtration. Monomeric TMBs have been designed using energy-based methods but this has required extensive expert input, limiting scalability and functionality. Here, we present a rapid generative AI method for TMB design that combines diffusion-based backbone generation conditioned on {beta}-barrel structural features with TMB-optimized sequence design. We characterized 48 designs (spanning 10-16 strands) that exhibited measurable conductances pertaining to pore diameters of 0.7-1.5 nm, and determined crystal structures of two designs that have atomic-level agreement with the designed models. We demonstrate the versatility of the generative method by designing nanopores with copper binding sites for selective ion sensing, larger pores that support DNA translocation, and longer pores with increased hydrophobic thickness, that mediate ion transport across three-dimensional networks of monoglyceride bilayers and synthetic polymer-lipid hybrid membranes composed of block copolymers.