Abstract
<jats:p>Ribonucleoproteins (RNPs) are powerful and precise genome editor proteins that promise to cure diseases with an underlying genetic basis. However, nanocarriers such as polymers or lipids still struggle to bind, encapsulate, and deliver fragile RNP payloads. Thus far, polymer chemists have focused on the chemical identity rather than the spatial arrangement of repeat units to identify design rules for polymer-mediated RNP delivery. To elucidate microstructure effects in polymer-mediated RNP delivery, we synthesized compositionally equivalent, length-matched statistical, block, and gradient copolymers. Then, we compared their polymer–RNP binding affinities, encapsulation efficiencies, polyplex sizes, and editing performance. Statistical copolymers, which are polymer ensembles with rich sequence diversity, bound RNPs more tightly and formed smaller (Rh ∼ 50 nm) polyplexes than structurally well-defined block copolymers, which surprisingly promoted polyplex aggregation. Further, microstructuredependent trends (statistical > gradient > block) in RNP binding strength propagated across cellular uptake and editing efficiency. For instance, statistical copolymersmatched state-of-the-art commercial reagents in non-homologous end joining (NHEJ) editing efficiency (60% at near-stoichiometric N/P ratios) whereas block copolymers performed no better than free RNPs. In human bronchial epithelial cells bearing G542X cystic fibrosis mutations, statistical and gradient copolymers exhibited robust NHEJ editing (25% for statistical copolymers S) with canonical spCas9 RNPs, suggesting that these polymers are promising for future investigations with base editor delivery. Our work reveals that RNP encapsulation and delivery demand chemical heterogeneity along polymer backbones supplied by a statistical distribution of hydrophobic, electrostatic, and hydrogen-bonding motifs. Microstructure is a powerful yet overlooked design handle that complements traditional compositional screening to tailor bespoke polymers for diverse genome editor payloads and cellular targets.</jats:p>