Generating experimentally unrelated target molecule-binding highly functionalized nucleic-acid polymers using machine learning.

ABSTRACT: In vitro selection queries large combinatorial libraries for sequence-defined polymers with target binding and reaction catalysis activity. While the total sequence space of these libraries can extend beyond 10²² sequences, practical considerations limit starting sequences to ≤~10¹⁵ distinct molecules. Selection-induced sequence convergence and limited sequencing depth further constrain experimentally observable sequence space. To address these limitations, we integrate experimental and machine learning approaches to explore regions of sequence space unrelated to experimentally derived variants. We perform in vitro selections to discover highly side-chain-functionalized nucleic acid polymers (HFNAPs) with potent affinities for a target small molecule (daunomycin K

SUBMITTER: Chen JC 

PROVIDER: S-EPMC9352670 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature