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

Jonathan C. Chen, Jonathan P. Chen, Max W. Shen, Michael Wornow, Minwoo Bae, Wei-Hsi Yeh, Alvin Hsu and David R. Liu ()
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Jonathan C. Chen: Broad Institute of Harvard and MIT
Jonathan P. Chen: Uber Technologies, Inc.
Max W. Shen: Broad Institute of Harvard and MIT
Michael Wornow: Broad Institute of Harvard and MIT
Minwoo Bae: Broad Institute of Harvard and MIT
Wei-Hsi Yeh: Broad Institute of Harvard and MIT
Alvin Hsu: Broad Institute of Harvard and MIT
David R. Liu: Broad Institute of Harvard and MIT

Nature Communications, 2022, vol. 13, issue 1, 1-17

Abstract: 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 KD = 5–65 nM). We then use the selection data to train a conditional variational autoencoder (CVAE) machine learning model to generate diverse and unique HFNAP sequences with high daunomycin affinities (KD = 9–26 nM), even though they are unrelated in sequence to experimental polymers. Coupling in vitro selection with a machine learning model thus enables direct generation of active variants, demonstrating a new approach to the discovery of functional biopolymers.

Date: 2022
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DOI: 10.1038/s41467-022-31955-4

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