In vitro selections with RNAs of variable length converge on a robust catalytic core

Experimental exploration of a ribozyme neutral network using evolutionary algorithm and deep learning

A neutral network connects all genotypes with equivalent phenotypes in a fitness landscape and plays an important role in the mutational robustness and evolvability of biomolecules. In contrast to earlier theoretical works, evidence of large neutral networks has been lacking in recent experimental studies of fitness landscapes. This suggests that evolution could be constrained globally. Here, we demonstrate that a deep learning-guided evolutionary algorithm can efficiently identify neutral genotypes within the sequence space of an RNA ligase ribozyme. Furthermore, we measure the activities of all 2¹⁶ variants connecting two active ribozymes that differ by 16 mutations and analyze mutational interactions (epistasis) up to the 16th order. We discover an extensive network of neutral paths linking the two genotypes and reveal that these paths might be predicted using only information from lower-order interactions. Our experimental evaluation of over 120,000 ribozyme sequences provides important empirical evidence that neutral networks can increase the accessibility and predictability of the fitness landscape.

Neutral networks, which are sets of genotypes connected via single mutations that share the same phenotype, are important for evolvability. Here, the authors provide experimental evidence of a neutral network in an RNA enzyme using a high-throughput assay and deep learning.

High-Throughput Activity Profiling of RNA-Cleaving DNA Catalysts by Deoxyribozyme Sequencing (DZ-seq)

RNA-cleaving deoxyribozymes have found broad application as useful tools for RNA biochemistry. However, tedious in vitro selection procedures combined with laborious characterization of individual candidate catalysts hinder the discovery of novel catalytic motifs. Here, we present a new high-throughput sequencing method, DZ-seq, which directly measures activity and localizes cleavage sites of thousands of deoxyribozymes. DZ-seq exploits A-tailing followed by reverse transcription with an oligo-dT primer to capture the cleavage status and sequences of both deoxyribozyme and RNA substrate. We validated DZ-seq by conventional analytical methods and demonstrated its utility by discovery of novel deoxyribozymes that allow for cleaving challenging RNA targets or the analysis of RNA modification states.