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

• Published in: Nature communications 2022-08, Vol.13 (1), p.4847-4847, Article 4847
• Main Authors: Rotrattanadumrong, Rachapun, Yokobayashi, Yohei
• Format: Article
• Language: English
• Subjects: 38/23; 631/181/2475; 631/181/735; 631/337/1910; 631/45/500; Algorithms; Biomolecules; Deep learning; Empirical analysis; Epistasis; Evolutionary algorithms; Fitness; Genetic algorithms; Genotypes; Humanities and Social Sciences; Machine learning; multidisciplinary; Mutation; Networks; Nucleotide sequence; Phenotypes; Reproductive fitness; Ribozymes; RNA ligase; Science; Science (multidisciplinary); Sequences
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-32538-z

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 2 16 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.