Janzen, E. | Search bioRxiv

bioRxiv2021

Error minimization and specificity could emerge in a genetic code as by-products ofprebiotic evolution

The emergence of the genetic code was a major transition in the evolution from a prebiotic RNA world to the earliest modern cells1. A prominent feature of the standard genetic code is error minimization, or the tendency of mutations to be unusually conservative in preserving biophysical features of the amino acid2-6. While error minimization is often assumed to result from natural selection, it has also been speculated that error minimization may be a by-product of emergence of the genetic code3. During establishment of the genetic code in an RNA world, self-aminoacylating ribozymes would enforce the mapping of amino acids to anticodons. Here we show that expansion of the genetic code, through co-option of ribozymes for new substrates, could result in error minimization as an emergent property. Using self-aminoacylating ribozymes previously identified during an exhaustive search of sequence space7, we measured the activity of thousands of candidate ribozymes on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited preferences for biophysically similar substrates, indicating that co-option of existing ribozymes to adopt additional amino acids into the genetic code would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of the evolution and functional expansion of a ribozyme system could lead to adaptive properties of a genetic code. Such spandrels could thus underlie significant prebiotic developments.

biochemistry↗

bioRxiv2020

Kinetic sequencing (k-Seq) as a massively parallel assay for ribozyme kinetics: utility and critical parameters

Characterization of genotype-phenotype relationships of genetically encoded molecules (e.g., ribozymes) requires accurate quantification of activity for a large set of molecules. Kinetic measurement using high-throughput sequencing (e.g., k-Seq) is an emerging assay applicable in various domains that potentially scales up measurement throughput to 105 ~ 106 unique sequences. However, technical challenges introduced by sequence heterogeneity and DNA sequencing must be understood to realize the utility and limitations of such assays. We characterized the k-Seq method in terms of model identifiability, effects of sequencing error, accuracy and precision using simulated datasets and experimental data from a variant pool constructed from previously identified ribozymes. Relative abundance, kinetic coefficients, and measurement noise were found to affect the measurement of each sequence. We introduced bootstrapping to robustly quantify the uncertainty in estimating model parameters and proposed interpretable metrics to quantify model identifiability. These efforts enabled the rigorous reporting of data quality for individual sequences in k-Seq experiments. Critical experimental factors were examined, and general guidelines are proposed to maximize the number of sequences having precisely estimated and identifiable kinetic coefficients from k-Seq data. Practices analogous to those laid out here could be applied to improve the rigor of similar sequencing-based assays.

biochemistry↗