DOI: 10.5061/dryad.nm1189t
title: Data from: Mapping a systematic ribozyme fitness landscape reveals a frustrated evolutionary network for self-aminoacylating RNA

description: Molecular evolution can be conceptualized as a walk over a 'fitness
 landscape', or the function of fitness (e.g., catalytic activity)over
 the space of all possible sequences. Understanding evolution requires
 knowing the structure of the fitness landscape and identifying the viable
 evolutionary pathways through the landscape. However, the fitness
 landscape for any catalytic biomolecule is largely unknown. The evolution
 of catalytic RNA is of special interest because RNA is believed to have
 been foundational to early life. In particular, an essential activity
 leading to the genetic code would be the reaction of ribozymes with
 activated amino acids, such as5(4H)-oxazolones, to form aminoacyl-RNA.
 Here we combine in vitro selection with a massively parallel kinetic assay
 to map a fitness landscape for self-aminoacylating RNA, with nearly
 complete coverage of sequence space in a central 21-nucleotide region. The
 method (SCAPE: sequencing to measure catalytic activity paired with in
 vitro evolution) shows that the landscape contains three major ribozyme
 families (landscape peaks). An analysis of evolutionary pathways shows
 that, while local optimization within a ribozyme family would be possible,
 optimization of activity over the entire landscape would be frustrated by
 large valleys of low activity. The sequence motifs associated with each
 peak represent different solutions to the problem of catalysis, so the
 inability to traverse the landscape globally corresponds to an inability
 to restructure the ribozyme without losing activity. The frustrated nature
 of the evolutionary network suggests that chance emergence of a ribozyme
 motif would be more important than optimization by natural selection.
description: Correlation of Fitness
 Effects - scriptPython script to
 calculate the correlation of fitness effects in a given ribozyme
 family.ActivityCorrelationGamma.pyCorrelation of Fitness Effects - source fileExcel source file used as input for the script ActivityCorrelationGamma.py.ActivityObservedData.xlsxk-seq Analysis - scriptPython script to calculate catalytic kinetics for a population of sequences, using the k-Seq methodology.kseq_tools_v01.pyk-seq Analysis - example input filesInput files for k-seq Analysis: kseq_rounds is example-rounds.txt, normalization_list is example-normalization.txt, substrate_concs is example-subst-concs.txt, rounds_to_average is example-rnds-to-avg.txt and rounds_to_error is example-rnds-to-err.txt.example_input_files.zipPathways between Peak Sequences - scriptPython script to searche for the shortest pathway between two sequences along a fitness landscape.peak_pather_v01.pyFastq files for original selectionFastq files pertaining to sequences present in rounds of selection, from the original BYO aminoacylase ribozyme selection. Conserved primer regions have been removed; sequences correspond only to the randomized internal region of the library.selection-branch1-fastqs.zipCount reads files for original selectionText files pertaining to sequences present in rounds of selection, from the original BYO aminoacylase ribozyme selection. Each file contains round information in its header, followed by sequences and the number of times they appeared in that round.selection-branch1-counts.zipFastq files for second selectionFastq files pertaining to sequences present in rounds of selection, from the second BYO aminoacylase ribozyme selection.selection-branch2-fastqs.zipCount reads files for second selectionText files pertaining to sequences present in rounds of selection, from the second BYO aminoacylase ribozyme selection. Each file contains round information in its header, followed by sequences and the number of times they appeared in that round.selection-branch2-counts.zipCount reads files for post-kseq populationsText files pertaining to sequences present in post-kseq populations, from the kseq assay performed at the end of selection. A, B, C etc. pertain to replicates; 1-indicates 250uM BYO, 2-indicates 50uM, 3-indicates 10uM, 4-indicates 2uM. Each file contains round information in its header, followed by sequences and the number of times they appeared in that round.kseq-counts.zip