Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences

Pressman, Abe; Moretti, Janina E.; Campbell, Gregory W.; Müller, Ulrich F.; Chen, Irene

doi:10.1093/nar/gkx816

Cited by 19 publications

(15 citation statements)

References 42 publications

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“…The log-normal shape of the frequency distribution of catalytic activity k s A s , is consistent with prior findings. 24,48 Because the rate constant scales exponentially with the activation energy, it was of interest to determine the distribution of k s alone. For the highest activity family (2.1), many ribozymes could be characterized by k s and A s separately.…”

Section: Resultsmentioning

confidence: 99%

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

et al. 2019

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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 as 5(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.

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Section: Resultsmentioning

confidence: 99%

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

et al. 2019

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“…Thus, new approaches use HTS to perform direct activity screens (47)(48)(49). Furthermore, fitness estimates can be notably improved by considering multiple rounds of selection (46).…”

Section: Measuring Molecular Fitness Landscapes With High-throughput mentioning

confidence: 99%

Molecular Fitness Landscapes from High-Coverage Sequence Profiling

Blanco

Janzen

Pressman

et al. 2019

Annu. Rev. Biophys.

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The function of fitness (or molecular activity) in the space of all possible sequences is known as the fitness landscape. Evolution is a random walk on the fitness landscape, with a bias toward climbing hills. Mapping the topography of real fitness landscapes is fundamental to understanding evolution, but previous efforts were hampered by the difficulty of obtaining large, quantitative data sets. The accessibility of high-throughput sequencing (HTS) has transformed this study, enabling large-scale enumeration of fitness for many mutants and even complete sequence spaces in some cases. We review the progress of high-throughput studies in mapping molecular fitness landscapes, both in vitro and in vivo, as well as opportunities for future research. Such studies are rapidly growing in number. HTS is expected to have a profound effect on the understanding of real molecular fitness landscapes.

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“…Comprehensive functional maps that assess the impact of all possible point and pairwise mutations, for example, can improve our understanding of sequence-structure-function relationships and guide efforts to engineer novel biocatalysts. In addition, comparisons of such maps with other systematic datasets, such those reporting natural sequence conservation 9 , or selection fitness in vitro [32][33][34][35] or in vivo 18,19 , can suggest mechanisms that underlie the higher-order effects of sequence variations. In practice, the sheer number of measurements required to score the kinetics of all single and double mutants surpasses the scale accessible to traditional biochemistry.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme

et al. 2020

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Massively parallel, quantitative measurements of biomolecular activity across sequence space can greatly expand our understanding of RNA sequence-function relationships. We report the development of an RNA-array assay to perform such measurements and its application to a model RNA: the core glmS ribozyme riboswitch, which performs a liganddependent self-cleavage reaction. We measure the cleavage rates for all possible single and double mutants of this ribozyme across a series of ligand concentrations, determining k cat and K M values for active variants. These systematic measurements suggest that evolutionary conservation in the consensus sequence is driven by maintenance of the cleavage rate. Analysis of double-mutant rates and associated mutational interactions produces a structural and functional mapping of the ribozyme sequence, revealing the catalytic consequences of specific tertiary interactions, and allowing us to infer structural rearrangements that permit certain sequence variants to maintain activity.

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Analysis of in vitro evolution reveals the underlying distribution of catalytic activity among random sequences

Cited by 19 publications

References 42 publications

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

Mapping a Systematic Ribozyme Fitness Landscape Reveals a Frustrated Evolutionary Network for Self-Aminoacylating RNA

Molecular Fitness Landscapes from High-Coverage Sequence Profiling

Comprehensive sequence-to-function mapping of cofactor-dependent RNA catalysis in the glmS ribozyme

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