ArticleScience (New York, N.Y.)2016
The fitness landscape of a tRNA gene.
Article in Science (New York, N.Y.), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
98 citing papers in PubMed.
- Machine learning framework for cost effective deep mutational scanning through targeted substitution profiling.BMC bioinformatics · 2026Article
- Allelic Variation at tRNA Genes in Three Nematode Species Indicates Mutation Load Despite Strong Purifying Selection.Genome biology and evolution · 2026Article
- How Does Transcription-Associated Mutagenesis Shape tRNA Microevolution?Genome biology and evolution · 2026Article
- Mistranslation suppresses mistranscription in eukaryotes.Nature communications · 2026Article
- Sex-biased duplicates are rapidly generated duringbioRxiv : the preprint server for biology · 2025Article
- Exploring the space of self-reproducing ribozymes using generative models.Nature communications · 2025Article
- Deep generative model of RNAs based on variational autoencoder with context-free grammar.Bioinformatics (Oxford, England) · 2025Article
- On the Probability of Reaching High Peaks in Fitness Landscapes by Adaptive Walks.Molecular biology and evolution · 2025Article
- MMRT: MultiMut Recursive Tree for predicting functional effects of high-order protein variants from low-order variants.Computational and structural biotechnology journal · 2025Article
- The highly rugged yet navigable regulatory landscape of the bacterial transcription factor TetR.Nature communications · 2024Article
- Pleiotropy, epistasis and the genetic architecture of quantitative traits.Nature reviews. Genetics · 2024Review
- popDMS infers mutation effects from deep mutational scanning data.Bioinformatics (Oxford, England) · 2024Article
- Genotype sampling for deep-learning assisted experimental mapping of a combinatorially complete fitness landscape.Bioinformatics (Oxford, England) · 2024Article
- Predictive evolutionary modelling for influenza virus by site-based dynamics of mutations.Nature communications · 2024Article
- Deep generative design of RNA family sequences.Nature methods · 2024Article
- popDMS infers mutation effects from deep mutational scanning data.bioRxiv : the preprint server for biology · 2024Article
- Cryo-EM structure and functional landscape of an RNA polymerase ribozyme.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- The Boltzmann distributions of molecular structures predict likely changes through random mutations.Biophysical journal · 2023Article
- Article
- Evolvability-enhancing mutations in the fitness landscapes of an RNA and a protein.Nature communications · 2023Article
38 more citing papers are in PubMed but not listed here.
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4 authors.
Funding
Abstract
Fitness landscapes describe the genotype-fitness relationship and represent major determinants of evolutionary trajectories. However, the vast genotype space, coupled with the difficulty of measuring fitness, has hindered the empirical determination of fitness landscapes. Combining precise gene replacement and next-generation sequencing, we quantified Darwinian fitness under a high-temperature challenge for more than 65,000 yeast strains, each carrying a unique variant of the single-copy tRNA(CCU)(Arg) gene at its native genomic location. Approximately 1% of single point mutations in the gene were beneficial and 42% were deleterious. Almost half of all mutation pairs exhibited statistically significant epistasis, which had a strong negative bias, except when the mutations occurred at Watson-Crick paired sites. Fitness was broadly correlated with the predicted fraction of correctly folded transfer RNA (tRNA) molecules, thereby revealing a biophysical basis of the fitness landscape.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.