Analyses of the effects of all ubiquitin point mutants on yeast growth rate

By Benjamin P. Roscoe, Kelly M. Thayer, Konstantin B. Zeldovich, D. Fushman, Daniel N. A. Bolon. 2013

232 citations recorded by the source corpus.

The amino acid sequence of a protein governs its function. We used bulk competition and focused deep sequencing to investigate the effects of all ubiquitin point mutants on yeast growth rate. Many aspects of ubiquitin function have been carefully studied, which enabled interpretation of our growth analyses in light of a rich structural, biophysical and biochemical knowledge base. In one highly sensitive cluster on the surface of ubiquitin almost every amino acid substitution caused growth defects. In contrast, the opposite face tolerated virtually all possible substitutions. Surface locations between these two faces exhibited intermediate mutational tolerance. The sensitive face corresponds to the known interface for many binding partners. Across all surface positions, we observe a strong correlation between burial at structurally characterized interfaces and the number of amino acid substitutions compatible with robust growth. This result indicates that binding is a dominant determinant of ubiquitin function. In the solvent inaccessible core of ubiquitin all positions tolerated a limited number of substitutions, with hydrophobic amino acids especially interchangeable. Some mutations null for yeast growth were previously shown to populate folded conformations indicating that for these mutants subtle changes to conformation caused functional defects. The most sensitive region to mutation within the core was located near the C-terminus that is a focal binding site for many critical binding partners. These results indicate that core mutations may frequently cause functional defects through subtle disturbances to structure or dynamics.

Subfields: Cellular and Molecular Neuroscience, Molecular Biology

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In Cellular and Molecular Neuroscience

Daniel N. A. Bolon

In Molecular Biology

Citations in the corpus

Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein (2013, background): “…has been used to study the in vivo effect of all possible 171 single amino acid substitutions across a 9-amino acid-long stretch of the yeast Hsp90 (Hietpas et al. 2011) and many of the possible 1425 single amino acid substitutions in the 75-amino acid-long ubiquitin sequence (Roscoe et al. 2013).”
A model of substitution trajectories in sequence space and long-term protein evolution. (2015, background): “…many or long trajectories of substitutions using the full ﬁtness matrix may still be troublesome, although this has been done experimentally for a small number of proteins with very short trajectories (McLaughlin et al. 2012; Gong et al. 2013; Roscoe et al. 2013; Thyagarajan and Bloom 2014).”
A Comprehensive, High-Resolution Map of a Gene’s Fitness Landscape (2014, background): “…experiments or experiments in which alleles are enriched based on a threshold for function are the current state of the art (Fowler et al. 2010; Araya et al. 2012; Deng et al. 2012; McLaughlin et al. 2012; Schlinkmann et al. 2012; Whitehead et al. 2012; Roscoe et al. 2013; Starita et al. 2013).”
Physical model of the genotype-to-phenotype map of proteins (2016, methodology): “By sequence alignment methods [33, 41–43], it is possible to test whether these sensitive positions in the protein exhibit strong correlations in the gene, as predicted by the model.”
Quantifying and understanding the fitness effects of protein mutations: Laboratory versus nature (2016, methodology): “The DFE for ubiquitin obtained by systematic mapping using saturation mutagenesis and deep sequencing.(8) The presented DFEs address nonsynonymous mutations only.”
Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels (2013, background): “In previous work, we demonstrated that EMPIRIC measurements of fitness based on measures of plasmid abundance correlate strongly with cellular growth rates for a large set of mutants [43], indicating that plasmid abundance and cellular abundance are coupled.”
Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics (2014, background): “The latter can be evaluated either from competition assays or direct measurements of growth rates [10 ,74] or through deep sequencing approaches [76–78].”