Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein
By Daniel Melamed, David L. Young, Caitlin E. Gamble, Christina R. Miller, Stanley Fields. 2013
231 citations recorded by the source corpus.
The RNA recognition motif (RRM) is the most common eukaryotic RNA-binding domain, with differences in its amino acid sequence affecting binding specificity and affinity. This study applied a deep mutational scanning approach to score the functional activity of more than 100,000 variants of the RRM2 domain of the yeast poly(A)-binding protein, Pab1. The authors use these data to refine the current understanding of the Pab1 domain and of RRM domains in general.
Subfields: Molecular Biology, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Molecular Biology
- Ben Lehner
- G. Diss
- Stanley Fields
- Douglas M. Fowler
- Jay Shendure
- Martin Weigt
- Lea M. Starita
- David L. Young
In Computational Biology & Bioinformatics
- Ben Lehner
- G. Diss
- Tyler N. Starr
- Matteo Figliuzzi
- Alexander Schug
- Douglas M. Fowler
- Jason J. Stephany
- Stanley Fields
Citations in the corpus
- Evolutionary context-integrated deep sequence modeling for protein engineering (2020, methodology): “We collected the fitness measurements of both single and double mutants of six proteins from previous DMS studies [37,49–53] .”
- Coevolutionary Landscape Inference and the Context-Dependence of Mutations in Beta-Lactamase TEM-1 (2015, background): “[20] Daniel Melamed, David L Young, Caitlin E Gamble, Christina R Miller, and Stanley Fields.”
- Variational auto-encoding of protein sequences (2017, methodology): “fitness measurements for double mutations of a reference sequence (wildtype) (Experimental measurements are yeast PABP mutants, see [8]).”
- Measuring the activity of protein variants on a large scale using deep mutational scanning (2014, methodology): “Therefore, we demonstrate deep mutational scanning using two exemplary cases: a phage-displayed short protein domain 4 and a longer protein sequenced by subassembly and assayed for activity in yeast 24 .”
- The genetic landscape of a physical interaction (2018, methodology): “Yeast transformation was performed in triplicates in parallel following a modified version of the protocol found in Melamed et al. (2013).”
- Epistasis in protein evolution (2016, background): “In one mutational scanning study, the most densely connected hotspots—the most nonspecifically coupled mutations—accounted for less than 20% of all epistatic interactions between mutations.(33) As expected, the more specific couplings typically involve direct interactions that modulate the effects o”