Protein model discrimination using mutational sensitivity derived from deep sequencing.

By Bharat V. Adkar, Arti Tripathi, Anusmita Sahoo, Kanika Bajaj, Devrishi Goswami, Purbani Chakrabarti, M. Swarnkar, Rajesh S. Gokhale, Raghavan Varadarajan. 2012

88 citations recorded by the source corpus.

A major bottleneck in protein structure prediction is the selection of correct models from a pool of decoys. Relative activities of ∼1,200 individual single-site mutants in a saturation library of the bacterial toxin CcdB were estimated by determining their relative populations using deep sequencing. This phenotypic information was used to define an empirical score for each residue (RankScore), which correlated with the residue depth, and identify active-site residues. Using these correlations, ∼98% of correct models of CcdB (RMSD ≤ 4Å) were identified from a large set of decoys. The model-discrimination methodology was further validated on eleven different monomeric proteins using simulated RankScore values. The methodology is also a rapid, accurate way to obtain relative activities of each mutant in a large pool and derive sequence-structure-function relationships without protein isolation or characterization. It can be applied to any system in which mutational effects can be monitored by a phenotypic readout.

Subfields: Genetics, Molecular Biology

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Citations in the corpus

Deep mutational scanning of an RRM domain of the Saccharomyces cerevisiae poly(A)-binding protein (2013, background): “Given the striking signature in the mutational profile of core residues, these profiles can serve as a general approach to evaluate structures that have been defined in vitro, as previously shown (Adkar et al. 2012) or to refine folding predictions for proteins whose structure is unknown.”
Using deep mutational scanning to benchmark variant effect predictors and identify disease mutations (2020, unknown): “The E. coli toxin ccdB was assayed by reverse survival, investigating its ability to restrict cell growth (Adkar et al , 2012).”
Latent Effects of Hsp90 Mutants Revealed at Reduced Expression Levels (2013, background): “Because many mutant defects may be hidden to experimental measurement at the wild type expression level, our results suggest that ratelimiting functional sites in proteins may be more prevalent than previously appreciated, and provides a useful guide for interpreting the growing field of systematic ”
A rapid, efficient, and economical inverse polymerase chain reaction-based method for generating a site saturation mutant library. (2014, unknown): “To calculate the frequency of any base at a given position in a mutant codon, all plasmid sequences obtained following mutagenesis were aligned against the WT sequence of the CcdB gene.”
Measuring the activity of protein variants on a large scale using deep mutational scanning (2014, background): “Other cell-based assays have been developed to select for toxin activity 17 , chaperone activity 18 or drug resistance 19,20 .”