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High-resolution sequence-function mapping of full-length proteins.

Kowalsky CA, Klesmith JR, Stapleton JA, Kelly V, Reichkitzer N, Whitehead TA - PLoS ONE (2015)

Bottom Line: Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest.We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections.Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, United States of America.

ABSTRACT
Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest. Deep sequencing of library DNA allows frequency reconstruction for tens of thousands of variants in a single experiment, yet short read lengths of current sequencers makes it challenging to probe genes encoding full-length proteins. Here we extend the scope of sequence-function maps to entire protein sequences with a modular, universal sequence tiling method. We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections. Using this protocol, sequence-function maps covering full sequences can be obtained in four to six weeks. Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

No MeSH data available.


Related in: MedlinePlus

Growth Selection Parameters.The parameters of growth-based selections should be chosen such that the range of enrichment ratios for the population lies between-4 and 4. A. Enrichment ratios as a function of the individual growth rate compared to the population growth rate. Following one generation of population growth (blue) the enrichment ratios remain around zero. Increasing the number of population generations the experiment is allowed to grow (1 generation, blue, 5 generations, red and 10 generations, green) increases the experimental resolution in discriminating mutant growth phenotypes. B. Enrichment ratios as a function of average population generation (gp) for various . For  values less than one (0.1, red; 0.5, blue; and 0.667, green) the enrichment ratios decrease with increasing population generations. Variants with values  values above one (1.5, black) show enhanced enrichment with increasing population generations.
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pone.0118193.g004: Growth Selection Parameters.The parameters of growth-based selections should be chosen such that the range of enrichment ratios for the population lies between-4 and 4. A. Enrichment ratios as a function of the individual growth rate compared to the population growth rate. Following one generation of population growth (blue) the enrichment ratios remain around zero. Increasing the number of population generations the experiment is allowed to grow (1 generation, blue, 5 generations, red and 10 generations, green) increases the experimental resolution in discriminating mutant growth phenotypes. B. Enrichment ratios as a function of average population generation (gp) for various . For values less than one (0.1, red; 0.5, blue; and 0.667, green) the enrichment ratios decrease with increasing population generations. Variants with values values above one (1.5, black) show enhanced enrichment with increasing population generations.

Mentions: Growth selections should be designed such that the number of generations the culture is allowed to grow fits a reasonable time frame (under 2 days) and there is high resolution of fitness for the entire library. Fig. 4 shows the enrichment ratios for a range of specific growth rates relative to the population-averaged growth rate for different numbers of doubling times. According to these results, the dynamic range of protein activities is maximized between five and ten doubling times. This range allows resolution of all variants with growth rates above 0.2 of the population-averaged growth rate. Furthermore, limiting the number of doublings minimizes the effect of spontaneous mutations in the background strain [43].


High-resolution sequence-function mapping of full-length proteins.

Kowalsky CA, Klesmith JR, Stapleton JA, Kelly V, Reichkitzer N, Whitehead TA - PLoS ONE (2015)

Growth Selection Parameters.The parameters of growth-based selections should be chosen such that the range of enrichment ratios for the population lies between-4 and 4. A. Enrichment ratios as a function of the individual growth rate compared to the population growth rate. Following one generation of population growth (blue) the enrichment ratios remain around zero. Increasing the number of population generations the experiment is allowed to grow (1 generation, blue, 5 generations, red and 10 generations, green) increases the experimental resolution in discriminating mutant growth phenotypes. B. Enrichment ratios as a function of average population generation (gp) for various . For  values less than one (0.1, red; 0.5, blue; and 0.667, green) the enrichment ratios decrease with increasing population generations. Variants with values  values above one (1.5, black) show enhanced enrichment with increasing population generations.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4366243&req=5

pone.0118193.g004: Growth Selection Parameters.The parameters of growth-based selections should be chosen such that the range of enrichment ratios for the population lies between-4 and 4. A. Enrichment ratios as a function of the individual growth rate compared to the population growth rate. Following one generation of population growth (blue) the enrichment ratios remain around zero. Increasing the number of population generations the experiment is allowed to grow (1 generation, blue, 5 generations, red and 10 generations, green) increases the experimental resolution in discriminating mutant growth phenotypes. B. Enrichment ratios as a function of average population generation (gp) for various . For values less than one (0.1, red; 0.5, blue; and 0.667, green) the enrichment ratios decrease with increasing population generations. Variants with values values above one (1.5, black) show enhanced enrichment with increasing population generations.
Mentions: Growth selections should be designed such that the number of generations the culture is allowed to grow fits a reasonable time frame (under 2 days) and there is high resolution of fitness for the entire library. Fig. 4 shows the enrichment ratios for a range of specific growth rates relative to the population-averaged growth rate for different numbers of doubling times. According to these results, the dynamic range of protein activities is maximized between five and ten doubling times. This range allows resolution of all variants with growth rates above 0.2 of the population-averaged growth rate. Furthermore, limiting the number of doublings minimizes the effect of spontaneous mutations in the background strain [43].

Bottom Line: Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest.We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections.Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan, United States of America.

ABSTRACT
Comprehensive sequence-function mapping involves detailing the fitness contribution of every possible single mutation to a gene by comparing the abundance of each library variant before and after selection for the phenotype of interest. Deep sequencing of library DNA allows frequency reconstruction for tens of thousands of variants in a single experiment, yet short read lengths of current sequencers makes it challenging to probe genes encoding full-length proteins. Here we extend the scope of sequence-function maps to entire protein sequences with a modular, universal sequence tiling method. We demonstrate the approach with both growth-based selections and FACS screening, offer parameters and best practices that simplify design of experiments, and present analytical solutions to normalize data across independent selections. Using this protocol, sequence-function maps covering full sequences can be obtained in four to six weeks. Best practices introduced in this manuscript are fully compatible with, and complementary to, other recently published sequence-function mapping protocols.

No MeSH data available.


Related in: MedlinePlus