High-throughput analysis of in vivo protein stability

Abstract

Determining the half-life of proteins is critical for an understanding of virtually all cellular processes. Current methods for measuring in vivo protein stability, including large-scale approaches, are limited in their throughput or in their ability to discriminate among small differences in stability. We developed a new method, Stable-seq, which uses a simple genetic selection combined with high-throughput DNA sequencing to assess the in vivo stability of a large number of variants of a protein. The variants are fused to a metabolic enzyme, which here is the yeast Leu2 protein. Plasmids encoding these Leu2 fusion proteins are transformed into yeast, with the resultant fusion proteins accumulating to different levels based on their stability and leading to different doubling times when the yeast are grown in the absence of leucine. Sequencing of an input population of variants of a protein and the population of variants after leucine selection allows the stability of tens of thousands of variants to be scored in parallel. By applying the Stable-seq method to variants of the protein degradation signal Deg1 from the yeast Matα2 protein, we generated a high-resolution map that reveals the effect of ∼30,000 mutations on protein stability. We identified mutations that likely affect stability by changing the activity of the degron, by leading to translation from new start codons, or by affecting N-terminal processing. Stable-seq should be applicable to other organisms via the use of suitable reporter proteins, as well as to the analysis of complex mixtures of fusion proteins.

Fig. 1.

Overview of Stable-seq. A, variants of a protein are fused to a biosynthetic enzyme that serves as a reporter protein. The variants determine the stability of the reporter, and thereby the growth rate of yeast. A library of plasmids encoding variants fused to such a reporter is constructed, transformed into yeast, and selected for reporter function. Plasmids isolated before and after selection are subjected to high-throughput sequencing. The change in the frequency of each variant is a measure of its stability. B, library design and sequence of Deg1. Residues 3–34 selected for doping to generate a Deg1 mutant library are highlighted in yellow.

Fig. 2.

Verification of the Stable-seq assay. A, spotting assay of Deg1–Leu2 variants with 5-fold serial dilutions. Growth on the -Ura plate, which requires only the presence of the URA3 transformation marker, serves as the spotting control, and growth on the -Leu -Ura plate selects for stable versions of Leu2. B, Western blot analysis of C-terminally FLAG-tagged Deg1–Leu2 variants in DOA⁺ and doa10Δ cells. The full-length Deg1–Leu2 and Deg1–Leu2^M1Δ proteins are unstable in DOA⁺ cells, but Deg1–Leu2 produces a Leu2-sized band. Both Deg1–Leu2 and Deg1–Leu2^M1Δ produce a full-length band in doa10Δ cells, as well as a smaller band that runs between Deg1–Leu2 and Leu2 that is likely due to cleavage of the full-length protein.

Fig. 3.

Selection assay and sequence analysis of Deg1–Leu2^M1Δ stability. A, a library of Deg1–Leu2^M1Δ variants transformed into yeast and plated without (-Ura) and with (-Leu -Ura) selection for stabilized Leu2^M1Δ. 100 times more cells were plated on the selection plate for comparison. B, heat map of enrichment scores of single mutations, with the Deg1 residue numbers along the top (residues in which mutations identified by Johnson et al. (7) were found are shown in boxes) and all possible mutations on the left axis. In the heat map, wild-type Deg1 sequences are shown; mutations identified by Johnson et al. (7) are indicated with black squares, and missing data with gray squares. C, previously identified stabilizing mutations in Deg1. The growth of colonies in the spotting assay and the data from the sequencing are compared to β-galactosidase values of Deg1–β-galactosidase variants identified by Johnson et al (7). The mutations identified by Johnson et al. (7) resulted in increases in stability, measured by β-galactosidase or pulse-chase assays, as shown (β-gal). The log₂E values are enrichment scores calculated from DNA sequence data: variant frequencies after leucine selection were divided by frequencies in the input library and then normalized to the wild-type ratio of frequencies.

Fig. 4.

Alternative start codon and its effect on in vivo protein stability. A, single mutant changes to methionine at each of residues 3–34 are represented with a heat map and bar plot. B, spotting assay of mutants containing an alternative start codon. These mutants were isolated from the pilot experiments to confirm the quality of the doped oligo library and to verify the assay design, and they include additional mutations. I4M; F30Y, I14M; F18I, I22M; D16A, I25M; S21R, L29R, I32M; D16A. C, heat map of double mutants containing a stop codon followed by a new methionine. Enrichment scores are represented as in Fig. 3B. Missing data are in gray.

Fig. 5.

N-terminal processing and its effect on in vivo protein stability. A, enrichment scores of mutations in codon 2. B, spotting assay of the N2 mutants with the highest log₂E scores identified in Fig. 5A. Changes to Lys or Arg resulted in good growth on the -Leu -Ura plate. C, effect of the nat3Δ allele on Deg1–Leu2^M1Δ production. BY4741 and nat3Δ strains carrying Deg1–Leu2^M1Δ variants were spotted on control and selection plates.

Fig. 6.

Analysis of epistatic effect of double mutants on protein stability. A, histogram of epistasis scores from 17,196 double mutants. B, spotting assay of double mutants with large positive epistasis, along with the constituent single mutants.

Fig. 7.

Prevalence and enrichment scores of stabilizing mutations. A, boxplots of groupings of stabilizing single mutations. Median values of log₂ enrichment scores are represented with thick black lines. The upper and lower quartiles (interquartile range (IQR)), maximum and minimum values except outliers, and outliers (greater or less than 1.5 times the IQR) are indicated with boxes, whiskers, and circles, respectively. B, fractions of sequence read counts of single mutations that stabilize are represented. Stabilizing mutations are grouped as previously identified by Johnson et al. (7); novel mutations in the same residues in which the mutations identified by Johnson et al. (7) were found; mutations that generate a new methionine, which likely serves as an alternative start codon; mutations at the second residue that may affect N-terminal processing and acetylation; and other stabilizing mutations. Data for codon 2 mutations are from the Deg1^3–34–Leu2 library.