Generating high accuracy peptide binding data in high throughput with yeast surface display and SORTCERY

Abstract

Library methods are widely used to study protein-protein interactions, and high-throughput screening or selection followed by sequencing can identify a large number of peptide ligands for a protein target. In this chapter we describe a procedure called "SORTCERY" that can rank the affinities of library members for a target with high accuracy. SORTCERY follows a three-step protocol. First, fluorescence activated cell sorting (FACS) is used to sort a library of yeast displayed peptide ligands according to their affinities for a target. Second, all sorted pools are deep sequenced. Third, the resulting data are analyzed to create a ranking. We demonstrate an application of SORTCERY to the problem of ranking peptide ligands for the anti-apoptotic regulator Bcl-x_L.

1. Introduction

High-throughput analysis of functional mutations in proteins, peptides or DNA by deep sequencing is emerging as a powerful technique. Properties such as protein stability, enzymatic activity and peptide ligand or DNA binding have been studied [1–16]. The general approach involves screening a library of mutants or performing a selection for a desired function. Library sequences in pre- and post-selected pools are then identified by next-generation sequencing, and computational routines are used to extract information about how sequence relates to function.

Many selection or screening processes have been employed for these types of studies, including in vitro assays, phage display, yeast-surface display in combination with fluorescence activated cell sorting (FACS), and in vivo assays. Some studies have used the observed frequencies of mutant variants in selected pools to infer sequence-function relationships [1–5]. As an alternative measure, enrichment scores have been calculated from the ratio of pre- and post-selection frequencies [6–14]. The effects of mutations in particular sequence positions have been investigated, either by experimentally screening single-mutant libraries or by assuming positional independence during computational post-processing. Position weight matrices have been built that score binding, stability, and function from sequence using this approach, sometimes with correction for non-specific binding or consideration of enrichment changes over multiple selection rounds [5,12,13]. Analyzing single-residue substitutions benefits from enhanced statistical power, because it is easy to saturate a single-position sequence space. But important context dependent effects may be neglected in this type of analysis.

In this chapter we introduce a high-accuracy alternative to enrichment-based methods for probing mutational effects on the affinity of peptide ligands. Our protocol “SORTCERY” comprises the three steps of selection, deep sequencing and computational analysis (Figure 1 A). The selection process involves two-color cell sorting of a yeast-surface displayed library based on the expression levels of displayed peptides and levels of binding to a target (Figure 1 B). Our sorting protocol builds on reports that two-color FACS can accurately distinguish between binders of different affinities [15–19] and agrees with a theoretical model describing the expected signals for clones expressing peptides with a range of binding strengths [20]. This model can guide sorting of a library into pools according to binding affinity, and the pools can then be deep-sequenced to obtain information about individual library member affinities. SORTCERY extracts information from deep-sequenced library pools using computational routines that rank observed mutant sequences according to binding strength.

Figure 1.

A) SORTCERY combines experimental and computational protocols to rank peptide ligands according to their affinity for a target. Yeast-displayed peptides are sorted into pools that include ligands of similar affinity using FACS. Deep sequencing information is generated for each sample and the distribution of each sequence over the FACS gates is determined. Pairwise comparison of distributions permits the calculation of the probability that one peptide binds more strongly than another for each pair of peptides. A global rank order of affinities is computed from the probabilities. B) SORTCERY's yeast-display and gate-setting scheme. Peptide expression and target binding are detected via tags that are recognized by pairs of primary and fluorescently labeled secondary antibodies. Two-color cell sorting is based on these two signals. Gates are set to optimally separate binders of different affinities and to exclude non-binders and non-expressing cells.

Applying SORTCERY to study helical peptide affinities for the apoptosis-regulating protein Bcl-x_L, we obtained extremely accurate rankings for ~1000 sequences over a range of dissociation constants from 0.1 nM to 60 nM (Figure 2 A). Our study is described in reference [20] and the reader is referred to that paper for in-depth exposition of the theory underlying SORTCERY, the results when applied to Bcl-x_L, and further discussion of strengths and limitations of this method. A special variant of our approach is described here (Figure 2 B, Note 9), that can potentially be used to analyze much larger libraries.

Figure 2.

A) Individually measured dissociation constants vs. SORTCERY ranking indices for 19 sequences from a ranking of ~1000 sequences. Clones have been reindexed from 1 to 19. Error bars for rank indices are 95% bootstrap confidence intervals: error bars for dissociation constants indicate standard deviations for four individual measurements. B) Ranking indices for the same 19 clones as determined by convoluted SORTCERY (see Note 9).

2. Materials

2.1. Cell Culture Media

1. SD+CAA/SG+CAA: Dissolve 5 g casamino acids, 1.7 g yeast nitrogen base, 5.3 g ammonium sulfate, 10.2 g Na₂HPO₄-7H₂O and 8.6 g NaH₂PO4-H₂O in 700 ml water and autoclave for 15 min at 22 psi and 120 °C. For growth media (SD + CAA), dissolve 50 g glucose in 50 ml water then sterilize with a 0.2 µm filter. Add 40 ml of this 50% glucose solution to the autoclaved media and fill up to 1 l with sterile water. For induction media (SG + CAA), dissolve 20 g galactose in 100 ml water then sterilize with a 0.2 µm filter. Add 100 ml of this 20% galactose solution to the autoclaved media and fill up to 1 l with sterile water.

2.2 Fluorescence activated cell sorting

1. Low protein binding 0.45 µm filter plates or bottle-top filters

2. BSS pH 8.0: 50 mM Tris, 100 mM NaCl, 1 mg/ml BSA

3. Primary antibody mixture: anti-HA (Roche) 1:100 dilution and anti-Myc (Sigma) 1:100 dilution in BSS

4. Secondary antibody mixture: APC-labeled anti-mouse (BD Biosciences) 1:40 dilution and PE-labeled anti-rabbit (Sigma) 1:100 dilution in BSS

2.3 Deep Sequencing Sample Preparation (see Note 1)

1. Zymoprep Yeast Plasmid Miniprep I (Zymo Research)

2. Isopropanole

3. High-Fidelity DNA Polymerase (e.g. Phusion)

4. Thermocycler

5. Gel equipment

6. PCR purification and gel extraction kits (QiaGen)

7. MmeI (New England Biolabs): MmeI restriction enzyme, NEB Cut Smart Buffer, 1 mM SAM

8. T4 Ligase

9. Primers and oligos

3. Methods

3.1 Cell growth and induction of yeast-surface display library (see Note 2)

1. Dilute cells to OD₆₀₀ of 0.05 in SD + CAA and grow for 8 h at 30 °C.

2. Dilute cells to OD₆₀₀ of 0.005 in SD + CAA and grow to OD of 0.1 – 0.4 at 30 °C.

3. Dilute cells to OD₆₀₀ of 0.025 in SG + CAA and grow to OD of 0.2 – 0.5 at 30 °C for induction of peptide expression.

3.2 Gate setting

1. SORTCERY uses a two-color FACS setup to monitor expression (F_e) and binding (F_b) signals on a log/log or biexponential scale. On a log(F_b) vs. log(F_e) plot, points of equal binding strength lie on a line with a slope of 1 [20]. Subdivide the log/log plot accordingly into areas (gates) of different affinities by dissecting it with lines of slopes of 1 (red lines in Figure 3). The number, position and spacing of the lines will affect the performance of the procedure. We recommend an equal spacing between lines as this will result in optimal resolution between binders of different affinities. The number of lines (and the resulting gates) depends on the required resolution. This can be determined by measuring the FACS profiles of several yeast-displayed standards (see Note 3). Lines should be positioned such that the gates cover an area from the strongest binders to the baseline signal of binding. FACS profiles of standards can help determine whether the experimental setup will generate samples with quality appropriate for a SORTCERY sort (see Note 4).

2. Gate boundaries should be set to exclude cells without significant expression signal and to prevent cells in the binding baseline from being captured in gates for higher affinities. Cutoffs can be established by monitoring the FACS profile of a non-binding yeast clone and noting: (1) the position of non-expressing cells (blob in the lower left corner of Figure 3), and (2) the binding baseline (lower right area in Figure 3). Determine appropriate cutoffs and set gate lower-edge boundaries accordingly (see example: green edges in Figure 3).

3. Cell sorters assign maximum signal values to any signal intensity above their scale of measurement. Such signals have, therefore, not been accurately determined. Exclude the maximum expression and binding signal areas from the gates by setting gate boundaries accordingly (see example: blue edges in Figure 3).

Figure 3.

Gate-setting for an affinity sort with 12 gates. The red, diagonal lines subdivide the axis of affinity into different intervals and thus insure that each gate corresponds to a unique range of dissociation constants. The green, lower- left borders exclude non-binding cells from higher-affinity gates and exclude non-expressing cells from all gates. The depicted FACS profile of a non-binder illustrates this. The blue, upper-right borders exclude cells with the maximum possible expression or binding signal, because affinities cannot be accurately estimated from such signals.

3.3 Cell sorting

1. Filter grown and induced yeast cells (step 3.1) and wash twice with BSS.

2. Incubate cells with target molecule in BSS for 2 h at 21 °C (see Notes 5 & 6). Shake gently during incubation.

3. Filter cells and wash twice with BSS.

4. Incubate with mixture of primary antibodies (20 µl per 10⁶ cells, see Notes 7 & 8) at 4 °C.

5. Filter cells and wash twice with BSS.

6. Incubate with mixture of secondary antibodies at 4 °C.

7. Filter cells and wash twice with BSS. Resuspend cells in BSS for sorting.

8. Sort cells into each individual gate and retain sorted pools for deep sequencing analysis (see Notes 9 & 10). Note the number of cells sorted into each pool. Also record the library distribution across all gates by recording how many cells hit each gate during a set time interval, e.g. a minute. This information is important for the deep sequencing analysis (section 3.5, point 4).

3.4 Deep sequencing sample preparation

3.4.1 DNA extraction

1. If >80,000 cells are sorted, spin cells down, aspirate supernatant and add 150 µl of solution 1 from the Zymoprep kit + 2 µl Zymolase. For smaller numbers of cells, directly add 50 µl of solution 1 per 100 µl cell suspension + 2 µl Zymolase per 150 µl total volume.

2. Incubate at 37 °C for 1 h on a shaker.

3. Successively add 150 µl of solutions 2 and 3 per 150 µl incubation volume and vortex after each addition, spin down precipitate and retain supernatant.

4. Add 1 volume isopropanol and 0.1 volume 3 M NaOAc to each volume of DNA extract. Store at −20 °C over night.

5. Spin at 13000 rpm at 4 °C for 10 min. Carefully remove supernatant. Resuspend DNA pellet in 20 µl sterile water (pellet may not be visible for small numbers of sorted cells).

3.4.2 DNA Amplification and adapter attachment

Most of this section is based on the excellent preparation protocol in [21].

1. For each sorted sample, separately, amplify DNA sequences encoding the peptide ligands out of plasmids by PCR. The 5' end of the forward primer needs to contain a binding site for the MmeI restriction enzyme: 5' GGGACCACCACCTCCGAC 3' (see Note 11). The 5' end of the reverse primer has to consist of a part of the Illumina adapter sequence: 5' CGGTCTCGGCATTCCTGC 3' (see Notes 12 & 13).

2. Purify PCR products with the Qiagen PCR-purification kit. Elute in 30 µl sterile water.

3. Digest the PCR product with the MmeI restriction enzyme. Incubate the digestion mixture for 1 h at 37 °C, then heat inactivate for 20 minutes at 80 °C (see Note 14).

   digestion reagents
   PCR product	12.5 µl
   1mM SAM	2.5 µl
   NEB Cut Smart Buffer	5 µl
   MmeI	5 µl per 8.6 pmol PCR product
   sterile water	fill up to 50 µl

4. Prepare double-stranded adapters by annealing single-stranded oligos. The forward strand should contain the standard Illumina read binding site [22], a unique barcode for multiplexing (Note 15) and a 3' TC, giving : 5' ACACTCTTTCCCTACACGACGCTCTTCCGATCTbarcodeTC 3'. The reverse complement strand should be 5' phosphorylated and lack the 5' GA 3' that would be complementary to the TC of the forward strand.

5. Ligate each digestion product with an adapter containing a unique barcode. Ligate for 30 minutes at 20 °C, then heat inactivate for 10 minutes at 65 °C.

6. Run the products of the ligation reaction on a gel. Gel purify the bands of correct size with the Qiaquick gel purification kit. Elute in 30 µl sterile water.

7. PCR-amplify the ligation product. Primers should contain overhangs that complete the Illumina adapter sequences.

   Forward: 5' AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCT 3'

   Reverse: 5' CAAGCAGAAGACGGCATACGAGATCGGTCTCGGCATTCCTGCATCTT 3'

   15 PCR cycles should be sufficient using Phusion polymerase.

8. Purify PCR products with the Qiagen PCR-purification kit. Elute in 30 µl sterile water.

9. Combine samples and perform a multiplexed deep sequencing run on an Illumina sequencer with the standard forward Illumina read primer: 5' ACACTCTTTCCCTACACGACGCTCTTCCGATCT 3'. If a reverse read is also to be carried out use a custom primer (see Note 16).

3.5 Computational analysis

1. Filter the Illumina data by only considering sequences with a high Phred score for the mutated positions and a low number of read errors in unmutated positions (see Note 17). If a reverse read has been performed that overlaps the forward read, compare complementary mutant codons and choose the version with the higher Phred score.

2. Assign each Illumina read to its sorted pool/gate by barcode identification.

3. Count the copies of each unique sequence across all pools. Discard sequences with low copy numbers when summing up counts from all gates. Calculate the number of sorted cells that each unique sequence likely originated from. Dividing the number of cells that were sorted into a pool by the number of sequence reads for this sample provides a rough estimate of the cells per read. As a rule of thumb, require at least 100 sorted cells for each observed sequence.

4. If a convoluted sort strategy was used, see Note 18. Otherwise, calculate the distribution over the gates for each unique sequence.

   $$f_{\mathit{\text{xj}}}=\frac{z_x\frac{n_{\mathit{\text{xj}}}}{{\sum }_i{n}_{\mathit{\text{xi}}}}}{{\sum }_y{z}_y\frac{n_{\mathit{\text{yj}}}}{{\sum }_i{n}_{\mathit{\text{yi}}}}}$$

   Here, f_xj is the normalized frequency of sequence j in gate x, n_xj is the number of reads of sequence j in deep sequencing data set x (which corresponds to gate x), z_x is the number of cells that hit gate x when measuring the distribution of cells across all gates.

5. Calculate all possible pairwise probabilities that a peptide A is a stronger binder than a peptide B and vice versa:

   $$p(A>B)={\sum }_x{f}_{\mathit{\text{xA}}}{\sum }_{y<x}{f}_{\mathit{\text{yB}}}$$

   Note that gate indices x and y are assigned from lowest to highest affinity gates, i.e. in the equation the sum over y runs over all gates that are smaller in affinity than gate x. Assign these probabilities as weights to the edges of a directed graph. The vertices of the graph represent peptides and the directed edge running from vertex B to vertex A indicates the assumption that peptide A is a stronger binder than peptide B(Figure 5 A).

6. Find the maximum linear subgraph by first applying the method described in [23]. To do this, randomly chose a peptide/vertex A. For each other peptide/vertex B, compare the edge weights of the two edges that connect it to A. If p(A>B) > p(B>A), ), then B is considered a worse binder than A, if p(B>A) > p(A>B) then B is considered a better binder than A. Group all peptides according to whether they are better vs. worse binders than A. Then, within each group, repeat the procedure of randomly choosing one peptide and evaluating all others with respect to it, continuing to subdivide the groups until an ordering from best to worst binder has been constructed. Determine the likelihood score for this ordering by summing up the logarithms of the edge weights for all directed edges that agree with the ordering (Figure 5 B). Repeat the procedure of constructing an ordering several times and retain the one with the best score. Further refine this ordering by inserting each individual peptide into all possible positions and keeping the new position if a better score is obtained. Run the routine several times, alternately starting with the best and the worst binding peptide. Finally, run a Monte-Carlo search in which moves correspond to exchanging the positions of two peptides in the ordering. The final result represents an affinity ranking of all peptides.

Figure 5.

A) A directed graph representing four peptide ligands and assumptions about their relative binding strengths. Each edge is weighted by the probability that the ligand at its base is a weaker binder than the ligand at its head. B) A linear subgraph of A). Note that no conflicting assumptions about binding strengths exist.