Supplementary Information - Nature Research · Supplementary Information Large-scale analysis of...

Supplementary Information

Large-scale analysis of protein structural changes

Yuehan Feng, Giorgia De Franceschi, Abdullah Kahraman, Martin Soste, Andre Melnik, Paul Boersema, Patrizia Polverino de Laureto,

Yaroslav Nikolaev, Ana Paula Oliveira and Paola Picotti

Nature Biotechnology: doi:10.1038/nbt.2999

Supplementary Table 2. Reproducibility of LiP-SRM analyses.

PeptideAverage

Fold Change (Apo/Holo)

StandardDeviation % CV

GLSDGEWQQVLNVWGK 1.6 0.12 7.5VEADIAGHGQEVLIR 1.6 0.37 22.6LFTGHPETLEK 2.3 0.17 7.4HGTVVLTALGGILK 1.4 0.06 4.5GHHEAELKPLAQSHATK 0.11 0.02 17.9GHHEAELKPL 8.0 1.35 17.0GHHEAELKPLAQ > 32.8 6.09 18.6YLEFISDAIIHVLHSK 1.3 0.21 15.6HPGDFGADAQGAMTK 2.6 0.21 8.2ALELFR 1.5 0.30 20.0

We used the apoMb/holoMb system, processed through a replicated LiP-SRM protocol to evaluate the reproducibility of LiP-SRM. Variability in the raw SRM intensities calculated from six replicated LiP-SRM analyses of apo- and holo-Mb spiked into a yeast proteome background is expressed as per cent coefficient of variation. % CVs result from the measurement of multiple SRM transitions for each peptide and from the six replicates (see also Supplementary Figure 3). In a separate experiment (bottom part of the table) the variability in the abundance (fold) changes for each peptide in the apoMb versus holoMb comparison was evaluated, based on two replicated LiP-SRM analyses. Average fold changes, standard deviations and % CVs result from the measurement of multiple SRM transitions for each peptide and the two biological replicates. The cleavage of the apoMb loop by PK was consistently observed only at the PL-AQ and AQ-SH peptide bonds for all analyzed replicates. Similar results were obtained when LiP was performed on a different day and by a different operator (data not shown). In both experiments the largest deviations are observed for peptides that disappear in one condition (e.g. GHHEAELKPLAQSHATK in apoMb samples subjected to LiP, or GHHEAELKPLAQ in holoMb samples subjected to LiP), as for these peptides the noise is integrated in that condition.


To assess whether LiP-SRM can be applied to biological samples where protein abundances change depending on the conditions applied, we evaluated whether the proteolytic patterns depend on the abundance of the protein substrate. We spiked holoMb or apoMb into replicates of a yeast proteome extract at two concentrations (10-fold difference, 1 pmol or 10 pmol of Mb species per µg of total yeast protein) and processed the same total amount of yeast protein extract (60 µg) through the LiP workflow. The amount of peptides generated in the double-digestion step was normalized to the abundance difference of the protein as determined by the trypsin controls. Variability in the proteolytic patterns is expressed as per cent coefficient of variation of the peptide fold change in the apoMb containing sample relative to the holoMb containing sample. % CVs reflect the measurement of different SRM transitions for each peptide, the two Mb dosages and two replicates.

Peptide% CV of Average

Fold Change (Apo/Holo)

GLSDGEWQQVLNVWGK 32.7VEADIAGHGQEVLIR 11.5LFTGHPETLEK 3.2HGTVVLTALGGILK 11.1GHHEAELKPLAQSHATK 19.3GHHEAELKPLAQ 30.6GHHEAELKPL 34.7HPGDFGADAQGAMTK 3.4ALELFR 4.1

Supplementary Table 3. Consistency of LiP-SRM patterns upon variation of protein abundance


Supplementary Note

Discovery-driven application of LiP-MS: choice of suitable large-scale relative

quantification techniques

In the discovery phase of our method, a variety of quantification approaches based on LC-MS could be deployed to identify HT and FT peptides that change in abundance when

comparing PK-treated and trypsin-only samples and across the two metabolic conditions. These approaches include: (1) label-free quantification based on intact peptide ion maps

(Bodenmiller et al, 2010); (2) quantification based on stable isotope-labelling, e.g. SILAC, iTRAQ or else (de Godoy et al., 2008; Mertins et al., 2012); (3) spectral counting (Weiss et

al., 2012); (3) quantification based on fragment ion maps, also known as data-independent acquisition or SWATH MS (Gillet et al., 2012). In our study, we applied first a spectral

counting approach, followed by SRM-based validation (glucose versus ethanol comparison). In a second discovery-based application (cell extracts +/- FBP) we then tested a label-free approach based on the alignment of MS1 peptide ion maps. The rationale for this choice is

explained below. Although less precise than quantification based on intact peptide signals in MS

spectra, the spectral counting approach is a robust semi-quantitative proteomic method. The main advantage of this approach for our LiP workflow is that spectral counting does not rely

on the correct alignment of maps of ion signals. The doubly-digested (trypsin + PK) and trypsin-only (control) samples are substantially different in their peptide content. Specifically,

while in the trypsin-only control peptides have the average length of a fully tryptic peptide and mostly end with positively charged amino acids (K and R), peptides produced upon

double-digestion are generally shorter, more hydrophilic and do not necessarily bear a positively charged residue at the C-terminus. The resulting peptide or fragment ion maps are

therefore very different in their shape and pattern, which poses significant challenges for their correct alignment. Thus, in the testing phase of our discovery approach we chose

spectral counting to avoid that our analysis could be affected by mis-alignment issues. A drawback of spectral counting might be its lower precision in extracting fold-changes for the

considered peptides. Therefore, in our workflow all peptide abundance changes of interest are validated and followed up by SRM measurements, a current gold-standard for precise

relative quantification. Once the discovery phase of our approach was tested and validated against existing

literature (glucose to ethanol transition and Cdc19 validation), in a second application (FBP screen) we applied an approach based on the alignment of MS1 ion maps. To avoid the

1


alignment issues described above we adopted a slightly modified analytical workflow. We first quantitatively compared MS1 maps from the trypsin-only controls with each other’s (step 1) and, separately, those from the doubly-digested samples with each other’s (step

2), using the alignment software Progenesis. We then normalized the results from step 2, using the results from step 1. This does not affect the logics of our normalization step, and

avoids forcing the alignment of drastically different MS1 maps. We statistically evaluated the results from triplicate experiments applying a fold change cut-off and a q-value cut-off

and identified significantly regulated peptides between FBP-treated and untreated samples. Only 6 peptides out of 12108 were found to be regulated when the two trypsin-

only controls were compared. In this experiment this is expected, as the addition of FBP is not supposed to affect the abundances of proteins in the lysate or trypsin cleavage

efficiency. We also compared the doubly-digested samples and identified novel structural transitions in the yeast proteome triggered by addition of FBP to the medium. We

compared the results of the MS1-based quantification with those obtained using spectral counting on the same samples. The two approaches yielded very similar results: 60% of

the peptides had the same regulation direction; of the ones that were regulated in opposite directions, 92% showed with both methods only a minor abundance change

between 0.5 and 2 fold, which does not fulfil our fold change cut-off of 2. We also confirmed that trying to align the trypsin-only and doubly-digested samples produces non-

sense results, with standard parameters for automatic alignment and in the absence of manual correction. We conclude that the MS1-based approach is applicable to our

method, though with a slightly revised analytical workflow and likely superior in terms of solidity and precision to the spectral counting approach. The spectral counting approach is

also “good enough” to identify interesting targets for subsequent SRM analysis, if no high-accuracy instrumentation is available.

Reliability of the discovery phase of the LiP approach

To evaluate the reliability of the discovery step of our method in terms of “false hit rate”, we designed the following experiment. We applied the double-digestion protocol to replicates of ApoMb or HoloMb spiked into a yeast proteome background (sample set

used in Supplementary Table 2) and evaluated the resulting MS1 peptide ion maps using the software Progenesis. When these two types of samples are compared, the only

protein changing conformational properties should be Mb, as the background proteome is identical. The analysis yielded in total 26 regulated peptides (abundance change cut-off,

2-fold; q-value < 0.02) out of 11,662 peptides identified in the experiment, at a FDR <1%.

2


Of these 11 were myoglobin peptides, including FT and HT peptides mapping to helix F and their missed cleaved forms. The other 15 were peptides associated to 15 different yeast proteins (1 peptide per protein). Thus, the false hit rate at the (peptide) quantification

level was of about 15/11,662, thus below 0.2% of the measured peptides (Supplementary Fig. 1). At the protein level, it was 15 out of 1,183, yielding a false hit rate of approximately

1.3%. In addition, multiple peptides were regulated for Mb while for the presumably incorrect hits only one peptide per protein appeared regulated. This suggests that

discarding proteins for which only one peptide is significantly regulated (as was done for the FBP experiment) might further reduce the false hit rate of the discovery phase. It can

not be excluded that the addition of the apo or holo forms of Mb (containing no and a small fraction of free heme group, respectively) affects the conformational properties of

other proteins in the yeast proteome, due to interaction effects like those observed for FBP. This in turn means that some of what appear false hits might have biological

meaning, thus possibly reducing further the actual false hit rate of the experiment. Influence of different experimental parameters on the results of LiP

We used the Mb system to assess the influence of different experimental

parameters on the results of LiP, including enzyme to substrate ratio, incubation time of the LiP protease, and use of different proteases. While holoMb was a stable protein,

mostly resistant to proteolytic cleavage even after one hour of incubation with PK, apoMb was cleaved at the PL-AQ peptide bond already after 1 min of incubation (Supplementary Figure 4). As expected, the amount of the HT peptide GHHEAELKP and FT peptide GHHEAELKPLAQSHATK for apoMB rapidly increased and decreased, respectively, with

increasing incubation times or amounts of PK. Incubation times of 1-5 min maximized the difference between the LiP patterns of holo and apoMb and are thus preferable for the conformational differentiation of the two structural states.

Longer incubation times (half an hour to one hour) or higher amounts of PK (E/S, 1/10), were less useful as they lead to the generation of secondary cleavage products (i.e.

initial proteolysis products were further digested to shorter peptides), thus complicating the interpretation of cleavage patterns (Supplementary Fig. 4). The use of different, low-

specificity proteases (thermolysin, subtilisin, and chymotrypsin, E/S, 1/100, incubation time, 5 to 15 minutes) for the LiP step, resulted in very similar results to those obtained

with PK (Supplementary Fig. 5). Cleavages of one or few peptide bonds were observed for the different proteases, all located between amino acid 82 and 94 of apoMb, the region

encompassing helix F in holoMb. The striking preferentiality of this set of promiscuous proteases for cleavages in the F-region of apoMb further confirms that the sites of intial

3


proteolytic cleavage are dictated by the structural features of the substrate, under LiP conditions.

Calculation of the conformational stoichiometry of apoMb/holoMb mixtures

We evaluated whether conformotypic peptides can be used to calculate the amount of the different protein conformations in a mixture of two protein

conformational states. We prepared different mixtures of holo and apoMb, containing 90, 50 and 10% of holoMb, respectively and spiked each of them into replicates of a

yeast proteome background. We then measured peptides GHHEAELKPL and GHHEAELKPLAQSHATK, using four SRM transitions per peptide and a label-free

approach, in the different samples. We also measured samples containing 100% ApoMb or 100% HoloMb as a reference. We extracted the signal intensity (area

under the SRM curve) for each transition measured. The resulting values are below.

We then applied the following equations to each SRM transition:

For each SRM transition we then solved the system with respect of the amount of ApoMB in each mixture, with the following results:

Peptide Precursor m/z Fragment m/z Fragm. 0% Holo 10% Holo 50% Holo 90% Holo 100% HoloGHHEAELKPL 377.5366 670.4134 y6 487762 357408 327366 79168 24611GHHEAELKPL 377.5366 599.3763 y5 1963840 1394899 1271984 305417 10961GHHEAELKPL 377.5366 470.3337 y4 2831360 2075771 1870630 444381 165290GHHEAELKPL 377.5366 357.2496 y3 3792501 2942524 2461851 609720 469102

GHHEAELKPLAQSHATK 464.2459 855.4683 y8 33941 51969 175824 262408 324569GHHEAELKPLAQSHATK 464.2459 742.3842 y7 127450 200822 652925 961245 1182775GHHEAELKPLAQSHATK 464.2459 671.3471 y6 40586 61775 219048 311896 383057GHHEAELKPLAQSHATK 464.2459 319.1976 y3 60164 93758 307253 457825 522555

SRM signal unknown sample = SRM signalHolo + SRM signalApoSRM signal Holo = SRM signal100% Holo * FractionHoloSRM signal Apo= SRM signal100% Apo * FractionApoFractionHolo + FractionApo = 1

Supplementary Table 4. SRM measurement of conformotypic peptides for holo- and apoMb in a holoMb/apoMb mixture

4


Supplementary References 1. Bodenmiller B, et al., Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci Signal. 2010 Dec 21;3(153). 2. de Godoy LM,et al. Comprehensive mass-spectrometry-based proteome quantification of haploid versus diploid yeast. Nature. 2008 Oct 30;455(7217):1251-4. 3. Mertins P, et al. TRAQ labeling is superior to mTRAQ for quantitative global proteomics and phosphoproteomics. Mol Cell Proteomics. 2012 Jun;11(6). 4. Weiss M, Schrimpf S, Hengartner MO, Lercher MJ, von Mering C. Shotgun proteomics data from multiple organisms reveals remarkable quantitative conservation of the eukaryotic core proteome. Proteomics. 2010, 10(6):1297-306. 5. Gillet LC, et al., Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics. 2012 Jun;11(6). 6. Picotti P, et al., Modulation of the structural integrity of the helix F in apomyoglobin by single amino acid replacements. Protein Sci. 2004 Jun;13(6):1572-85 7. Evans SV & Bayer GD, High-resolution study of the three-dimensional structure of horse heart metmyoglobin. J Mol Biol. 1990 Jun 20;213(4):885-97.

We last calculated the average (different transitions per peptide) % of ApoMb in each mixture and the associated standard deviation. The results obtained are reported below, using each of the two peptides and both of them.

Peptide Average % Apo,in 10% Holo Mix

Average % Apo,in 50% Holo Mix

Average % Apo,in 90% Holo Mix

GHHEAELKPLAQSHATK 93.3 +/- 0.5 48.9 +/- 2.1 19.3 +/- 3.5GHHEAELKPL 72.2 +/- 1.5 63.5 +/- 2.4 10.4 +/- 4.5

GHHEAELKPLAQSHATKand GHHEAELKPL, combined

82.8 +/- 11.3 56.2 +/- 8.0 14.8 +/- 6.0

Supplementary Table 6. Comparison of the apoMb/holoMb mixture derived from the SRM measurement of conformotypic peptides for holo- and apoMb

5

Peptide Precursorm/z

Fragmentm/z

Fragm. Fraction Apo,in 10% Holo Mix

Fraction Apo,in Holo 50% Mix

Fraction Apo,in Holo 90% Mix

GHHEAELKPLAQSHATK 464.2459 855.4683 y8 0.938 0.512 0.214GHHEAELKPLAQSHATK 464.2459 742.3842 y7 0.930 0.502 0.210GHHEAELKPLAQSHATK 464.2459 671.3471 y6 0.938 0.479 0.208GHHEAELKPLAQSHATK 464.2459 319.1976 y3 0.927 0.466 0.140

GHHEAELKPL 377.5366 670.4134 y6 0.719 0.654 0.118GHHEAELKPL 377.5366 599.3763 y5 0.709 0.646 0.151GHHEAELKPL 377.5366 470.3337 y4 0.717 0.640 0.105GHHEAELKPL 377.5366 357.2496 y3 0.744 0.600 0.042

Supplementary Table 5. Calculation of the amount of holo- and apoMb in a holoMb/apoMb mixture using the intensities of the associated conformotypic peptides


1550160016501700

Wavenumber (cm-1)

Rel

ativ

e A

bsor

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1715 16801669

1654

1646 1640 1614

1576

1560

a

(a) ThT binding assay. α-Syn samples were diluted into a ThT containing buffer and emission fluorescence spectra were recorded after excitation at 440 nm. (b) Far-UV circular dichroism (CD) spectra. CD measurements show the transition of α-Syn from a random coil (band at 198 nm) to a β-sheet-rich (band at 218 nm) conformation. (c) Amide I bands of Fourier transform-infrared (FT-IR) spectra (top) and their relative second derivatives (bottom). Second derivative spectra were used to identify the different spectral components. The FT-IR spectrum of M-α-Syn is indicative of an unfolded structure (band at 1640 cm-1); the F-α-Syn spectrum shows the presence of a cross-β-structure (bands at 1614 cm-1 and 1680 cm-1).

Wavelength (nm)200 210 220 230 240 250

[q] x

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c M-α-Syn F-α-Syn

M-α-Syn F-α-Syn

M-α-Syn F-α-Syn

Supplementary Figure 1. Conformational characterization of monomeric (M-) and fibrillar (F-) α-Syn


Log2 (Fold change Yeast+HoloMb / Fold Change Yeast+ApoMb)

-Log

10 (q

-val

ue)

Data were obtained from six independent replicates of each conditions. X-axis: Log2 fold change. Y-axis: negative Log10 of q-value (FDR-adjusted p-value). C.V., coefficient of variation. The horizontal grey line marks the q-value threshold of 0.02 that is used to filter the data whereas the vertical grey lines mark the fold change cut-off of 5 in both directions.

Supplementary Figure 2. Volcano plot showing significantly regulated peptides when comparing yeast proteomes spiked-in with holo- or apoMb.


XIC of +MRM (55 pairs): 521.793/801.437 Da ID: ALF.GAIAAAHYIR.+2y7.light from Sample 13 (a1pk) of 20140215_GDF_Mb_6plicates.wiff (... Max. 2.2e5 cps.

10 15 20 25 30Time, min

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apoMb R1

apoMb R2

apoMb R5

apoMb R6

XIC of +MRM (55 pairs): 521.793/801.437 Da ID: ALF.GAIAAAHYIR.+2y7.light from Sample 19 (h1pk) of 20140215_GDF_Mb_6plicates.wiff (... Max. 3.5e5 cps.

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apoMb R3

apoMb R4

holoMb R1

holoMb R2

holoMb R5

holoMb R6

holoMb R3

holoMb R4

Inte

nsity

(cps

)In

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ps)

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)In

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Retention Time Retention Time

HoloMb or ApoMb were spiked into a yeast proteome extract. Each sample was prepared six times (six replicates) and subjected to the LiP workflow. Proteolytic patterns were analyzed directly by SRM. Raw chromatographic traces of the SRM analysis are reported to allow a visual evaluation of the reproducibility of LiP patterns and of their SRM measurement. R1-6 indicates the different replicates.

Supplementary Figure 3. Reproducibility of LiP patterns


a) LiP time-course of holo and apoMb monitored by SDS-PAGE. LiP was performed in the absence of the yeast background proteome and omitting the subsequent trypsin digestion step, to enable the direct SDS-PAGE analysis of large LiP cleavage products. The E:S ratio was 1:100 (w/w). Aliquots were taken from the reaction mixture and proteolysis was stopped by adding 0.1% aqueous trifluoroacetic acid to a pH < 3. Proteolysis was monitored by SDS-PAGE, using a Tricine buffer system and staining with Coomassie Brilliant Blue R-250. Standard ladder: Ultra Low Range Molecular Weight Marker, M.W.: 1,060-26,600 (Sigma). b) Effect of protease incubation time and E/S ratio, as determined by LiP-SRM. LiP-SRM was performed with either holo or apoMb spiked into a yeast background proteome. Abundance changes of the HT (GHHEAELKPL) and FT (GHHEAELKPLAQSHATK) peptides in the PK-treated samples normalized to the trypsin control samples are reported PK: proteinase K; For peptide GHHEAELKPL, which is undetectable in the trypsin control samples, only the most intense SRM transition was used for the calculation.

Incubation time GHHEAELKPLAQSHATK

GHHEAELKPL Apo Holo

Time (Min)

Enzyme to Substrate

ratio E/S

1/10 1/100 1/1000 1/10 1/100 1/1000

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1 5 30 60 1 5 30 60 Time (Min)

E/S

GHHEAELKPLAQSHATK

GHHEAELKPL

1/10 1/100 1/1000 1/10 1/100 1/1000

1 5 30 60 1 5 30 60

St.MW*

Holo Mb Apo Mb 1’ 5’ 0 60’ 30’ 1’ 0 60’ 30’

26.6 kDa

17 14.2

6.5

3.5 1.1

a

b

Supplementary Figure 4. Assessing different technical parameters of LiP based on the model protein myoglobin.


The secondary structure of horse holoMb is displayed. The A through H α-helices of the protein are indicated by boxes along the protein sequence. The amino acid sequence of helix F (residues 82 to 97) is displayed at the bottom of the figure and the sites of initial proteolytic cleavage by thermolysin (Th), subtilisin (Su), chymotrypsin (Ch) and proteinase K (PK) are indicated by arrows. LiP cleavage sites were identified by mass spectrometry analysis as described above (data not shown). LiP was conducted for 1 min with Th, or 10 min with chymotrypsin and subtilisin or 5 min with PK. The identity of the proteolytic fragments was established by off-line reverse-phase HPLC combined with mass spectrometry analysis (data not shown), as previously described (Picotti et al., 2004).

N-term C-termA B C D E G HF

G-H-H-E-A-E-L-K-P-L-A-Q-S-H-A-T-K-H-

PKChSu

SuPK SuTh

Supplementary Figure 5. Schematic representation of Mb LiP cleavage sites obtained with various proteases.


Antioxidant activity

Binding

Catalytic activity

Enzyme regulation

Ion channel

Receptor activity

Structural molecule

Transcription regulation

Translation regulation

Transporter

Apoptosis

Metabolic process

Cell adhesion

Cell cycle

Cell. component organization

Cellular process

Developmental process

Gener. of precursor metabolites and energy

Immune system molecule

Metabolic process

Response to stimulus

System process

Transport Metabolic process

Intracellular (cytosolic)

Plasma membrane

Protein complex

Ribonucleoprotein complex

Catalytic activity

Plots were obtained with the tool Panther (http://www.pantherdb.org/). (a) Gene ontology classification based on molecular function. (b) Gene ontology classification based on biological process. (c) Gene ontology classification based on subcellular localization.

a

b

c

Intracellular (cytosolic)

Supplementary Figure 6. Gene Ontology analysis of proteins changing proteolytic pattern in yeast grown in ethanol, relative to yeast grown in glucose.


GO term Description P-value FDR q-value Enrichment (N, B, n, b)

GO:0006091 generation of precursor metabolites and energy 4.95E-09 1.45E-05 2.39 (1413,82,281,39)

GO:0006096 glycolysis 3.21E-08 4.71E-05 3.83 (1413,21,281,16)

GO:0044710 single-organism metabolic process 4.13E-08 4.04E-05 1.42 (1413,472,281,133)

GO:0019752 carboxylic acid metabolic process 1.60E-07 1.17E-04 1.69 (1413,214,281,72)

GO:0043436 oxoacid metabolic process 1.99E-07 1.17E-04 1.68 (1413,215,281,72)

GO:0006082 organic acid metabolic process 1.99E-07 9.73E-05 1.68 (1413,215,281,72)

GO:0044281 small molecule metabolic process 2.87E-06 1.20E-03 1.42 (1413,371,281,105)

GO:0006094 gluconeogenesis 1.03E-05 3.78E-03 3.69 (1413,15,281,11)

GO:0006099 tricarboxylic acid cycle 1.63E-05 5.31E-03 3.06 (1413,23,281,14)

GO:0006007 glucose catabolic process 2.42E-05 7.10E-03 2.59 (1413,35,281,18)

GO:0019319 hexose biosynthetic process 2.72E-05 7.25E-03 3.46 (1413,16,281,11)

GO:0046364 monosaccharide biosynthetic process 2.72E-05 6.65E-03 3.46 (1413,16,281,11)

GO:0044237 cellular metabolic process 3.04E-05 6.85E-03 1.13 (1413,1040,281,233)

GO:1901605 alpha-amino acid metabolic process 3.43E-05 7.18E-03 1.78 (1413,116,281,41)

GO:0006006 glucose metabolic process 5.57E-05 1.09E-02 2.34 (1413,43,281,20)

GO:0008152 metabolic process 9.41E-05 1.73E-02 1.11 (1413,1096,281,241)

GO:0019320 hexose catabolic process 9.94E-05 1.71E-02 2.38 (1413,38,281,18)

GO:0043086 negative regulation of catalytic activity 1.46E-04 2.38E-02 3.48 (1413,13,281,9)

GO:0019318 hexose metabolic process 1.80E-04 2.78E-02 2.19 (1413,46,281,20)

GO:1901564 organonitrogen compound metabolic process 2.03E-04 2.98E-02 1.38 (1413,307,281,84)

GO:0006520 cellular amino acid metabolic process 2.21E-04 3.08E-02 1.55 (1413,165,281,51)

GO:0046365 monosaccharide catabolic process 3.32E-04 4.43E-02 2.21 (1413,41,281,18)

GO:0044092 negative regulation of molecular function 3.38E-04 4.31E-02 3.23 (1413,14,281,9)

GO:0044723 single-organism carbohydrate metabolic process 3.51E-04 4.28E-02 1.80 (1413,84,281,30)

GO:0044724 single-organism carbohydrate catabolic process 3.63E-04 4.26E-02 2.10 (1413,48,281,20)

GO:1901566 organonitrogen compound biosynthetic process 3.80E-04 4.28E-02 1.49 (1413,185,281,55)

GO:0055114 oxidation-reduction process 4.12E-04 4.47E-02 1.48 (1413,194,281,57)

GO:0016053 organic acid biosynthetic process 4.33E-04 4.53E-02 1.66 (1413,112,281,37)

GO:0046394 carboxylic acid biosynthetic process 4.33E-04 4.38E-02 1.66 (1413,112,281,37)

GO:0016052 carbohydrate catabolic process 5.04E-04 4.93E-02 2.05 (1413,49,281,20)

GO:0005996 monosaccharide metabolic process 5.04E-04 4.77E-02 2.05 (1413,49,281,20)

GO:0006549 isoleucine metabolic process 5.25E-04 4.81E-02 3.35 (1413,12,281,8)

GO:0015980 energy derivation by oxidation of organic compounds 5.85E-04 5.20E-02 2.08 (1413,46,281,19)

GO:0005975 carbohydrate metabolic process 7.76E-04 6.69E-02 1.61 (1413,119,281,38)

GO:0071704 organic substance metabolic process 8.08E-04 6.77E-02 1.11 (1413,1014,281,223)

GO:0044711 single-organism biosynthetic process 9.75E-04 7.95E-02 1.52 (1413,149,281,45)

Funct ional enr ichment was computed us ing the onl ine too l GOr i l la (http://cbl-gorilla.cs.technion.ac.il/), based on gene ontology terms. The complete set of proteins identified in the experiment was used as a background proteome (see also protein lists in Supplementary Table 11). (a) Graphical representation of the functional enrichment analysis. The output of the enrichment analysis is visualized as a hierarchical structure to represent the relations between enriched GO terms. (b) Functional enrichment of the different biological modules is expressed by a p-value and a FDR-adjusted p-value (q-value).

Supplementary Figure 7. Functional enrichement analysis of the set of proteins changing conformation in the glucose-to-ethanol transition


a b

c d

e

8ID

FBP

PGA

3PG2OP

TPP

ATP

Adh1

Gpm1Pdc1

F6P

FDP Pfk1

3PG

Pgk1 Tdh1-3

Cdc19

f

g

NAD+

MRY

HT peptides showing an abundance change of at least 2-fold are highlighted in green on their respective protein structures. Note the general proximity of the peptides to small molecule binding sites. Protein structures are shown in cartoon representation, while small molecules are depicted in multicolour sphere representation. (a) The ligand 8ID in the structure of Adh1 (PDB file 2HCY-A) is an analog of the substrate NAD+. (b) The structure of Cdc19 (PDB file 1A3W-A) shows the allosteric effector fructose-1,6-biphosphate (FBP) and the analog PGA of its product phosphoenolpyruvate. (c) The structure of Pdc1 (PDB file 2VK8-A) displays the co-factor thiamine diphosphate (TPP) and the substrate analog 2OP, which replaces 2-oxo acid substrates. (d) Gpm1 (PDB file 1QHF-A) is depicted with its product 3-phospho-glycerate (3PG). (e) The structure of Pgk1 (PDB file 3PGK-A) shows the location of both substrate molecules ATP and 3-phospho-glycerate (3PG). (f) The homology model of Tdh1 (based on PDB file 1HDG-O) shows the location of its co-factor NAD+ and a substrate analog (MRY), which replaces the substrate D-glyceraldehyde-3-phosphate. (g) The homology model of Pfk1 (based on PDB file 3OPY-E) shows the location of the substrate fructose-6-phosphate (F6P) and the allosteric effector fructose-2,6-diphosphate (FDP) that were extracted from the PDB entry 3O8O. The oligomeric state of the enzymes was not considered in the representation, in order to better highlight the location of the peptides.

Supplementary Figure 8. Conformational changes detected in the discovery phase of our LiP workflow.


A set of enzymes that change protelytic pattern in the glucose-ethanol transition is shown. Yeast strains with individual GFP-tagged metabolic enzymes were from the yeast-GFP clone collection (Invitrogen, Carlsbad, CA, USA) and cultured in glucose (Glu) or ethanol (Eth) as described in the main text for the metabolic shift experiment. The images were acquired on an upright microscope with excitation and emission filters set for measurement of GFP fluorescence. Image processing was performed with ImageJ (Wayne Rasband, National Institutes of Health, Bethesda, MD).

Supplementary Figure 9. Fluorescence microscopy images of yeast strains expressing green fluorescent protein (GFP)-tagged metabolic enzymes.

Cdc19

Gpm1

Pdc1

Glu Eth

Tdh1

Pgk1

Pfk1

Glu Eth


y = 2E-05x + 0.0641R² = 0.9995

y = 6E-05x + 0.0684R² = 0.9993

0.05

0.1

0.15

0.2

0.25

0 500 1000 1500 2000 2500 3000

EthEth + FBP

Time (s)

Abs

orba

nce

Yeast cells were cultured in the ethanol-based medium, sampled and lysed as described in the Methods. The activity test of yeast pyruvate kinase was carried out using the Pyruvate Kinase Activity Assay Kit (Sigma). Pyruvate kinase catalyzes the transfer of a phosphate group from phospho(enol)pyruvate (PEP) to ADP, yielding one molecule of pyruvate and one molecule of ATP. Pyruvate concentration was determined by a coupled enzyme assay, which results in a colorimetric (570 nm)/fluorometric product, proportional to the amount of pyruvate in the mixture.

Supplementary Figure 10. Enzyme activity assay for yeast pyruvate kinase in the yeast extract in the presence and absence of FBP.


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