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Quantitative fragmentomics allow affinity mapping of interactomes

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Gogl, Gergo, Zambo, Boglarka, Kostmann, Camille, Cousido-Siah, Alexandra, Morlet, Bastien, Durbesson, Fabien, Negroni, Luc, Eberling, Pascal, Jané, Pau, Nominé, Yves, Zeke, Andras, Østergaard, Søren, Monsellier, Élodie, Vincentelli, Renaud, Travé, Gilles·2022·Nature Communications
DOI 10.1038/s41467-022-33018-0PMC PMC9482650

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Abstract

Human protein networks have been widely explored but most binding affinities remain unknown, hindering quantitative interactome-function studies. Yet interactomes rely on minimal interacting fragments displaying quantifiable affinities. Here, we measure the affinities of 65,000 interactions involving PDZ domains and their target PDZ-binding motifs (PBM) within a human interactome region particularly relevant for viral infection and cancer. We calculate interactomic distances, identify hot spots for viral interference, generate binding profiles and specificity logos, and explain selected cases by crystallographic studies. Mass spectrometry experiments on cell extracts and literature surveys show that quantitative fragmentomics effectively complements protein interactomics by providing affinities and completeness of coverage, putting a full human interactome affinity survey within reach. Finally, we show that interactome hijacking by the viral PBM of human papillomavirus E6 oncoprotein substantially impacts the host cell proteome beyond immediate E6 binders, illustrating the complex system-wide relationship between interactome and function.

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Fig. 1 Affinity profiling of a subsection of the PDZ-PBM interactome: principle and data overview. The human PDZ-PBM interactome represents ~106 potential interactions (left panel). Focusing on a viral- and cancer-relevant region (upper part of left panel, zoomed in middle panel), we measured the 59,578 affinities of 133 human PDZs for 424 human and 24 viral PBMs (fully measured subsection, left half of middle panel) and 5,573 extra affinities involving the remaining 133 PDZs and a subset of 45 PBMs (right half of middle panel). The figure does not indicate the scale of all potential viral hijacking interactions. Measured or non-measured PDZ-PBM pairs are respectively colored in heat map mode (see color scale in lower right panel) or in gray. Any horizontal or vertical cross-section of that interactome represents an individual PDZome- or PBMome- binding profile, as illustrated for NET1 PBM and SCRIB_1 PDZ (top right corner). The horizontal cross-section of the middle panel is indicated by a horizontal arrow in the corner of the PDZome binding profile and the vertical cross-section of the middle panel is indicated by a vertical arrow in the corner of the PBMome binding profile. Affinity-based Euclidian distances computed from the fully measured subsection reveal a clade of human PBMs displaying the highest interactomic similarities with HTLV1 Tax1 and HPV E6 oncoviral PBMs (middle right panel). Note the general similarity of the heatmap patterns of the oncoviral PBMs and of the identified human PBM clade, and the fine pattern differences further revealed by sub-clustering using the UGPMA approach. For more details, see Supplementary Data 1.
Fig. 1 Affinity profiling of a subsection of the PDZ-PBM interactome: principle and data overview. The human PDZ-PBM interactome represents ~106 potential interactions (left panel). Focusing on a viral- and cancer-relevant region (upper part of left panel, zoomed in middle panel), we measured the 59,578 affinities of 133 human PDZs for 424 human and 24 viral PBMs (fully measured subsection, left half of middle panel) and 5,573 extra affinities involving the remaining 133 PDZs and a subset of 45 PBMs (right half of middle panel). The figure does not indicate the scale of all potential viral hijacking interactions. Measured or non-measured PDZ-PBM pairs are respectively colored in heat map mode (see color scale in lower right panel) or in gray. Any horizontal or vertical cross-section of that interactome represents an individual PDZome- or PBMome- binding profile, as illustrated for NET1 PBM and SCRIB_1 PDZ (top right corner). The horizontal cross-section of the middle panel is indicated by a horizontal arrow in the corner of the PDZome binding profile and the vertical cross-section of the middle panel is indicated by a vertical arrow in the corner of the PBMome binding profile. Affinity-based Euclidian distances computed from the fully measured subsection reveal a clade of human PBMs displaying the highest interactomic similarities with HTLV1 Tax1 and HPV E6 oncoviral PBMs (middle right panel). Note the general similarity of the heatmap patterns of the oncoviral PBMs and of the identified human PBM clade, and the fine pattern differences further revealed by sub-clustering using the UGPMA approach. For more details, see Supplementary Data 1.CC BY · PubMed Central
Fig. 2 Validation and distribution of the quantified PDZ-PBM affinities. A Comparison of PDZ-PBM dissociation constants quantified with holdup assay (HU) or competitive fluorescence polarization (FP). The Pearson correlation coefficient (PCC) was determined based on affinities quantifiable by both methods (n = 255). B Comparison of PDZ-PBM dissociation constants quantified with holdup assay or obtained from literature. PCC was determined based on interactions quantified by both methods (n = 362). C PDZ-PBM dissociation constants quantified from HU follow an exponential-like distribution. The gray zone indicates the range of pKd quantification thresholds that slightly varies between different assays. The red line indicates an exponential probability distribution function, predicted from the data. D PDZ-PBM dissociation constants obtained from literature follow a normal distribution. The red line indicates the predicted normal distribution. Note the different affinity range in C and D. Source data are provided as a Source Data file. For more details, see Supplementary Data 1.
Fig. 2 Validation and distribution of the quantified PDZ-PBM affinities. A Comparison of PDZ-PBM dissociation constants quantified with holdup assay (HU) or competitive fluorescence polarization (FP). The Pearson correlation coefficient (PCC) was determined based on affinities quantifiable by both methods (n = 255). B Comparison of PDZ-PBM dissociation constants quantified with holdup assay or obtained from literature. PCC was determined based on interactions quantified by both methods (n = 362). C PDZ-PBM dissociation constants quantified from HU follow an exponential-like distribution. The gray zone indicates the range of pKd quantification thresholds that slightly varies between different assays. The red line indicates an exponential probability distribution function, predicted from the data. D PDZ-PBM dissociation constants obtained from literature follow a normal distribution. The red line indicates the predicted normal distribution. Note the different affinity range in C and D. Source data are provided as a Source Data file. For more details, see Supplementary Data 1.CC BY · PubMed Central
Fig. 3 Molecular determinants behind binding specificities in the PDZ-PBM interactome. A PDZome-binding profile of HPV35 E6 PBM. B The affinity-weighted specificity logo of SNTB1 PDZ matches well with HPV35 E6 PBM at positions p0, p-2, p-3 and p-4, which display favorable contacts in the crystal structure of the SNTB1/HPV35 E6 complex. C MAGI1-2 PDZ logo matches with HPV35 E6 PBM at the same positions, but also at p-5 (Arg), exposed to the acidic β2-β3 loop of MAGI1_2 in the MAGI1/HPV35 E6 complex. Accordingly, HPV35 E6 binds stronger to MAGI1-2 than to SNTB1. D PBMome-binding profile of SYNJ2BP PDZ. E RPS6KA1 PBM matches poorly to the logo of SYNJ2BP. Accordingly, RPS6KA1 binds only weakly to SYNJ2BP. The p-3 phosphorylated RPS6KA1 PBM matches better to SYNJ2BP logo, with acidic phospho-Ser at p-3 contacting two residues of SYNJ2BP β2 and β3 strands in the complex. Accordingly, p-3 phosphorylated RPS6KA1 shows increased binding to SYNJ2BP. F HTLV Tax1 PBM sequence matches strongly with SYNJ2BP logo at p0, p-1, p-2, p-3, and p-4. Accordingly, Tax1 is the strongest SYNJ2BP binder in our interactome. In the SYNJ2BP-Tax1 complex, Glu at p-3 of Tax1 engages similar contacts as pSer of RPS6KA1_−3P, while Arg at p-4 provides additional contact to the α2 helix of SYNJ2BP. G Superposition of Tax1 and p-3 phosphorylated RPS6KA1 PBMs bound to SYNJ2BP. The carboxylate-binding loop of SYNJ2BP is shifted in the phosphorylated RPS6KA1-bound complex, most likely related to unfavorable Val → Leu substitution at p0 of RPS6KA1 PBM. H The logo of SNX27 indicates preference for class 1 PBMs (Ser/Thr residue at p-2). MERS E has a class 3 PBM (Glu at p-2) with poor match to SNX27 logo; yet MERS E binds relatively well to SNX27. In the SNX27/MERS E complex Trp at p-1 of MERS E establishes favorable hydrophobic contacts. I Interactomic distance profile of MERS E in the explored PBM space. The closest neighbor of MERS E is ARVCF, a class 1 PBM with Trp at p-1. Source data are provided as a Source Data file. For further details see Supplementary Figs. 2 and 3, Supplementary Table 1 and Supplementary Data 1. All logos were taken from the ProfAff server.
Fig. 3 Molecular determinants behind binding specificities in the PDZ-PBM interactome. A PDZome-binding profile of HPV35 E6 PBM. B The affinity-weighted specificity logo of SNTB1 PDZ matches well with HPV35 E6 PBM at positions p0, p-2, p-3 and p-4, which display favorable contacts in the crystal structure of the SNTB1/HPV35 E6 complex. C MAGI1-2 PDZ logo matches with HPV35 E6 PBM at the same positions, but also at p-5 (Arg), exposed to the acidic β2-β3 loop of MAGI1_2 in the MAGI1/HPV35 E6 complex. Accordingly, HPV35 E6 binds stronger to MAGI1-2 than to SNTB1. D PBMome-binding profile of SYNJ2BP PDZ. E RPS6KA1 PBM matches poorly to the logo of SYNJ2BP. Accordingly, RPS6KA1 binds only weakly to SYNJ2BP. The p-3 phosphorylated RPS6KA1 PBM matches better to SYNJ2BP logo, with acidic phospho-Ser at p-3 contacting two residues of SYNJ2BP β2 and β3 strands in the complex. Accordingly, p-3 phosphorylated RPS6KA1 shows increased binding to SYNJ2BP. F HTLV Tax1 PBM sequence matches strongly with SYNJ2BP logo at p0, p-1, p-2, p-3, and p-4. Accordingly, Tax1 is the strongest SYNJ2BP binder in our interactome. In the SYNJ2BP-Tax1 complex, Glu at p-3 of Tax1 engages similar contacts as pSer of RPS6KA1_−3P, while Arg at p-4 provides additional contact to the α2 helix of SYNJ2BP. G Superposition of Tax1 and p-3 phosphorylated RPS6KA1 PBMs bound to SYNJ2BP. The carboxylate-binding loop of SYNJ2BP is shifted in the phosphorylated RPS6KA1-bound complex, most likely related to unfavorable Val → Leu substitution at p0 of RPS6KA1 PBM. H The logo of SNX27 indicates preference for class 1 PBMs (Ser/Thr residue at p-2). MERS E has a class 3 PBM (Glu at p-2) with poor match to SNX27 logo; yet MERS E binds relatively well to SNX27. In the SNX27/MERS E complex Trp at p-1 of MERS E establishes favorable hydrophobic contacts. I Interactomic distance profile of MERS E in the explored PBM space. The closest neighbor of MERS E is ARVCF, a class 1 PBM with Trp at p-1. Source data are provided as a Source Data file. For further details see Supplementary Figs. 2 and 3, Supplementary Table 1 and Supplementary Data 1. All logos were taken from the ProfAff server.CC BY · PubMed Central
Fig. 4 Exploring interaction networks prone to perturbation by oncoviral PBMs in host cell models by AP-MS. A Identification, from extracts of indicated cell lines, of PDZ-containing prey proteins retained by HPV E6 and HTLV1 Tax1 PBM baits. Results of comparable studies by Strickland et al.39 (*) Thomas et al.38 (**) and Al-Saleem et al.40 (***) are also provided. For each PDZ-protein identified, the number of PDZ domains and the presence or absence of PDZ-oligomerization L27 domains are indicated at the bottom of the table. The four prey proteins containing the 6 PDZ domains that we selected as baits for the next round of AP-MS experiments are highlighted in gray. B Identification, from indicated cell extracts, of PBM-containing prey proteins retained by the 6 PDZ domains selected in A. The ten top prey proteins of the list belong to the oncoviral-like human PBM clade revealed by our quantitative fragmental interactome. Interaction partners in A and B were found by comparing the prey quantities compared to a control resin. Binding threshold was defined at >2-fold enrichment and <0.01 P value, calculated by two-tailed unpaired T-test. Blue cells indicate detectable interaction between the immobilized bait (minimal binding fragment) and the endogenous prey (full-length protein). Note that only interaction partners containing PDZ domains or C-terminal PBMs are indicated. For a comprehensive list of all identified interaction partners, see Supplementary Data 2.
Fig. 4 Exploring interaction networks prone to perturbation by oncoviral PBMs in host cell models by AP-MS. A Identification, from extracts of indicated cell lines, of PDZ-containing prey proteins retained by HPV E6 and HTLV1 Tax1 PBM baits. Results of comparable studies by Strickland et al.39 (*) Thomas et al.38 (**) and Al-Saleem et al.40 (***) are also provided. For each PDZ-protein identified, the number of PDZ domains and the presence or absence of PDZ-oligomerization L27 domains are indicated at the bottom of the table. The four prey proteins containing the 6 PDZ domains that we selected as baits for the next round of AP-MS experiments are highlighted in gray. B Identification, from indicated cell extracts, of PBM-containing prey proteins retained by the 6 PDZ domains selected in A. The ten top prey proteins of the list belong to the oncoviral-like human PBM clade revealed by our quantitative fragmental interactome. Interaction partners in A and B were found by comparing the prey quantities compared to a control resin. Binding threshold was defined at >2-fold enrichment and <0.01 P value, calculated by two-tailed unpaired T-test. Blue cells indicate detectable interaction between the immobilized bait (minimal binding fragment) and the endogenous prey (full-length protein). Note that only interaction partners containing PDZ domains or C-terminal PBMs are indicated. For a comprehensive list of all identified interaction partners, see Supplementary Data 2.CC BY · PubMed Central
Fig. 5 Amounts of complex formation between endogenous preys and bait fragments in AP-MS correlate with their corresponding affinities. A Correlations between enrichment values and affinities measured using PBM baits (upper panels) or PDZ baits (lower panels). For preys containing several PDZ domains, we assumed additivity of association constants (see Methods). On the left side, example correlations are shown for one PDZ (SYNJ2BP) and one PBM (ATP2B4) prey protein. On the right side, only the Pearson correlation coefficient (PCC) is shown for measured preys. Note that correlations are overall weaker than in the case of PDZ preys (upper panels), with an average PCC of 0.5 instead of 0.8. B Correlations between enrichment values obtained from different cell lysates measured using PBM baits (upper panels) or PDZ baits (lower panels). On the left side, example correlations are shown for one PDZ (SYNJ2BP) and one PBM (ATP2B4) prey protein. On the right side, only the Pearson correlation coefficient (PCC) is shown for measured preys. Note that correlations are overall weaker than in the case of PDZ preys (upper panels). Source data are provided as a Source Data file. See Supplementary Data 2 for more details.
Fig. 5 Amounts of complex formation between endogenous preys and bait fragments in AP-MS correlate with their corresponding affinities. A Correlations between enrichment values and affinities measured using PBM baits (upper panels) or PDZ baits (lower panels). For preys containing several PDZ domains, we assumed additivity of association constants (see Methods). On the left side, example correlations are shown for one PDZ (SYNJ2BP) and one PBM (ATP2B4) prey protein. On the right side, only the Pearson correlation coefficient (PCC) is shown for measured preys. Note that correlations are overall weaker than in the case of PDZ preys (upper panels), with an average PCC of 0.5 instead of 0.8. B Correlations between enrichment values obtained from different cell lysates measured using PBM baits (upper panels) or PDZ baits (lower panels). On the left side, example correlations are shown for one PDZ (SYNJ2BP) and one PBM (ATP2B4) prey protein. On the right side, only the Pearson correlation coefficient (PCC) is shown for measured preys. Note that correlations are overall weaker than in the case of PDZ preys (upper panels). Source data are provided as a Source Data file. See Supplementary Data 2 for more details.CC BY · PubMed Central
Fig. 6 Proteomic impact of stable HPV16 E6 expression in HEK293T cells and the contribution of its PBM. A Proteomic changes measured with label free mass spectometry (n = 3) in HEK293T cells stably expressing HPV16 E6 or HPV16 E6ΔPBM compared to a control cell line “IRES”. Dots indicate the detected 2273 proteins. Black dots indicate proteins whose abundances changed significantly in both E6- and E6ΔPBM-expressing cells as compared to control cells. The dotted line indicates the diagonal. B Proteomic difference between the cell lines expressing E6, or E6ΔPBM. The dotted line indicates the statistical significance threshold (P < 0.05, two-tailed unpaired T-test) and dark dots indicate proteins whose statistical difference is higher than this threshold. C Proteins that show statistically significant differences in concentration between E6- and E6ΔPBM-expressing cell lines often change in opposite direction compared to the control cell line. D Relative amounts of 11 PDZ proteins and p53 in the three cell lines measured by the proteomic survey. Relative XIC intensities are shown compared to the intensities of the control cell line. E Relative amounts of 5 PDZ-proteins, as well as p53 and E6AP, quantified by western blot. GAPDH normalized abundancies were compared to the intensities of the control cell line. In D and E, mean and STD were calculated from three individual measurements indicated by black dots, and statistical significance is indicated by * for P < 0.05 and ** for P < 0.01. Two-tailed unpaired t-tests were used to determine statistical significance. Exact p values are provided in the source data file together with the other source data. See Supplementary Fig 4 for results of western blots and Supplementary Data 3 for full proteomic results.
Fig. 6 Proteomic impact of stable HPV16 E6 expression in HEK293T cells and the contribution of its PBM. A Proteomic changes measured with label free mass spectometry (n = 3) in HEK293T cells stably expressing HPV16 E6 or HPV16 E6ΔPBM compared to a control cell line “IRES”. Dots indicate the detected 2273 proteins. Black dots indicate proteins whose abundances changed significantly in both E6- and E6ΔPBM-expressing cells as compared to control cells. The dotted line indicates the diagonal. B Proteomic difference between the cell lines expressing E6, or E6ΔPBM. The dotted line indicates the statistical significance threshold (P < 0.05, two-tailed unpaired T-test) and dark dots indicate proteins whose statistical difference is higher than this threshold. C Proteins that show statistically significant differences in concentration between E6- and E6ΔPBM-expressing cell lines often change in opposite direction compared to the control cell line. D Relative amounts of 11 PDZ proteins and p53 in the three cell lines measured by the proteomic survey. Relative XIC intensities are shown compared to the intensities of the control cell line. E Relative amounts of 5 PDZ-proteins, as well as p53 and E6AP, quantified by western blot. GAPDH normalized abundancies were compared to the intensities of the control cell line. In D and E, mean and STD were calculated from three individual measurements indicated by black dots, and statistical significance is indicated by * for P < 0.05 and ** for P < 0.01. Two-tailed unpaired t-tests were used to determine statistical significance. Exact p values are provided in the source data file together with the other source data. See Supplementary Fig 4 for results of western blots and Supplementary Data 3 for full proteomic results.CC BY · PubMed Central

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Metadata

Authors
Gogl, G., Zambo, B., Kostmann, C., Cousido-Siah, A., Morlet, B., Durbesson, F., Negroni, L., Eberling, P., Jané, P., Nominé, Y., Zeke, A., Østergaard, S., Monsellier, É., Vincentelli, R., & Travé, G.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Novo Nordisk (Denmark) (DK)
Corresponding author
Travé, Gilles
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-09-17
Volume
13
Issue
1
Pages
5472-5472
ISSN
2041-1723 (ISSN-L)
DOI
10.1038/s41467-022-33018-0
PMC
PMC9482650
OpenAlex
W4296139765
Cited by
51
References
3
Citations by year
2026: 15 · 2025: 8 · 2024: 13 · 2023: 11 · 2022: 4
FWCI
2.94
Type
journal article
Language
en
Update notices
correction (2022-12-07)
Open access
Licence
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DORA PSI (Paul Scherrer Institute) (submittedVersion, other-oa)
  • HAL (submittedVersion)
  • HAL (submittedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Agence Nationale de la Recherche (ANR-10-INSB-05-01., 10-INSB-05-01, 10 IDEX 0002, ANR-10-IDEX-0002, ANR- 10-INSB-05, ANR-17-EURE-, ANR-20-SFRI-0012, ANR-18-CE92-0017, ANR-17-EURE-0023, ANR18, ANR-10-IDEX, ANR-18-EURE-0023, ANR-17, ANR-10, ANR-10-INBS-0005)
  • Institut National de la Santé et de la Recherche Médicale (ANR 10 IDEX 0002, ANR-17-EURE-0023, ANR-20-SFRI-0012)
  • Fondation pour la Recherche Médicale (SPF202005011975, ANR-17-EURE-0023, ANR-10-IDEX-0002, ANR 20-SFRI-0012)
  • Université de Strasbourg (ANR 20-SFRI-0012, 10-IDEX-0002, ANR-10-IDEX- 0002, ANR-17-EURE-0023, ITI 2021 2028)
  • Fondation ARC pour la Recherche sur le Cancer (ANR-10-IDEX-0002, ANR 20-SFRI-0012, ANR-17-EURE-0023)
  • Ligue Contre le Cancer (ANR-10-IDEX-0002, ANR-17-EURE-0023, ANR 20-SFRI-0012, Équipe labellisée 2015)
  • Centre National de la Recherche Scientifique (ANR-10-IDEX-0002, ANR-17-EURE-0023, 2021-2028, ANR 20 SFRI 0012)
  • French Infrastructure for Integrated Structural Biology (ANR-10- INSB-05, ANR-10-INSB-0501, INSB-05)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Biotin and Related Studies
Keywords
protein-protein interaction, binding affinity, PDZ domain, PDZ-binding motif, human interactome, interactome mapping, viral interference, E6 oncoprotein, host-virus interaction, viral infection, cancer
MeSH terms
Cell Extracts, Humans, Mass Spectrometry, Proteome, Papillomaviridae, PDZ Domains

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CoChat. (2026). Citation check: Quantitative fragmentomics allow affinity mapping of interactomes. https://app.cochat.ai/papers/10.1038/s41467-022-33018-0
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Gogl, G., Zambo, B., Kostmann, C., Cousido-Siah, A., Morlet, B., Durbesson, F., Negroni, L., Eberling, P., Jané, P., Nominé, Y., Zeke, A., Østergaard, S., Monsellier, É., Vincentelli, R., & Travé, G. (2022). Quantitative fragmentomics allow affinity mapping of interactomes. Nature Communications. https://doi.org/10.1038/s41467-022-33018-0