dataesr/scientific-paragraphs-categorization huggingface dataset:
Fabienne Lohou contributed in writing the paper, planned the experiment, organised and coordinated the Savè site, prepared the experiment including the development of measurement strategy, participated in the measurement campaign and was responsible for the organisation of post-processing. Yannick Bezombes was responsible engineer for the UHF wind profiler, participated to the settlement and monitoring of the UHF wind profiler. Guillaume Bret participated to the Savè site settlement and electricity installation at site. Claire Delon participated to the campaign and was responsible for chemistry measurement. Solene Derrien prepared the experiment and was responsible for the settlement and unsettlement of UPS devices. Cheikh Dione participated to the campaign and performed data quality control, data postprocessing, generation of metadata, and submission of data to the database.
acknowledgements The  DACCIWA  project has received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 603502. We also want to thank staff from  NCAS (National Centre for Atmospheric Science) ,  KIT (Karlsruhe Institute of Technology)  and  UPS  ( Université Paul Sabatier Toulouse ) for helping to install the equipment as well as the people from  KNUST (Kwame Nkrumah University of Science and Technology) in Kumasi, INRAB (Institut National des Recherches Agricoles du Bénin ) in Savè for allowing the equipment on their ground and  NCAS  for providing the instrumentation deployed at Kumasi. We thank the  AERIS data infrastructure  for providing access to the data used in this study.
Box 1: Synthesis of empirical data To determine how common it is for community metrics to observe opposite stressor interactions, we analysed a database of multiple-stressor experiments in freshwater systems collected for meta-analyses by  Jackson et al. (2016)  and  Morris et al. (2022). 59 of the 125 studies in the database quantified stressor interactions at the community level. There were 16 cases where stressor interactions were reported for both biomass and diversity (Table  S1 ). There were also 12 cases where stressor interactions were reported for both biomass and decomposition (Table  S2 ). Interactive effects between stressors were quantified using Hedges' d  (Hedges, 1981). Annotated code for analysis of the empirical data is available in an R notebook at https://github. com/jamesaorr/observation-stressor-interactions.
Annotated code for our simulations is available in a Jupyter Notebook at https://github. com/jamesaorr/observation-stressor-interactions. Box 2: Geometrical theory of stressor interactions at the community-level Here bold symbols denote S-dimensional vectors, where S is the richness of the community. Let N 0 be the initial state of a community: the vector of species biomass prior to stressor exposure. Let N 1 and N 2 be the community states after exposure to stressor 1 and 2, respectively. Let N 12 be the state following exposure to both stressors. The observed-non additive response, quantified via an ecosystem function f (N ), is ∆f = f (N 1 ) + f (N 2 ) -f (N 12 ) -f (N 0 ). ( 4 ) The interaction type -antagonistic or synergistic-is determined by the sign of ∆f. If positive, more change occurred than expected, thus a synergistic interaction.
Acknowledgements JAO and MCJ were supported by a  Natural Environment Research Council  grant ( NE/V001396/1 ). JAO was supported by an  Irish Research Council   Laureate Award  ( IRCLA/2017/112 ) and  TCD Provost 's PhD Award held by JJP. JFA and ALJ were supported by an  Irish Research Council   Laureate Award  ( IRCLA/2017/186 ). JFA was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ).
The data, assembled into an open-access database (https://profaff. igbmc. science), define a quantitative interactomic space where proteins are located and clustered according to their binding preferences. Using these data, PDZ-PBM specificities can be depicted by binding profiles and specificity logos, that we interpret by crystallographic studies on selected instances. We also show that amounts of prey proteins captured in cell lysates by exogenous PDZ or PBM baits correlate with the corresponding PDZ-PBM affinities. Finally, we investigated how the interactomic properties of the viral PBM of HPV E6 oncoprotein impact the host cell proteome.
1 (K d = 800 μM) and M). In total, we performed 79,374 3 single-point holdup experiments on 65,151 interactions, covering ~55% of the interactomic space defined by the 266 human PDZ-domains and the 448 human and viral PBMs (Figure  1 ). We quantified 18,332 unique dissociation constants. The remaining 46,825 PDZ-PBM affinities were below quantification threshold, thereby defining the "negatome" of the explored interactomic space. Negatome data  (Blohm et al. , 2014)  are often missing in interactomic studies, hindering both the benchmarking and the modeling of interaction networks (Alvarez-Ponce, 2017)  (Zhang et al. , 2018)   (Mei & Zhang, 2019). The complete affinity data (Table  S1 ) are freely accessible on our online database ProfAff (for "Profiling Affinities") (https://profaff. igbmc.
science), allowing user-friendly visualisation and analyses. We complemented our measurements with 395 detailed competitive fluorescence polarization experiments (FP). The quantified affinities show an excellent agreement with those measured by holdup assay (Figure  2A ). We also assembled, expanding upon a recent review  (Amacher et al. , 2020) , a literature compendium of affinities previously measured for mammalian PDZ-PBM pairs (Table  S1 ). Considering unavoidable experimental discrepancies (methods, exact lengths of constructs and peptides, species of origin. ) our dataset remarkably agrees with this compendium (Figure  2B ). Empirically, we have found that the quantified PDZ-PBM affinities follow a nearly perfect exponential-like distribution (Figure  2C ). A handful (less than 0. 5%) show pK d >6, only 8% have a pK d >5, and the majority has a 3< pK d <4.
We also curated and assembled a second literature benchmark based on qualitative interactions devoid of affinity data, involving full-length PDZ-and PBM-containing proteins or their fragments (Table  S1  and Figure  S1 ). Of that qualitative benchmark, 1248 interactions found a match with at least one PDZ-PBM pair in our measured interactomic space, and 725 (58%) could be assigned to at least one quantified PDZ-PBM affinity. We also found that only 0. 5%, 2. 1% and 3. 4% out of the 14,839 protein pairs for which we quantified PDZ-PBM affinity constants corresponded to protein pairs recorded by the proteomewide interactomic study BioPlex  (Huttlin et al. , 2021)  or in the interaction databases IntAct  (Kerrien et al. , 2012)  and Biogrid  (Oughtred et al. , 2021) , respectively. Our exhaustive affinity survey of a defined interactomic space thus indicates that the current coverage of the human interactome remains very sparse.
Topology of the human PDZ-PBM interactomic space pK d values can be considered as coordinates of multidimensional interactomic spaces. Our dataset almost exhaustively covers two such spaces, one describing PDZ-binding by PBMs and the other describing PBM-binding by PDZs, respectively comprising 133 and 448 affinity dimensions. The more two PBMs or two PDZs bind to the same targets with similar affinities, the closer they stand in these spaces. Euclidian distances between PDZs or between PBMs can be calculated from their differences in affinities (ΔppK d = ΔpΔpG/2. 303RT, see Methods). These distances allow identifying closest interactomic neighbours (Figure  S2A ). For example, the PDZbinding profiles of PBMs of the RhoA guanine nucleotide exchange factor NET1 and of the Ras effector protein RASSF6 are extremely similar, and this is captured by a very short Euclidian distance.
Discussion Here we systematically measured 65,000 affinities in a focused region of the human interactome, which was only sparsely covered by pre-existing full-interactome databases. The human proteome is estimated to contain 35x10 3 domain instances dispersed across 10 4 distinct families and about one million motifs, including post-translational variants  (Tompa et al. , 2014). The full fragmental interactome, once subdivided in its multiple domain-motif families, may thus represent hundreds of millions of potential interactions. This exceeds by 10 2 to 10 3 -fold the numbers of interactions documented in Biogrid (~500,000), HuRI (~56,000), BioPlex (~120,000) and PCP-SILAM (~125,000)  (Oughtred et al. , 2021)   (Skinnider et al. , 2021)   (Luck et al. , 2020 )  (Huttlin et al. , 2021). Thus, the human interactome still strongly lacks coverage due to sparse 10 sampling.
For further details see Figure  S2  and Table  S1 ,2. All logos were taken from the ProfAff server. S4  for full proteomic results. (A) Number of PDZ-PBM interactions detected by our screen, that would have been retained by other methods with different sensitivity thresholds. In our dataset, we have used, by default, the lowest possible pK d quantification thresholds (situated in the grey zone on the plot) derived from the binding detection thresholds of the source holdup data. This way, we quantified the dissociation constants of 18,332 out of 65,157 measured interactions. The curve describes the number of these interactions that would have been retained using less sensitive methods, as a function of the minimal detection threshold pK d min of those methods. For instance, a method with pK d min = 5 would only detect the ~1500 interactions displaying a K d < 10 μM) and M.
(B) Coverage by our data of previously known protein-protein interactions, as a function of sensitivity threshold. For the interactomic region explored by our dataset, we plotted the percentage of qualitative protein-protein interactions (PPI) documented in large scale interactomic resources (n=1,248) which found a match to PDZ-PBM pK d values quantified in our measurements, in the function of a variable minimal detection threshold pK d min , defined as before. For proteins with multiple PDZ domains, we assumed an "additive" effect between the distinct binding sites (see Methods). If we account for all the interactions detected and quantified in our assay, our data confirm nearly 60% of the interactions (direct or indirect) reported in large scale resources. If we consider that our measurements could have been performed using less sensitive methods, this percentage further decreases, as a function of the pK d min assumed for these methods.
(C) Affinity distribution histogram of all the protein-protein interactions found in large scale interactomic resources, for which we could match PDZ-PBM pK d values quantified in our measurements. The affinities used for the distribution are taken from our dataset. For interactions involving PDZ-proteins with multiple PDZ-domains, "additive" affinities were considered. Histograms were calculated using all available affinities using a bin size of 0. 1 pK d. (D) Affinity distribution histogram of all the potential human protein-protein interactions that may exist according to our domain-motif data. The affinities used for the distribution are obtained from our PDZ-PBM affinity dataset. For interactions involving PDZ-proteins with multiple PDZ-domains, "additive" affinities were considered. (A) Analysis of some PDZ protein expressions in these cells by Western-blot.
Holdup experiments were carried out after 3 washing steps (10 resin volume, holdup buffer). 5 μM) and l bacterial lysate with 4 μM) and M His 6 -MBP-PDZ supplemented 31 with 4 μM) and M BSA and 0. 05 mg/ml lysozyme was incubated on the filter plate for 15 min with shaking before filtration by centrifugation. Filtrates were analyzed with Caliper Labchip GXII (Perkin Elmer, Waltham, Massachusetts), and collected data was analyzed with in-house developed algorithms  (Jané et al. , 2020). In total, our database includes 4,475 unique interactions characterized by CALIP method, originating from 9,920 measurements. We have found that the standard deviation of duplicate experiments (n = 1,893) was σ = 0. 05 BI. We set our binding detection threshold based on this and defined it at 2 σ (BI = 0. 1).
Based on this threshold, 807 unique interactions showed detectable binding in CALIP holdup experiments. SAPF experiments were carried out in the original layout. Resin preparation was identical as in LYSC holdup experiments. In each well 2. 5 μM) and l peptide-or biotin-saturated resin was incubated with 5 μM) and l single affinity-purified His 6 -MBP-PDZ at a concentration of 4 μM) and M. His 6 -MBP-PDZ was supplemented with 50 nM fluorescein and 100 nM mCherry. Incubation was carried out on filter plates for 15 min with shaking before filtration. Filtrates were analyzed on a PHERAstar (BMG Labtech, Offenburg, Germany) microplate reader by using 485 ± 10 nm -528 ± 10 nm (fluorescein), 575 ± 10 nm -620 ± 10 nm (mCherry), and 295 ± 10 nm -350 ± 10 nm (Trpfluorescence) band-pass filters. In total, our database includes 6,850 unique interactions characterized by SAPF method, all measured in singlicate.
Fluorescence intensity measurements were performed as described in SAPF experiments. The reverse layout made it possible to include several peptides on the filter plate that was measured previously with Caliper-based holdup methods. In case the reverse layout was used, measured BI values were compared to LYSC standards to determine the partial activity of each PDZ sample. 32 Then, the determined partial activity was applied in the calculations to normalize the obtained BI values. In total, our database includes 59,179 unique interactions characterized by DAPF method, originating from 62,604 measurements. We have found that the standard deviation of duplicate experiments (n = 2,780) had an average of 0. 029 BI (with a few outliers) and a median of 0. 019 BI, respectively. Based on these, we used σ = 0.
Then, this mixture was used for preparing a dilution series of the competitor (i. e. the studied peptides) and the measurement was carried out identically as in the direct experiment. Analysis of FP experiments were carried out using ProFit, an in-house developed, Python-based fitting program  (Simon et al. , 2020). The dissociation constants of the direct and competitive FP experiments were obtained by fitting the measured direct data with a quadratic binding equation first and by fitting the measured competitive data with a competitive equation, using several obtained parameters from the first fit  (Roehrl et al. , 2004). In part, competitive fluorescence polarization data was taken from  (Jané et al. , 2020)  and  (Gogl et al. , 2020). In total, 395 unique interactions were characterized by competitive FP in our database.
In the least sensitive experiment, the K d quantification threshold was found to be only 30 34 µM and it was at 790 µM in the most sensitive experiment. On average, the quantification threshold was around K d = 320 µM. In and >0. 681 pKd difference indicates a 99. 7% confidence (3 σ). However, this does not mean that only >0. 454 pKd differences should be considered as reliable in our database. One needs to assess the uncertainty of each studied measurement in several ways (e. g. by comparing different holdup measurements, or the ways of affinity conversion) in order to make objective judgment on data quality.
Profaff database To aid the access to our affinity measurements, we established a PHP-and MySQL-based online database that is accessible at the https://profaff. igbmc. science/ address. On the database, one can browse the affinity profiles of PDZ-domains and PBMs by searching based on gene ID, motif sequence or consensus motif. It is possible to access all additional information of each interaction, such as measured BI values, details of normalization and conversion, or even the competitive titrations of FP measurements. In addition, for each PDZ-domains where we detected binders above a certain (adjustable) binding threshold, we provide affinity weighted frequency plots, based on the sequences of all detected binders, where the weights are calculated based on eq. 3: weig ht =10 p K d -bindingt hres h old (3) Finally, the ProfAff database is built so that it can include any holdup measurements done on other types of domain-motif interaction.
For example, in the actual version it also includes measured affinities from some of our previous works: of 14-3-3 proteins with a few phosphopeptides (phosphorylated PBMs)  (Gogl et al. , 2021)  and of HPV E6 proteins with host LxxLL motifs  (Bonhoure, A. et al, manuscript in preparation). All these sub-databases are interconnected therefore, it is possible to see whether a PBM is also targeted by 14-3-3 domains, or if a protein also contains an LxxLL site. The code of the database is uploaded to GitHub (at https://github. com/GoglG/ProfAff ). We hope that the ProfAff database will serve as a proof of concept database for the community and will set a new trend for future interactomic databases.
Quantitative Reference Interactome A non-exhaustive collection of previously published PDZ-PBM affinities used as an external benchmark was generated as follows. We started from the database published by Amacher et al 36  (Amacher et al. , 2020) , which makes an inventory of different PDZ-PBM affinities published up to 2007. To complete this list, articles were collected from PubMed using the keywords "PDZ[Title] AND interaction" (April 2021). If an affinity measurement was performed, the nature of the PDZ and the PBM interacting along with their Uniprot codes, the method used for measuring the interaction, the eventual modification to the PBM peptide necessary for the measurement (i. e. fluorophore, biotin, etc), and the measured K d along with the standard deviation of the measurement (when available), were collected.
The same information was PBM. For a complete list of this reference interactome, as well as references for each interaction, see Table  S1. For interaction matching we used only affinities from human, mouse, and rat PDZ-domains. PDZ-domain boundaries were not assessed during data collection. In addition, the database contains PBMs with lengths between 4 and >100 residues, but only affinities from 8-12mer peptides were used for interaction matching. Interaction matching was performed based on corresponding 10mer PBM sequences (i. e. exact, extended, or truncated) and PDZ-domain names. In total, we have found 362 matching affinities between our measurements and this filtered benchmark dataset, where the affinities showed a PCC of 0. 59 (see Figure  2B ).
In addition, we do not take into account the formation of higher order, multivalent complexes that may form, especially at higher concentrations. The contribution of unmeasurable weak interactions was also neglected in the calculations. Although for these reasons, our calculation is inherently imperfect, it readily provides a lower limit for the affinity of the "fulllength complexes" since most of these neglected effects would in principle increase the global affinity. Most importantly, this simple calculation can be done without any a priori knowledge about the synergistic nature of each multi-domain-proteins. To collect previously observed qualitatively reported binding events between PDZ and PBM proteins, we analyzed the Biogrid (4. 4. 199 version), Intact (2020_10_01 version), and Bioplex (Dec_2019 version, both 293T and HCT116 datasets) databases  (Oughtred et al.
, 2021)   (Kerrien et al. , 2012)   (Huttlin et al. , 2021). We performed the search within the interactomic space that we experimentally studied using the holdup method. To find instances of reported interactions, we looked for co-occurrence of the IDs of PDZ-and PBM-proteins within single entries. In case the PBM originated from a PDZ-protein, this algorithm identifies it as an observed interaction. Thus, any potential self-binding was manually checked in all databases. In total, we have identified 1,233, 629, 152 and 56 observed interactions from the Biogrid, Intact, Bioplex (293T) and Bioplex (HCT116) databases. In total, our qualitative interactome includes 1,654 unique interactions observed between the 150 PDZ-proteins and the 448 PBM-proteins. Out of these, 38 interactions.
Crystallization For crystallization, PDZ domains were cloned with an N-terminal TEV protease cleavable His 6 tag and a C-terminal ANXA2 (Annexin A2) tag  (Gógl et al. , 2018)   (Ecsédi et al. , 2020). His 6 -PDZ-ANXA2 constructs were produced in E. coli BL21(DE3) with IPTG induction (1 mM IPTG at 18 ºC for overnight expression) and harvested cells were lysed in a buffer containing 50 mM TRIS pH 7. 5, 150-300 mM NaCl, 2 mM BME, cOmplete EDTA-free protease inhibitor cocktail (Roche, Basel, Switzerland), 1% Triton X-100, and trace amount of DNAse, RNAse, and 39 crystal structures of MAGI1_2-ANXA2 complex (PDB ID 5N7D) and the structures of the PDZ domains of SNTB2, SYNJ2BP, and SNX27 (2VRF, 2JIK, 6SAK) using Phaser and structure refinement was carried out with PHENIX  (Adams et al. , 2010). TLS refinement was applied during the refinement.
(C) Affinity distribution histogram of all the protein-protein interactions found in large scale interactomic resources, for which we could match PDZ-PBM pK d values quantified in our measurements. The affinities used for the distribution are taken from our dataset. For interactions involving PDZ-proteins with multiple PDZ-domains, "additive" affinities were considered. Histograms were calculated using all available affinities using a bin size of 0. 1 pK d. (D) Affinity distribution histogram of all the potential human protein-protein interactions that may exist according to our domain-motif data. The affinities used for the distribution are obtained from our PDZ-PBM affinity dataset. For interactions involving PDZ-proteins with multiple PDZ-domains, "additive" affinities were considered.
our database, converted affinities are shown as pK d values, where pK d is defined as the negative 10-base logarithm of the dissociation constant. Therefore, pK d values are closely related to the change in Gibbs free energy upon binding. Due to the different detection thresholds of the different holdup experiments, as well as the different peptide concentrations in each assay, we do not merge together the affinities determined by different holdup methods. Instead, we provide each determined affinities separately for all measured holdup assays, as well as a "composite affinity" for each interaction, which is taken from the most reliable holdup method. The uncertainty of determined pKd values can be estimated in at least two different ways, either using the measured, or propagated uncertainty of BI values, or by measuring the pKd variability between the holdup and the FP experiments.
Standard deviation of BI values is provided in our database in case more than one experiment was performed. In case of singlicate experiments, one can propagate the errors that we observed in the same kind of experiments when duplicates were measured (σ = 0. 05 for LYSC/SAPF and σ = 0. 025 for DAPF experiments). This standard deviation of BI values can be used to propagate the error into the determined pKd value. However, an accurate error propagation model needs to include both the variability of the BI values and the peptide concentration and the latter is difficult to assess based on current data. Alternatively, it is possible to do error estimation based on pKd values determined by FP. Note that we use the FP dataset for the determination of the peptide concentration, therefore the two datasets are globally centered.