Diel regulation of protein levels and protein modification had been less studied than transcript rhythms. These data tables in .XLSX format report partial proteome (Table_S1) and phosphoproteome data (Table_S2), assayed using shotgun mass-spectrometry, from cultures of the alga Ostreococcus tauri under light-dark cycles, sampled at Zeitgeber times (ZT, hours) 0, 4, 8, 12, 16 and 20. 10% of quantified proteins but two-thirds of phosphoproteins were rhythmic. Gene Ontology enrichment analysis was applied to infer the functional enrichment of the proteins or phosphoproteins, grouped by their loadings in PCA analysis (Table_S3), by hierarchical clustering (Table_S4) or by the peak time of their rhythmic profile (Table_S5).Prompted by night-peaking and apparently dark-stable proteins, we also tested the proteome of cultures transferred to prolonged darkness for 24, 48, 72 or 96h (Table_S6), where the proteome changed less than under the diel cycle. The raw data are available from ProteomeXchange, with identifiers PXD001734, PXD001735 and PXD002909.

All proteins and phosphosites whose levels are significantly changing during starvation, mating, or fusion time course. The data are presented as 6 distinct tabs in.xls format, presenting proteomics and phosphoproteomics over the starvation time course, proteomics and phosphoproteomics over the mating time course, and proteomics and phosphoproteomics over the fusion time course. Log2-transformed, median-corrected values are shown for all, except for the mating time course where the starvation values have been subtracted. Lighter shading indicates data from individual replicates; darker shading shows average values. An additional first tab presents a detailed legend. (XLSX)

The submitted dataset contains raw files from 96 synthetic peptide libraries, using either HCD or ETD as fragmentation technique. The synthesized 96 tryptic peptide libraries containing >100,000 unmodified peptides plus their corresponding >100,000 phosphorylated counterparts with precisely known sequences and modification sites. All these libraries were subjected to LC-MS/MS on an Orbitrap mass spectrometer using HCD and ETD fragmentation. The generated mass spectrometric data deposited in this database can be used in numerous ways to develop, evaluate and improve experimental and computational proteomic strategies. Raw MS data files were converted into Mascot generic format files (MGF) using Mascot Distiller (2.4.2.0, www.matrixscience.com). Important parameters included: i) signal to noise ratio of 20 for MS/MS and ii) time domain off (no merging of spectra of the same precursor). The MGF files were searched against human IPI v3.72 including the sequences of all 96 libraries,using the Mascot search engine (2.3.1, 24). Search settings: Decoy search using a randomized version of the human IPI v3.72 including the sequences of all 96 libraries was enabled; monoisotopic peptide mass (considering up to two 13C isotopes); trypsin/P as protease; a maximum of four missed cleavages; peptide charge +2 and +3; peptide tol. +/- 5 ppm; MS/MS tol. +/- 0.02 Da; instrument type ESI-Trap (for HCD data) or ETD-Trap (for ETD data) respectively; variable modifications: oxidation (M), phospho (ST), phospho (Y). The result files were exported to pepXML and Mascot XML with default options provided by Mascot.

My PhD study primary contains three parts: Part 1. A new AP-MS technique (FISAP) was developed and it significantly reduced the sample amount and process time for potential large scale applications.

Part 2. Study site-specific phosphotyrosine (pTyr) dependent interactome for the central linear region between the PTB and SH2 domains of the adaptor protein Shc1 with site-specific phosphorylation.

Part 3. Applied FISAP to study the pTyr-dependent interactome of 9 immune co-inhibitory receptors, which are important targets for current tumor immunotherapy.

The quality control of these pulldowns and MS data are included, and the synthesis work and design of the ligation sites of the Shc1 long peptide and 9 immune receptor tails were organised in the Figure and description word file. The raw files generated from Waters UPLC-MS and LCQ-Fleet of the final product of each long peptide for reference are separately uploaded in zip format together. To read the raw file, software Masslynx 4.1 from Waters or Xcalibur 4.0 from ThermoFisher are needed.

At neuronal synapses, activation of group I metabotropic glutamate receptors (mGluR1/5) triggers a form of long-term depression (mGluR-LTD) that relies on new protein synthesis and the internalization of AMPA-type glutamate receptors. Dysregulation of these processes has been implicated in the development of mental disorders such as autism spectrum disorders and therefore merit a better understanding on a molecular level. Here, to study mGluR-induced signaling pathways, we integrated quantitative phosphoproteomics with the analyses of newly synthesized proteins via bio-orthogonal amino acids (azidohomoalanine) in a pulsed labeling strategy in cultured hippocampal neurons stimulated with DHPG, a specific agonist for group I mGluRs. We identified several kinases with important roles in DHPG-induced mGluR activation, which we confirmed using small molecule kinase inhibitors. Furthermore, changes in the AMPA receptor endocytosis pathway in both protein synthesis and protein phosphorylation were identified, whereby Intersectin-1 was validated as a novel player in this pathway. This study revealed several new insights into the molecular pathways downstream of group I mGluR activation in hippocampal neurons, and provides a rich resource for further analyses.

Dynamic protein phosphorylation and dephosphorylation play an essential role in cell cycle progression. Kinases and phosphatases are generally highly conserved across eukaryotes, underlining their importance for post-translational regulation of substrate proteins. In recent years, advances in phospho-proteomics have shed light on protein phosphorylation dynamics throughout the cell cycle and ongoing progress in bioinformatics has significantly improved annotation of specific phosphorylation events to a given kinase. However, the functional impact of individual phosphorylation events on cell cycle progression is often unclear. To address this question, we used the Synthetic Physical Interactions (SPI) method, which enables the systematic recruitment of phospho-regulators to most yeast proteins. Using this method, we identified several putative novel targets involved in chromosome segregation and cytokinesis. The SPI method monitors cell growth and therefore serves as a tool to determine the impact of protein phosphorylation on cell cycle progression.

Skeletal muscles are composed of a heterogeneous collection of fiber types with different physiological adaption in response to a stimulus and disease-related conditions. Each fiber has a specific molecular expression of myosin heavy chain molecules (MyHC). So far MyHCs are currently the best marker proteins for characterization of individual fiber types and several proteome profiling studies have helped to dissect the molecular signature of whole muscles and individual fibers. Herein, we describe a mass spectrometric workflow to measure skeletal muscle fiber type-specific proteomes. To bypass the limited quantities of protein in single fibers, we developed a Proteomics high-throughput Fiber Typing (ProFiT) approach enabling profiling of MyHC in single fibers. Aliquots of protein extracts from separated muscle fibers were subjected to capillary LC-MS gradients to profile MyHC isoforms in a 96-well format. Muscle fibers with the same MyHC protein expression were pooled and subjected to proteomic, pulsed-SILAC and phosphoproteomic analysis. Our fiber type-specific quantitative proteome analysis confirmed the distribution of fiber types in the soleus muscle, substantiates metabolic adaptions in oxidative and glycolytic fibers, and highlighted significant differences between the proteomes of type IIb fibers from different muscle groups, including a differential expression of desmin and actinin-3. A detailed map of the Lys-6 incorporation rates in muscle fibers showed an increased turnover of slow fibers compared to fast fibers. In addition, labeling of mitochondrial respiratory chain complexes revealed a broad range of Lys-6 incorporation rates, depending on the localization of the subunits within distinct complexes.

Protein-Protein, Genetic, and Chemical Interactions for Wolfgeher D (2015):The dynamic interactome of human Aha1 upon Y223 phosphorylation. curated by BioGRID (https://thebiogrid.org); ABSTRACT: Heat Shock Protein 90 (Hsp90) is an essential chaperone that supports the function of a wide range of signaling molecules. Hsp90 binds to a suite of co-chaperone proteins that regulate Hsp90 function through alteration of intrinsic ATPase activity. Several studies have determined Aha1 to be an important co-chaperone whose binding to Hsp90 is modulated by phosphorylation, acetylation and SUMOylation of Hsp90 [1], [2]. In this study, we applied quantitative affinity-purification mass spectrometry (AP-MS) proteomics to understand how phosphorylation of hAha1 at Y223 altered global client/co-chaperone interaction [3]. Specifically, we characterized and compared the interactomes of Aha1-Y223F (phospho-mutant form) and Aha1-Y223E (phospho-mimic form). We identified 99 statistically significant interactors of hAha1, a high proportion of which (84%) demonstrated preferential binding to the phospho-mimic form of hAha1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium (http://proteomecentral.proteomexchange.org) via the PRIDE partner repository [4] with the dataset identifier PXD001737.

All proteins and phosphosites identified in during starvation, mating, or fusion time course. Data presented in S1 Table is also included. The data are presented as 4 distinct tabs in.xls format, presenting proteomics and phosphoproteomics over the starvation and mating time course, and proteomics and phosphoproteomics over the fusion time course. Log2-transformed, median-corrected values are shown for all. Lighter shading indicates data from individual replicates; darker shading shows average values. In the starvation-mating time course, different color shadings highlight the starvation data, the uncorrected mating data and the starvation-subtracted mating data. An additional first tab presents a detailed legend. (XLSX)

Activation of the T cell receptor (TCR) leads to a network of early signaling predominantly orchestrated by tyrosine phosphorylation in T cells. The TCR is commonly activated using soluble anti-TCR antibodies, but this approach is not antigen-specific. Alternatively, activating the TCR using specific antigens of a range of binding affinities in the form of a peptide-major histocompatibility complex (pMHC) is presumed to be more physiological. However, due to the lack of wide-scale phosphotyrosine (pTyr) proteomic studies directly comparing anti-TCR antibodies and pMHC, a comprehensive definition of these activated states remains enigmatic. Elucidation of the tyrosine phosphoproteome using quantitative pTyr proteomics enables a better understanding of the unique features of these activating agents and the role of ligand binding affinity on signaling. Here, we apply the recently established Broad-spectrum Optimization Of Selective Triggering (BOOST) to examine perturbations in tyrosine phosphorylation of human TCR triggered by anti-TCR antibodies and pMHC. Our data reveal that high-affinity ovalbumin (OVA) pMHC activation of the human TCR triggers a largely similar, albeit potentially stronger, pTyr-mediated signaling regulatory axis compared to the anti-TCR antibody. The signaling output resulting from OVA pMHC variants correlates well with their weaker affinities, enabling affinity-tunable control of signaling strength. Collectively, we provide a framework for applying BOOST to compare pTyr-mediated signaling pathways of human T cells activated in an antigen-independent and antigen-specific manner.

We describe a single-step centrifugal elutriation method to produce synchronous G1-phase procyclic trypanosomes at a scale amenable for proteomic analysis of the cell cycle. Using ten-plex tandem mass tag technology, we quantified 5,325 proteins across the cell cycle in this parasite, providing a useful resource for the scientific community. Of these, 384 proteins were classified as cell cycle regulated and these were subdivided into nine distinct clusters of temporal regulation. A number of known cell cycle regulators in trypanosomes were detected in these groups, validating our approach, as well as forty novel and essential cell cycle regulated proteins that could be considered as future drug targets. Through cross-comparison to the TrypTag microscopy database, we were able to validate the cell cycle regulated patterns of expression for many of these proteins of unknown function. A convenient interface to access and interrogate these data is also presented.

This is the ``Raw'' data directory of KinPred that holds the original predictions on the current proteome, the PhosphoProteome version used to generate v1.0 post-filtered data (Final data in this project). Data includes: Reference Human Proteome (Uniprot). File name includes the date human reference proteome was pulled from Uniprot and the file is in fasta format.Reference Human Phosphoproteome (ProteomeScout). Folder name includes the date this data was pulled from ProteomeScout and includes the ProteomeScoutAPI compatible data and citations folder. https://github.com/NaegleLab/ProteomeScoutAPIPredictions for GPS. This folder contains the hi, low, and medium threshold predictions that allow us to infer the kinase-specific edge weights that determine the threshold.

Protein-Protein, Genetic, and Chemical Interactions for Meant A (2019):Proteomic analysis reveals a role for RSK in p120-catenin phosphorylation and melanoma cell-cell adhesion. curated by BioGRID (https://thebiogrid.org); ABSTRACT: The RAS/mitogen-activated protein kinase (MAPK) signaling pathway regulates various biological functions, including cell survival, proliferation and migration. This pathway is frequently deregulated in cancer, including melanoma, which is the most aggressive form of skin cancer. RSK (p90 ribosomal S6 kinase) is a MAPK-activated protein kinase required for melanoma growth and proliferation, but relatively little is known about its function and the nature of its cellular partners. In this study, we used a proximity-based labeling approach to identify RSK proximity partners in cells. We identified many potential RSK interacting proteins, including p120ctn (p120-catenin), which is an essential component of adherens junction (AJ). We found that RSK phosphorylates p120ctn on Ser320, which appears to be constitutively phosphorylated in melanoma cells. We also found that RSK inhibition increases melanoma cell-cell adhesion, suggesting that constitutive RAS/MAPK signaling negatively regulates AJ integrity. Together, our results indicate that RSK plays an important role in the regulation of melanoma cell-cell adhesion.

The characterization of phosphotyrosine mediated protein-protein interactions is vital for the interpretation of downstream pathways of transmembrane signaling processes. Currently however, there is a gap between the initial identification and characterization of cellular binding events by proteomic methods and the in vitro generation of quantitative binding information in the form of equilibrium rate constants (Kd values). In this work we present a systematic, accelerated and simplified approach to fill this gap: using cell-free protein synthesis with site-specific labeling for pull-down and microscale thermophoresis (MST) we were able to validate interactions and to establish a binding hierarchy based on Kd values as a completion of existing proteomic data sets. As a model system we analyzed SH2-mediated interactions of the human T-cell phosphoprotein ADAP. Putative SH2 domain-containing binding partners were synthesized from a cDNA library using Expression-PCR with site-specific biotinylation in order to analyze their interaction with fluorescently labeled and in vitro phosphorylated ADAP by pull-down. On the basis of the pull-down results, selected SH2’s were subjected to MST to determine Kd values. In particular, we could identify an unexpectedly strong binding of ADAP to the previously found binding partner Rasa1 of about 100 nM, while no evidence of interaction was found for the also predicted SH2D1A. Moreover, Kd values between ADAP and its known binding partners SLP-76 and Fyn were determined. Next to expanding data on ADAP suggesting promising candidates for further analysis in vivo, this work marks the first Kd values for phosphotyrosine/SH2 interactions on a phosphoprotein level.

Histoplasma capsulatum is a thermally dimorphic fungus with worldwide distribution, and high incidence in the Americas. It is the etiologic agent of histoplasmosis, an important life-threatening systemic mycosis. Dimorphism is an important feature for fungal survival in different environments and it has been related to the virulence of H. capsulatum, and essential to the establishment of infection. Proteomic profiles have brought important contributions to the knowledge of metabolism and pathogenicity in several biological models. However, studies of the H. capsulatum proteome have been underexplored. In the present study, we report the first proteomic comparison between the mycelium and the yeast cells of H. capsulatum. Liquid chromatography coupled to mass spectrometry was used to evaluate the proteomic profile of the two phases of H. capsulatum. In summary, 214 proteins were only detected/or preferentially abundant in mycelium, while the same occurred to 335 proteins in yeast cells. In mycelium, enzymes related to the glycolytic pathway and to the alcoholic fermentation showed greater abundance, suggesting a higher use of anaerobic pathways for energy production. In yeast cells, proteins related to the tricarboxylic acid cycle and response to temperature stress showed high abundance. Proteins related to oxidative stress response or involved with cell wall metabolism were identified with differential abundance in both conditions. Validation of proteomic data was performed by enzymatic activity determination, western blot assays, or immunofluorescence microscopy. These experiments corroborated, directly or indirectly, the abundance of isocitrate lyase, 2-methylcitrate synthase, catalase B, and mannosyl-oligosaccharide-1,2-alpha-mannosidase in the mycelium and heat shock protein (HSP) 30, HSP60, glucosamine-fructose-6-phosphate aminotransferase, glucosamine-6-phosphate deaminase, and N-acetylglucosamine-phosphate mutase in yeast-cells. The proteomic profile associated functional classification analyzes of proteins provided a better understanding of the metabolic reorganization and cell wall remodeling on the yeast form of H. capsulatum.

This data respository contains data associated with a study to characterize how missense mutations in PTPN11, the gene encoding the protein tyrosine phosphatase SHP2, alter the protein-protein interactions and localization. SHP2 has a catalytic domain that dephosphorylates proteins and two phosphotyrosine-binding SH2 domains. One part of this respository contains data that were used to map the sequence recognition profiles of the two wild-type SH2 domains of SHP2. These sequence recognition profiles were used to predict binding sites for each SH2 domain across the human proteome, and they are juxtaposed with the proteomics data presented in the parent study. The other part of the repository contains microscopy data examining the mitochondrial localization of wild-type SHP2. Methods The deep sequencing data were generated using a high-throughput method for profiling the sequence specificities of SH2 domains. This method has been documented extensively in two published manuscripts: https://doi.org/10.7554/elife.82345https://doi.org/10.1073/pnas.2407159121 The overall approach entails the use of degenerate, genetically-encoded peptide libraries, with a structure X5-Y-X5, where X is any of the 20 canonical amino acids and Y is tyrosine. The libraries (approximately 1 million random sequences) are displayed on the surface of E. coli cells, then enzymatically phosphorylated using a mixture of tyrosine kinases. Then, SH2 domains immobilized on magnetic beads are used to enriched those phosphorylated cells with optimal peptide sequences for the particular SH2 domain. These cells are isolated, and DNA encoding the peptides is amplified by PCR and subject to deep sequencing. An input library that has not been selected by any SH2 domain is also sequenced. Next, the sequenced libraries are translated from DNA to peptide sequences. The frequency of each amino acid at each position in the selected library is counted and normalized to that in the input library. These normalized matrices are used to calculate a binding score for all documented tyrosine phosphorylation sites seen in the PhosphoSitePlus database (https://www.phosphosite.org/homeAction.action). The scoring method is described in detail here: https://doi.org/10.7554/elife.82345 The raw fastq files, the translated sequence files, count matrices, and calculated scores, along with the scripts to generate these scores, are provided in this data repository. The analysis pipeline is also extensively documented here: https://doi.org/10.7554/elife.82345 The confocal microscopy data were acquired under oil immersion 60x magnification (Nikon, MRD71670) using a confocal spinning disk microscope (Andor Dragonfly) coupled to a Nikon Ti-2 inverted epifluorescence microscope with automated stage control, Nikon Perfect Focus System and a Zyla PLUS 4.2-megapixel USB3 camera. Illumination was done with 100 mW 405 nm, 50 mW 488 nm, 50 mW 561 nm and 140 mW 640 nm solid-state lasers. All hardware was controlled using Andor Fusion software. Lasers, laser powers, exposure times, objectives and experiment-specific acquisition parameters are 100% power 100ms exposure for all the images. Images were acquired with 11 z-slices at 2.0-μm intervals (Total scan size 20 μm). The images are in .ims format and can be opened in software such as ImageJ/Fiji. A metadata file for the microscopy data is also provided.

Usage Notes- Data is provided under the CC BY-NC, which permits reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.- Please cite this dataset using the Figshare DOI if used in any publications or derivative work.---## ContentsSupplementary data 1 Cell line ReMEA scoresSupplementary data 2 Fold changes for the LSD1i proteomicsSupplementary data 3 ReMEA scores for the LSD1i proteomicsSupplementary data 4 Average ReMEA scores for primary AML cellsSupplementary data 5 Fold changes from chemotherapy phosphoSupplementary data 6 ReMEA score for chemotherapy phospho---## DescriptionThis dataset contains supporting data for the publication titled "Response Marker Enrichment Analysis (ReMEA) predicts the antiproliferative impact of pharmacological and genetic perturbagens on cancer cells" submitted to Nucleic Acids Research. The data include the collection of ReMEA scores computed with the four large cell line proteomics datasets referenced in the paper. Scores were made using the cell line signatures whose construction is described in the publication.All files are provided in [format(s), e.g., CSV, TXT, PDF, .R, .py, .zip].---## File InventoryBelow is a description of each of the 9 files included dataset 1:1. [STAT_matched_DRUG_scores.csv] Description: Single Train And Test Set scores for drug perturbations. Signatures made from and tested with same proteomics dataset.2. [STAT_matched_RNAi_scores.csv] Description: Single Train And Test Set scores for RNAi perturbations. Signatures made from and tested with same proteomics dataset.3. [STAT_matched_CRISPR_scores.csv] Description: Single Train And Test Set scores for CRISPR perturbations. Signatures made from and tested with same proteomics dataset.4. [LODO_AVG_DRUG_scores.csv] Description: Leave One Dataset Out scores for drug perturbations.5. [LODO_AVG_RNAi_scores.csv] Description: Leave One Dataset Out scores for RNAi perturbations.6. [LODO_AVG_CRISPR_scores.csv] Description: Leave One Dataset Out scores for CRISPR perturbations.7. [k_fold_goncalves_DRUG_remea_scores.csv] Description: K-fold ReMEA drug scores using the Goncalves et al. 2022 proteomics dataset as described in publication.8. [k_fold_goncalves_RNAi_remea_scores.csv] Description: K-fold ReMEA RNAi scores using the Goncalves et al. 2022 proteomics dataset as described in publication.9. [k_fold_goncalves_CRISPR_remea_scores.csv] Description: K-fold ReMEA CRISPR scores using the Goncalves et al. 2022 proteomics dataset as described in publication.---

The architecture of cellular proteins connected to form signaling pathways in response to internal and external cues is much more complex than a group of simple protein-protein interactions. Post translational modifications on proteins (e.g., phosphorylation of serine, threonine and tyrosine residues on proteins) initiate many downstream signaling events leading to protein-protein interactions and subsequent activation of signaling cascades leading to cell proliferation, cell differentiation and cell death. As evidenced by a rapidly expanding mass spectrometry database demonstrating protein phosphorylation at specific motifs, there is currently a large gap in understanding the functional significance of phosphoproteins with respect to their specific protein connections in the signaling cascades. A comprehensive map that interconnects phospho-motifs in pathways will enable identification of nodal protein interactions that are sensitive signatures indicating a disease phenotype from the physiological hemostasis and provide clues into control of disease. Using a novel phosphopeptide microarray technology, we have mapped endogenous tyrosine-phosphoproteome interaction networks in breast cancer cells mediated by signaling adaptor protein GRB2, which transduces cellular responses downstream of several RTKs through the Ras-ERK signaling cascade. We have identified several previously reported motif specific interactions and novel interactions. The peptide microarray data indicate that various phospho-motifs on a single protein are differentially regulated in various cell types and shows global downregulation of phosphoprotein interactions specifically in cells with metastatic potential. The study has revealed novel phosphoprotein mediated signaling networks, which warrants further detailed analysis of the nodes of protein-protein interaction to uncover their biomarker or therapeutic potential.

Human up-frameshift 1 (UPF1) is an ATP-dependent RNA helicase and phosphoprotein implicated in several biological processes but is best known for its key function in nonsense-mediated mRNA decay (NMD). Here we employed a combination of stable isotope labeling of amino acids in cell culture experiments to determine by quantitative proteomics UPF1 interactors. We used this approach to distinguish between RNA-mediated and protein-mediated UPF1 interactors and to determine proteins that preferentially bind the hypo- or the hyper-phosphorylated form of UPF1. Confirming and expanding previous studies, we identified the eukaryotic initiation factor 3 (eIF3) as a prominent protein-mediated interactor of UPF1. However, unlike previously reported, eIF3 binds to UPF1 independently of UPF1’s phosphorylation state. Furthermore, our data revealed many nucleus-associated RNA-binding proteins that preferentially associate with hyper-phosphorylated UPF1 in an RNase-sensitive manner, suggesting that UPF1 gets recruited to mRNA and becomes phosphorylated before being exported to the cytoplasm as part of the mRNP.

Reversible phosphorylation is one of the major mechanisms of signal transduction, and signaling networks are critical regulators of cell growth and development. However, few of these networks have been delineated completely. Towards this end, quantitative phosphoproteomics is emerging as a useful tool enabling large-scale determination of relative phosphorylation levels. However, phosphoproteomics differs from classical proteomics by a more extensive sampling limitation due to the limited number of detectable sites per protein. Here, we propose a comprehensive quantitative analysis pipeline customized for phosphoproteome data from interventional experiments for identifying key proteins in specific pathways, discovering the protein-protein interactions and inferring the signaling network. We also made an effort to partially compensate for the missing value problem, a chronic issue for proteomics studies. The dataset used for this study was generated using SILAC (Stable Isotope Labeling with Amino acids in Cell culture) technique with interventional experiments (kinase-dead mutations). The major components of the pipeline include phosphopeptide meta-analysis, correlation network analysis and causal relationship discovery. We have successfully applied our pipeline to interventional experiments identifying phosphorylation events underlying the transition to a filamentous growth form in Saccharomyces cerevisiae. We identified 5 high-confidence proteins from meta-analysis, and 19 hub proteins from correlation analysis (Pbi2p and Hsp42p were identified by both analyses). All these proteins are involved in stress responses. Nine of them have direct or indirect evidence of involvement in filamentous growth. In addition, we tested four of our predicted proteins, Nth1p, Pbi2p, Pdr12p and Rcn2p, by interventional phenotypic experiments and all of them present differential invasive growth, providing prospective validation of our approach. This comprehensive pipeline presents a systematic way for discovering signaling networks using interventional phosphoproteome data and can suggest candidate proteins for further investigation. We anticipate the methodology to be applicable as well to other interventional studies via different experimental platforms.