Protein Identification from SDS-PAGE:Protein in gel bands (self-made gel), have been reduced with DTT, alkylated with iodoacetomide, subjected to 3x alternating washes, and proteolysis with Trypsin (sequential grade, Promega) in 50mM TEAB buffer at 37C overnight. Peptides extracted and desalted on u-HLB Oasis plates, eluted with 60 acetonitrile/ 0.1%TFA, dried.

Dry peptides re-constituted in 20uL acetonitrile/0.1uL,and analyzed by LC/MS/MS on Orbitrap Lumos-ETD. Resolution set to 120K, 30K for the precursor and fragment ions, respectively. HCD energy at 31 with a isolation window set to 0.8Da without offset. Injected 10% for samples 3 and 4.

Data analysis: MS/MS raw data were searched via PD2.3 with Mascot 6.1, against RefSeq2017_83_human database (containing common contaminants). Mascot .dat files were validated with PD2.3-Percolator.

The data and data analysis scripts in this dataset are referenced in the manuscript, "LILBID laser dissociation curves: a mass spectrometry-based method for the quantitative assessment of dsDNA binding affinities", which is in preparation for publication. The contents of this dataset are as follows:

1) raw data from UV melting curves 2) settings, concentrations, and both raw and processed data from ITC experiments 3) raw spectrum and imaging data from qLILBID experiments 4) programming scripts used to process the qLILBID data

Notes on the ITC data: The iTC200 microcalorimeter (Malvern Panalytical, Malvern, UK) used in the ITC experiments produces .itc files to be opened and analyzed in Origin (Originlab, Northampton, MA, US) using an add-on. The resulting Origin files, including data interpretation and figures, are provided here. Raw data and interpreted data have been gathered from the .itc files and the Origin files and assembled into tab-separated .dat files, so that the data are also accessible to users who do not have Origin.

The Origin files can be understood as follows. After data collection, the ITC raw data are loaded into the Origin-based software. Initially, the baseline is created (Data1Coeff worksheet) and the data plotted in µcal/second as a function of time (minutes), shown in the mRawITC (graph) and the Data1RAW (data) windows. The peaks are integrated (area in µcal) and then plotted in units of kcal/mole of injectant as a function of molar ratio (injected ligand per molecule in the cell), shown in the DeltaH window. The first injection is negligible and therefore always deleted. According to the data points in the DeltaH window, a curve is fitted to obtain the molar ratio (N), Ka, ΔH and ΔS, the data is shown in the Data1 worksheet. The Data1 worksheet hereby contains the following information: DH: heat change resulting from the given injection (µcal/injection); INJV: volume of the injection; Xt: concentration of injected ligand in the cell before next injection; Mt: concentration of molecule in the cell before next injection; volume corrected; XMt: molar ratio of ligand per molecule in the cell after the injection as displayed in the DeltaH window; NDH: Normalized DH in kcal/moles of injectant as displayed in the DeltaH window, Fit: data points of the fitted curve. In the end, the results are presented in the ITCFINAL window (final figure). Additional information can be found in the MicroCal iTC200 System User Manual.

The same data labeling system has been used for the tab-separated .dat files.

This example dataset is used to demonstrate the PIQMIe proteomics web service.

Input files: *.txt|fasta

Output files: *.dat|sqlite

Protein phosphorylation is instrumental to early signaling events. Studying system-wide phosphorylation in relation to processes under investigation requires a quantitative proteomics approach. In Arabidopsis, auxin application can induce pericycle cell divisions and lateral root formation. Initiation of lateral root formation requires transcriptional reprogramming following auxin-mediated degradation of transcriptional repressors. The immediate early signaling events prior this derepression are virtually uncharacterized. To identify the signal molecules responding to auxin application we used the Lateral Root Inducible System which was previously developed to trigger synchronous division of pericycle cells. To identify and quantify the early signaling events following this induction we combined 15N based metabolic labeling and phospho-enrichment and applied a mass spectrometry based approach. In total, 3068 phosphopeptides were identified from auxin treated root tissue. This experiment represents one of the largest quantitative phosphoprotein dataset on Arabidopsis to date. Key proteins responding to auxin treatment included the MDR and PIN2 auxin carriers, auxin response factor2, suppressor of auxin resistance3 and sorting nexin1 (SNX1). Mutational analysis of Serine16 of SNX1 showed that overexpression of the mutated forms of SNX1led to retarded growth and reduction of lateral root formation due to the reduced outgrowth of the primodium, showing proof of principle for our approach. Data Processing: All MS/MS spectra were centroided and merged to a single peak list file using MaxQuant (version 1.0.13.13), which was searched using the Mascot search engine (version 2.2.0, Matrix Science) containing a total 67344 entries (which includes contaminants and an equivalent number of decoy sequences) generated from the publicly available Arabidopsis database (The Arabidopsis Information Resource (TAIR9); June 2009; file name, TAIR9_pep_20090619) with carbamidomethylcysteine as a fixed modification. Oxidized methionine and phosphorylation (serine, threonine, and tyrosine) were searched as variable modifications. Searches were done with tryptic specificity allowing two miscleavages and an initial tolerance on mass measurement of 50 ppm in MS mode and 0.6 Da for MS/MS ions and score cutoff of 20. The resulting .dat files were exported and filtered for a <1% false discovery rate (FDR) at the peptide level using in-house developed software Rockerbox (Version1.1.0) (35). Relative quantification ratios of the identified phosphopeptides and protein were derived by MSQuant (MSQ2.0b4, 2010-02-25) (36). Proteins were quantified with at least two unique non phosphopeptides or single peptide with a score no less than 60. The phospho peptide ratio was normalized by dividing the ratio of its protein of origin. To determine whether phosphorylation ratios for normalized peptides were significantly different a p-value was generated using significance B which is part of the MaxQuant package. The changes were considered significant at a p-value of 0.05 or less. For comparison at the phosphopeptide level the most recent the full dataset with experimental sites was downloaded from PhosPhAt 4.0. Phosphopeptides with ambiguous residues denoted as (s), (t), etc., were converted to S and T, respectively. In addition we used Supplementary Table S1 from Lan et al., (2012) for phosphopeptide comparison. Modifications other than nonambiguous phosphorylation were ignored resulting in a reduction from the original 879 phosphopeptides to 556 peptides. We used pep2pro 'TAIR10 wos' dataset for comparison at the protein level.

The .dat files were open on Scaffold™ software to group the peptides identified according to Mascot parameters. (XLSX)

Yeast was cultured in 2 parallel retentostats reaching near-zero growth. 5 time points were sampled from each reactor and labeled with 6-plex TMT. Pooled samples were fractionated with SCX. 2+ and 3+ fractions were run individually on a 3 h gradient of RP-UHPLC and sprayed into a Q-Exactive mass spectrometer. The raw data obtained, were initially processed with proteome discoverer 1.3 (Thermo Fisher, Bremen, Germany). The created peak lists were searched with Mascot (Matrix Science, Version 2.3) using the SGD database (containing 5779 entries) and the following parameters: 50 p.p.m. precursor mass tolerance and 0.05 Da fragment ion tolerance. Up to two missed cleavages were accepted, oxidation of methionine was set up as variable modification whereas cysteine carbamidomethylation and the TMT label on lysines and the N-terminus as fixed modifications. The resulting .dat files were exported and filtered for <1% false discovery rate at peptide level using in-house developed software Rockerbox (version 2.0.1) utilizing the percolator algorithm (van den Toorn et al, 2011). The filtering for significant changing proteins was done with the isobar software and a significance threshold of p ≤ 0.05. Isobar employs robust statistics that captures spectra and sample variability into a single statistical framework which is described in (Breitwieser et al, 2011).

The .dat files were open on Scaffold™ software to group the peptides identified according to Mascot parameters. (XLSX)

This upload is a zip containing the following files:Predicted EI-MS Spectra of CompTox Chemicals Dashboard Structures:Predicted EI-MS spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1021/acs.analchem.6b01622). These data are provided in .dat ASCII format.Predicted MS/MS Spectra in ESI-positive mode of CompTox Chemicals Dashboard Structures:Predicted MS/MS spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1007/s11306-014-0676-4) in ESI-positive mode. These data are provided in .dat ASCII format.Predicted MS/MS Spectra in ESI-negative mode of CompTox Chemicals Dashboard Structures:Predicted MS/MS spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1007/s11306-014-0676-4) in ESI-negative mode. These data are provided in .dat ASCII format.Database of Predicted Spectra of CompTox Chemicals Dashboard Structures:Predicted spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1007/s11306-014-0676-4 and https://doi.org/10.1007/s11306-014-0676-4) in ESI-positive and negative modes and EI-MS. These data are provided in an SQL relational database.Database Schema File of Predicted Spectra of CompTox Chemicals Dashboard Structures:Predicted spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1007/s11306-014-0676-4 and https://doi.org/10.1007/s11306-014-0676-4) in ESI-positive and negative modes and EI-MS. These data are provided in an SQL relational database and described in this SQL Schema file.Chemical Metadata from the CompTox Chemicals Dashboard Linked to Predicted Spectra:Chemical metadata from the CompTox Chemicals Dashboard are linked through the unique DSSTox chemical identifier (DTXCID) enabling integration to predicted mass spectral data. Chemical metadata includes CASRN, molecular formula, SMILES, presence in lists, and data source occurrences.Chemical Structures that failed during mass spectral prediction:Predicted spectra of ~700,000 chemical structures from the CompTox Chemicals Dashboard were generated using the CFM-ID model developed by Allen, et al. (https://doi.org/10.1007/s11306-014-0676-4 and https://doi.org/10.1007/s11306-014-0676-4) in ESI-positive and negative modes and EI-MS. Due to structural errors and model constraints, the prediction of all MS modes failed for 56 structures.