AGGRESCAN protein aggregation prediction data is provided as

AGGRESCAN protein aggregation prediction data is provided as the global protein aggregation propensity of the sequence (Na4vSS). With regard to A3D prediction, the total and the average scores corresponding to the overall and average aggregation propensities of the analyzed protein structures are provided. Both in AGGRESCAN and A3D predictions the smallest the score is the highest it is the predicted solubility of the variant (Table 1). The Aβ42 peptide structures corresponding to PDB 2OTK and its variants were modeled using the static and dynamic modes. In Fig. 1 residues are colored according to their Aggrescan3D score. Table 1 and Fig. 1 illustrate the increasing solubilizing effect of the introduced mutations. Because in the used Aβ42 peptide structures the mutated side chains expose to solvent more than 25% of their surface there is a good correlation between AGGRESCAN and A3D scores. A3D aggregation propensity data were compared with previously obtained biological data (Fig. 2), observing a striking correlation between the predicted and the experimentally determined solubility, measured as the total intracellular fluorescence of the GFP fused to the specific peptide variant [1]. Not surprisingly, the best correlation with A3D was found for the monomeric 2OTK structure, which in static mode exhibited an R2=0.994, superior to the correlation found for AGGRESCAN predictions, with R2=0.960. In the same manner, the A3D predicted aggregation propensity exhibits an excellent correlation with the impact the different peptides have on both cell gsk3 inhibitor and viability [1] (Fig. 2), with R2=0.998 and R2=0.999 for the 2OTK structure, respectively; being again more accurate than AGGRESCAN, which predictions exhibit correlation coefficients of R2=0.978 and R2=0.988 with the impacts the peptides cause on cell metabolism and viability, respectively.
Experimental design, materials and methods
Acknowledgments Work in our lab is supported by Ministerio de Economia y Competividad, Spain [BFU2013-44763-P to S.V.].
Specifications table
Value of the data
Data
Experimental design, materials and methods
Multicolor DNA/SMLM imaging A summary/list of fluorescent probes which can be efficiently used in our SMLM approach is shown in Table 2. Particular attention is given to multicolor experiments using fluorescent dyes performing well in conjunction with several DNA dyes suitable for SMLM.
Acknowledgments
Doctoral scholarship Doctus awarded to D.Ż-B. from the European Union is gratefully acknowledged. This work has been supported by the Boehringer Ingelheim Foundation; by University of Heidelberg; and by a grant from Polish National Science Center (2013/11/B/NZ3/00189) and Jagiellonian University (DS 004815). Faculty of Biochemistry, Biophysics and Biotechnology of JU is a partner of the Leading National Research Center (KNOW) supported by the Ministry of Science and Higher Education.
Data This proteomics dataset comprise LC-MS/MS raw files obtained from bottom-up MS analysis of histone H3 and H4 isolated using different procedures (Fig. 1) from mouse and human tissues, which were either stored as frozen samples or formalin-fixed and paraffin-embedded (FFPE). The dataset also includes the output files of the database search for common hPTMs and formalin-induced modifications.
Experimental design, materials and methods The data correspond to three main experiments (Tables 1 and S1): (1) analysis of FFPE and corresponding frozen tissue in mouse spleen or liver, stored for few weeks up to 6 years, (2) analysis of FFPE and corresponding frozen tissue in breast cancer samples derived from 3 patients, and (3) analysis of 20 FFPE breast cancer tissues belonging to different subtypes (Luminal A-like, Luminal B-like, Triple Negative and HER2 positive). The analysis of breast cancer samples was carried out using a super-SILAC mix of heavy-labeled histones from 4 breast cancer cell lines as internal standard.