Molecular Cell, Volume 46. Supplemental Information

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1 Molecular Cell, Volume 46 Supplemental Information Mapping N-Glycosylation Sites across Seven Evolutionary Distant Species Reveals a Divergent Substrate Proteome Despite a Common Core Machinery Dorota F. Zielinska, Florian Gnad, Katharina Schropp, Jacek R. Wiśniewski, and Matthias Mann Figure S1. Sequence Recognition Motifs N-glycosylation consensus sequences were derived using MotifX (1). WebLogo (2) was used to create relative frequency plots. By far the most abundant sequence motif is the canonical one, with serine and threonine on position 2. In the set of surrounding sequences that do not match with the N-!P-[S T] motif, N-G in S. cerevisiae and N-X-C in remaining species are statistically overrepresented. 1

2 Figure S2. Topology Prediction of Delta-Sarcoglycan versus Localization of the N-Glycosite We predicted the topology of delta-sarcoglycan using the commonly used software HMMTOP (3). The predicted topology is not in concordance with the location of the N-glycosylation site, which is expected to be on the outside of the cell. Therefore, this prediction is presumably incorrect. The inclusion of N-glycosylation information in machine learning methods might increase the performance of topology prediction. 2

3 Figure S3. Comparison of Phylogenetic Group-Specific N-Glycosylated and Non-N- Glycosylated Proteins The proportion of the phylogenetic group specific proteins that have orthologs within the own phylogenetic group, but not in other eukaryotic species is significantly higher in the N- glycoproteome (dark color) compared to the proteome (light color) indicating the role of N- glycosylation in group specific biological processes 3

4 Supplemental Experimental Procedures Protein digestion. The lysates containing 0.4 mg protein, ideally in a volume between 30 and 100 µl, were diluted with 8 M urea in 0.1 M Tris/HCl ph 8.5 to a volume of 200 µl. The solutions were added on the top of Microcon filtration units YM-30 (Millipore) and centrifuged at 14,000 x g at 20 C for 15 min. After addition of 200 µl 8 M urea in 0.1 M Tris/HCl, ph 8.5, the samples were centrifuged for another 15 min at the same conditions. This step was performed twice. The proteins were alkylated with 100 µl 0.05 M iodoacetamide, 8 M urea in 0.1 M Tris/HCl ph 8.5. After 20 min incubation in the dark, the samples were centrifuged at the above stated conditions. Then the filters were rinsed three times with 100 µl 8 M urea in 0.1 M Tris/HCl ph 8.5 and twice with 100 µl 40 mm NH 4 HCO 3. The enzymatic digestion was performed with 4 µg trypsin (Promega) or 15 µg Glu-C (Roche) in 40 µl 40 mm NH 4 HCO 3, simply by adding the enzyme to the top of the filters and incubation overnight at 37 C or 25 C, respectively. Samples were eluted and the filters were washed twice with 40 µl binding buffer (1 mm CaCl 2, 1 mm MnCl 2, 0.5 M NaCl in 20 mm TrisHCl, ph 7.3). Lectin enrichment and deglycosylation. The resulting peptides were transferred to a YM-30 filtration units and incubated for 1 hour with a lectin mixture containing 90 µg ConA, 90 µg WGA and 90 µg RCA120 in 36 µl 2 x binding buffer. To A. thaliana peptides only ConA was added in the amount of 270 µg. After a centrifugation at 14,000 x g at 20 C for 10 minutes, the lectin-bound peptides were washed 4 times with 200 µl binding buffer and twice with 50 µl 40 mm NH 4 HCO 3 in H 18 2 O (CIL). The deglycosylation was performed by adding 2 µl PNGase F (1 U / µl H 18 2 O ) (Roche) in 40 µl 40 mm NH 4 HCO 3 in H 18 2 O and 3 h incubation at 37 C. The deglycosylation of plant glycopeptides was performed by adding 2 mu PNGase A (Europa) in 50 4

5 mm citrate-phosphate buffer, ph 5. The deglycosylated peptides were eluted by centrifugation and the filters were rinsed twice with 50 µl 40 mm NH 4 HCO 3. Mass spectrometry. The deglycosylated peptides were purified on StageTips (4) prior to the measurement on the LTQ-Orbitrap Velos mass spectrometer (Thermo Fisher Scientific, Germany). The mass spectrometer was coupled to HPLC via a nanoelectrospray ion source. The 15 cm fused silica emitter (Proxeon Biosystems, Denmark), packed in-house with the reverse phase material ReproSil-Pur C18 AQ, 3 µm resin (Dr. Maisch, GmbH) was used for peptide separation. A 240 min gradient starting at 2% and ending by 90% of 80% (v/v) CH3CN, 0.5% (v/v) acetic acid was applied. The mass spectrometer was operated in data dependent mode, with full scans acquisition in the Orbitrap with 30,000 resolution at m/z 400. Ten most intense ions were fragmented by HCD with normalized collision energy of 40. The fragment ions were detected in the Orbitrap at 7,500 resolution. Data processing. Analysis of the data was performed with the recently updated MaxQuant software(5) version Cysteine carbamidomethylation was set as a fixed modification and methionine oxidation, protein N-terminal acetylation and asparagine deamidation with 18 O incorporation as variable modifications (for A. thaliana only asparagine deamidation). The peptides were identified using the Andromeda search engine integrated into MaxQuant. MS/MS spectra were searched against the following databases: SGD database, S. pombe Genome Project (Sanger Institute), Wormpep200, FlyBase 5.24, IPI Danio rerio 3.67, IPI Arabidopsis thaliana Fragment ions were identified with maximal mass deviation of 20 ppm. For identification the false discovery rate was specified to 1% on both peptide and site level. Maximum of two miscleavages were allowed and a minimum peptide length of six amino acids was required. 5

6 Data analysis. Sites that were identified in at least two independent measurements with a minimum localization probability of 95% were defined as Class I sites and considered for further analysis. For A. thaliana all sites that do not match the canonical motif N-!P-[S T] and these that were detected as deamidated in a separately measured plant proteome were removed from the high-confidence set. Class I sites and all detected sites are listed in Supplementary Tables S2 and are available in the PHOSIDA database ( along with their corresponding spectra (6). Sequence motif analysis. We used Motif-X (1) to derive overrepresented motifs among sites that did not match with N-!P-[S T]. To illustrate the overrepresentations we applied WebLogo software (2). T Test was used to compare the position specific amino acid frequencies in sequences surrounding glycosylated asparagines residues with the ones in sequences surrounding non-glycosylated asparagines. Secondary structure and solvent accessibility prediction. We performed large-scale secondary structure prediction and solvent accessibility calculation employing the SABLE 2.0 program (7). We applied the method to all identified N-glycosylated proteins and derived the predicted structures of both glycosylated and non-glycosylated asparagines. We used the T Test to statistically estimate differences in secondary structure localization and accessibility. Gene ontology enrichment analysis. To derive over- and underrepresented biological processes and cellular components of glycosylated proteins compared to whole species proteomes Cytoscape (8) and BinGO (9) were used. Gene ontology annotations were retrieved from the GOA database (10). The statistical significance was calculated using the hypergeometric model and the Benjamini Hochberg false discovery rate correction. 6

7 Machine learning based sequence motif analysis. To train a support vector machine based N- glycosylation site predictor, we derived the surrounding sequences of both N-glycosylated (positive set) and non-n-glycosylated (negative set) asparagine residues for each species. The surrounding sequences contained one to eight amino acids surrounding the site to both termini. Training and testing of the support vector machine was performed as described for the prediction of phosphorylated and acetylated sites (6, 11). Interestingly, the performance of the N- glycosylation predictor was comparable with the one of a simple motif matcher: All asparagines of any protein sequence of interest were predicted to be N-glycosylated, if they match with the canonical N-glycosylation motif (N-X-[S T]). Supplemental References 1. D. Schwartz, S. P. Gygi, Nat Biotechnol 23, 1391 (Nov, 2005). 2. T. D. Schneider, R. M. Stephens, Nucleic Acids Res 18, 6097 (Oct 25, 1990). 3. G. E. Tusnady, I. Simon, Journal of molecular biology 283, 489 (Oct 23, 1998). 4. J. Rappsilber, Y. Ishihama, M. Mann, Analytical chemistry 75, 663 (Feb 1, 2003). 5. J. Cox, M. Mann, Nat Biotechnol 26, 1367 (Dec, 2008). 6. F. Gnad et al., Genome Biol 8, R250 (Nov 26, 2007). 7. M. Wagner, R. Adamczak, A. Porollo, J. Meller, J Comput Biol 12, 355 (Apr, 2005). 8. P. Shannon et al., Genome Res 13, 2498 (Nov, 2003). 9. S. Maere, K. Heymans, M. Kuiper, Bioinformatics 21, 3448 (Aug 15, 2005). 10. D. Barrell et al., Nucleic acids research 37, D396 (Jan, 2009). 11. C. Choudhary et al., Science (New York, N.Y 325, 834 (Aug 14, 2009). 7