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Prediction of ID and Its Use in Functional Proteomics 85Fig. 3. Illustration of disorder-to-order transition on binding. This example shows thebinding of a disordered region of Bad (ribbon) binding to Bcl-XL (globular). Modifiedfrom Oldfield et al. (82).the official PONDR site (http://www.pondr.com/) by special request. A typicaloutput of this predictor is shown in Fig. 2A as magenta horizontal bars. Noticethat the predicted α-MoRFs are located within the distinctive downward spikein the PONDR VLXT curve.3.3.2. Predicting Potential Phosphorylation SitesIt has been shown that intrinsic disorder prediction might help increasethe prediction accuracy of several protein posttranslational modificationsites, including protein phosphorylation (83) and methylation (99). For example,DEPP (or DisPhos) uses disorder information to improve the discriminationbetween phosphorylation and nonphosphorylation sites. The retrievedprediction score approximates the probability that the residue is phosphorylated.Only residues with a prediction score more than 0.5 (which) are consideredto be phosphorylated. The step-by-step protocol of DEPP analysis ispresented next.1. Go to the PONDR working page and click the DEPP Prediction button. This willbring you to the DEPP working page. While on this page, type Protein name inthe space provided (optional) and enter NCBI Accession Code or ProteinSequence (FASTA format or sequence only) in the corresponding boxes. Scrolldown the page and check the box Raw Output at the Output Options section. Byclicking Submit Query button you will be forwarded to the DEPP results page.2. The top of DEPP results page represents the plot providing the distribution ofDEPP scores over the amino acid sequence. You will have three types of symbolscorresponding to the Thr (green triangles), Ser (blue squares), and Tyr residues(red circles) predicted to be phosphorylated. Only residues possessing DEPPscores more than 0.5 are shown. Fig. 4 represents an illustrative DEPP plot for thetransactivation domain of human progesterone receptor (residues 1-566, Swiss-Prot accession no. P06401).3. Raw data related to this analysis are at the end of the page in the PREDICTORVALUES section. The DEPP NNP STATISTICS section provides useful informationon the number of phosphorylated serines, threonines, and tyrosines, together with
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Gene Function Analysis
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METHODS IN MOLECULAR BIOLOGYGene Fu
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PrefaceThis volume of Methods in Mo
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Prefaceixcolleagues demonstrate how
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xiiContentsPART III EXPERIMENTAL ME
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ICOMPUTATIONAL METHODS I
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4 BidautTable 1Input File Format Us
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6 BidautTable 2Folder Layout to Use
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8 Bidaut• alphaA: this is the num
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10 Bidautcomputing the maximum corr
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12 BidautFig. 3. The complete Clutr
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Table 3Some Identified Patterns (5,
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16 BidautFig. 4. This is a comparis
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18 BidautReferences1. Hughes, T. R.
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20 Kirov et al.way to associate gen
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22 Kirov et al.based on a study ass
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24 Kirov et al.1. Retrieve the gene
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26Fig. 1. Functional associations f
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28 Kirov et al.Fig. 2. Pathway anal
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30 Kirov et al.3. Gene symbols usag
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32 Kirov et al.9. OBO_Team, Open Bi
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3Estimating Gene Function With Leas
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Estimating Gene Function With LS-NM
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50 Gonye et al.activity and problem
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52 Gonye et al.Currently, PAINT can
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54 Gonye et al.dynamic nature of th
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Prediction Using PAINT 57represente
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7Estimating Protein Function Using
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Estimating Protein Function Using P
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130 Davuluriinteracting proteins an
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Table 1Web URLs of Promoter, TF Dat
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134 DavuluriPWM-based models do not
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136 DavuluriTF-map alignments of or
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138 Davuluridiscussed which program
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140 DavuluriTable 2ER-a-Responsive
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Table 3Sample Data Matrix Represent
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Table 3 (Continued)Class MYCMAX MYC
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146 DavuluriFig. 3. (A) CART Tree:
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148 Davuluri11. Vlieghe, D., Sandel
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150 Davuluri44. Berezikov, E., Gury
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9Mining Biomedical Data Using MetaM
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Mining Biomedical Data Using MMTx a
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172 Ho et al.Fig. 1. Artificial exa
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174 Ho et al.allowing for cases whe
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176 Ho et al.A different measure is
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178 Ho et al.3.1.3. LA and Generali
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180 Ho et al.The ECF-statistic can
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182 Ho et al.In the special case of
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184 Ho et al.Fig. 5. An illustratio
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186 Ho et al.Fig. 7. The power curv
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188 Ho et al.this section were not
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190 Ho et al.References1. Schena, M
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IIIEXPERIMENTAL METHODS
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194 Caldwell et al.for sequences th
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196 Caldwell et al.query because it
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198 Caldwell et al.Fig. 1. (A) Prot
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200 Caldwell et al.outside primer o
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202 Caldwell et al.5. Targeting scr
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204 Caldwell et al.will allow the s
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206 Caldwell et al.3.1.6. Plasmid P
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208 Caldwell et al.PCR amplify the
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210 Caldwell et al.8. Thawing cells
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212 Zhang et al.Going one step beyo
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214 Zhang et al.Fig. 2. Generation
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216 Zhang et al.Perform PCR cycles,
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218 Zhang et al.Fig. 4. Schematic m
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220 Zhang et al.Fig. 5. Replacement
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13Construction of Simple and Effici
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DNA Vector-Based shRNA-Expression S
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244 Hust et al.overcome by two appr
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246 Hust et al.Fig. 1. Schematic de
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248 Hust et al.interaction during p
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250 Hust et al.3.4. Titering1. Inoc
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252 Hust et al.10. Shortly before u
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254 Hust et al.activity by preservi
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15A Bacterial/Yeast Merged Two-Hybr
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Screening in Yeast With a Bacterial
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16A Bacterial/Yeast Merged Two-Hybr
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Dual Bait-Compatible Bacterial Two-
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318 Thibodeau-Beganny and Joungbeen
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320 Thibodeau-Beganny and JoungFig.
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324 Thibodeau-Beganny and JoungTypi
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328 Thibodeau-Beganny and JoungPCR
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330 Thibodeau-Beganny and Joung16-1
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332 Thibodeau-Beganny and Joung2. P
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334 Thibodeau-Beganny and Joung11.
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336 IndexKknockin (gene knockin) 19
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