 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9XTT4 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MGDQFDSLMERIRVRQAEMSGEGEVAKENGPVTSKITSVKQEVASPTKVF 50
51 GSSSKCNDGPSTPVHFHPQEPKETTPNMKENAENSLNSFKDATVNESSSK 100
101 KTSRFSMLAQEIDEYEYDYQSQYNKPKEAYMKGRSPRMSIGETRPAVLCT 150
151 PAGAQAMKSPNVAVSSKSAPEIALGMSDFEARRIKFAQPIVNVNYLPNES 200
201 SIFSGGSGSSVNDQSTVLGGSAEMMNVTTSSLSCGELSMNQHITHENTII 250
251 NAQSCDNREAILRERQQVSNEEYGPHTFMRKKVPKEASSATSSSSSTTTL 300
301 TTISGASGSTTSGISNAPQDSASTKTTTNTFTSSYLLTKTSNNNINALVS 350
351 SSPKPFSKDIGRSMFSPVHFTPKSTSSPKTLSESIFSPSKSAAVEGSIAT 400
401 TRRLQFEEKLKKSSSANVTAPPAPTSAPVPTPRHVAPLAPTVAQQSHLTP 450
451 NHRHAAQQKKHLFPVVGIVATAPIPVQTQWRGQSNTPVVQGARADEKTAG 500
501 NEPPVGAGVGKLKNLKSRWEFSSATGTPIHPDATEDSLIATAIKMKESAI 550
551 PKQLGHRSERKGPSASSLYSQGARSNTASPASKSTRYEQEEEDDVFEAPE 600
601 FNDGGDVISEDGILQEEEEDTSKFIDNAFGFMEGSGAGTPSPYREPPLQR 650
651 LEKNRPPAEVIEEETENEDESEPYEPEEEEDDDATTQFPVPERSRKSSSQ 700
701 LAYSVSFYRKIQRDRNEESSTVLAGPVISQISPSAPPMSSSLTSQQKLRQ 750
751 LTTGPANGARIVESAKDAHDRIKRAIQVEEQLVAQSKRAMILARDKPSFR 800
801 GSREEFEAQWAMLRHVEKHRALLTEYDRLKRDGPRIIDGPRGTITVSQLS 850
851 VNMARDYVSANIASSKKSDEVFYFAAILRYGEQVDVSKMVTSDGGLNRRG 900
901 VLEFPVPLMLTGIPPDFRATVEIYGQRSMRESTSHEDKYKLKNSTFKAKT 950
951 RNTFLGGGSTSSANQSLFVDPAASSSSTSSTTSNFNLLGTFSFDINCPGK 1000
1001 HLYNMSHTVYPLEGITQMKVRKQAIDGADITYHGFLSMYQRTGEGLGSWT 1050
1051 RYWCALENGEMKFWKQPEDEGTKGYTALMDLSTCCRSEGASVVEDICPFP 1100
1101 NSFHIDVWAPKMDTSDPRGIERLRVMLAADTAQDLQTWLSLINSTSKQLC 1150
1151 TWRNPIVNQ 1159
Positively and negatively influencing subsequences are coloured according to the following scale:
(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)
What does the NucPred score mean?
| You have to decide on a NucPred score threshold. Sequences which score greater than or equal to this threshold are predicted to spend some time in the nucleus. Higher thresholds yield fewer predicted nuclear proteins, but these predictions are more accurate (you can have higher confidence in them). The table below gives more details of the performance of NucPred estimated using the sequences it was trained on (by cross-validation). Another benchmark is available in the Bioinformatics 2007 paper. |
| NucPred score threshold | Specificity | Sensitivity |
| see above | fraction of proteins predicted to be nuclear that actually are nuclear | fraction of true nuclear proteins that are predicted (coverage) |
| 0.10 | 0.45 | 0.88 |
| 0.20 | 0.52 | 0.83 |
| 0.30 | 0.57 | 0.77 |
| 0.40 | 0.63 | 0.69 |
| 0.50 | 0.70 | 0.62 |
| 0.60 | 0.71 | 0.53 |
| 0.70 | 0.81 | 0.44 |
| 0.80 | 0.84 | 0.32 |
| 0.90 | 0.88 | 0.21 |
| 1.00 | 1.00 | 0.02 |
| Sequences which score >= 0.8 with NucPred and which
are predicted by PredictNLS to contain an NLS have been shown to be 93% correct with a coverage of 16%. (PredictNLS by itself is 87% correct with 26% coverage on the same data.) |
Go back to the NucPred Home Page.