 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P47179 from www.uniprot.org...
The NucPred score for your sequence is 0.28 (see score help below)
1 MVNISIVAGIVALATSAAAITATTTLSPYDERVNLIELAVYVSDIRAHIF 50
51 QYYSFRNHHKTETYPSEIAAAVFDYGDFTTRLTGISGDEVTRMITGVPWY 100
101 STRLKPAISSALSKDGIYTAIPTSTSTTTTKSSTSTTPTTTITSTTSTTS 150
151 TTPTTSTTSTTPTTSTTSTTPTTSTTSTTPTTSTTSTTPTTSTTSTTPTT 200
201 STTSTTPTTSTTSTTPTTSTTSTTPTTSTTPTTSTTSTTSQTSTKSTTPT 250
251 TSSTSTTPTTSTTPTTSTTSTAPTTSTTSTTSTTSTISTAPTTSTTSSTF 300
301 STSSASASSVISTTATTSTTFASLTTPATSTASTDHTTSSVSTTNAFTTS 350
351 ATTTTTSDTYISSSSPSQVTSSAEPTTVSEVTSSVEPTRSSQVTSSAEPT 400
401 TVSEFTSSVEPTRSSQVTSSAEPTTVSEFTSSVEPTRSSQVTSSAEPTTV 450
451 SEFTSSVEPTRSSQVTSSAEPTTVSEFTSSVEPTRSSQVTSSAEPTTVSE 500
501 FTSSVEPIRSSQVTSSAEPTTVSEVTSSVEPIRSSQVTTTEPVSSFGSTF 550
551 SEITSSAEPLSFSKATTSAESISSNQITISSELIVSSVITSSSEIPSSIE 600
601 VLTSSGISSSVEPTSLVGPSSDESISSTESLSATSTFTSAVVSSSKAADF 650
651 FTRSTVSAKSDVSGNSSTQSTTFFATPSTPLAVSSTVVTSSTDSVSPNIP 700
701 FSEISSSPESSTAITSTSTSFIAERTSSLYLSSSNMSSFTLSTFTVSQSI 750
751 VSSFSMEPTSSVASFASSSPLLVSSRSNCSDARSSNTISSGLFSTIENVR 800
801 NATSTFTNLSTDEIVITSCKSSCTNEDSVLTKTQVSTVETTITSCSGGIC 850
851 TTLMSPVTTINAKANTLTTTETSTVETTITTCPGGVCSTLTVPVTTITSE 900
901 ATTTATISCEDNEEDITSTETELLTLETTITSCSGGICTTLMSPVTTINA 950
951 KANTLTTTETSTVETTITTCSGGVCSTLTVPVTTITSEATTTATISCEDN 1000
1001 EEDVASTKTELLTMETTITSCSGGICTTLMSPVSSFNSKATTSNNAESTI 1050
1051 PKAIKVSCSAGACTTLTTVDAGISMFTRTGLSITQTTVTNCSGGTCTMLT 1100
1101 APIATATSKVISPIPKASSATSIAHSSASYTVSINTNGAYNFDKDNIFGT 1150
1151 AIVAVVALLLL 1161
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.) |
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