 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9SQI2 from www.uniprot.org...
The NucPred score for your sequence is 0.54 (see score help below)
1 MASSSSSERWIDGLQFSSLLWPPPRDPQQHKDQVVAYVEYFGQFTSEQFP 50
51 DDIAELVRHQYPSTEKRLLDDVLAMFVLHHPEHGHAVILPIISCLIDGSL 100
101 VYSKEAHPFASFISLVCPSSENDYSEQWALACGEILRILTHYNRPIYKTE 150
151 QQNGDTERNCLSKATTSGSPTSEPKAGSPTQHERKPLRPLSPWISDILLA 200
201 APLGIRSDYFRWCSGVMGKYAAGELKPPTIASRGSGKHPQLMPSTPRWAV 250
251 ANGAGVILSVCDDEVARYETATLTAVAVPALLLPPPTTSLDEHLVAGLPA 300
301 LEPYARLFHRYYAIATPSATQRLLLGLLEAPPSWAPDALDAAVQLVELLR 350
351 AAEDYASGVRLPRNWMHLHFLRAIGIAMSMRAGVAADAAAALLFRILSQP 400
401 ALLFPPLSQVEGVEIQHAPIGGYSSNYRKQIEVPAAEATIEATAQGIASM 450
451 LCAHGPEVEWRICTIWEAAYGLIPLNSSAVDLPEIIVATPLQPPILSWNL 500
501 YIPLLKVLEYLPRGSPSEACLMKIFVATVETILSRTFPPESSRELTRKAR 550
551 SSFTTRSATKNLAMSELRAMVHALFLESCAGVELASRLLFVVLTVCVSHE 600
601 AQSSGSKRPRSEYASTTENIEANQPVSNNQTANRKSRNVKGQGPVAAFDS 650
651 YVLAAVCALACEVQLYPMISGGGNFSNSAVAGTITKPVKINGSSKEYGAG 700
701 IDSAISHTRRILAILEALFSLKPSSVGTPWSYSSSEIVAAAMVAAHISEL 750
751 FRRSKALTHALSGLMRCKWDKEIHKRASSLYNLIDVHSKVVASIVDKAEP 800
801 LEAYLKNTPVQKDSVTCLNWKQENTCASTTCFDTAVTSASRTEMNPRGNH 850
851 KYARHSDEGSGRPSEKGIKDFLLDASDLANFLTADRLAGFYCGTQKLLRS 900
901 VLAEKPELSFSVVSLLWHKLIAAPEIQPTAESTSAQQGWRQVVDALCNVV 950
951 SATPAKAAAAVVLQAERELQPWIAKDDEEGQKMWKINQRIVKVLVELMRN 1000
1001 HDRPESLVILASASDLLLRATDGMLVDGEACTLPQLELLEATARAIQPVL 1050
1051 AWGPSGLAVVDGLSNLLKCRLPATIRCLSHPSAHVRALSTSVLRDIMNQS 1100
1101 SIPIKVTPKLPTTEKNGMNSPSYRFFNAASIDWKADIQNCLNWEAHSLLS 1150
1151 TTMPTQFLDTAARELGCTISLSQ 1173
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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