 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P55160 from www.uniprot.org...
The NucPred score for your sequence is 0.69 (see score help below)
1 MSLTSAYQHKLAEKLTILNDRGQGVLIRMYNIKKTCSDPKSKPPFLLEKS 50
51 MEPSLKYINKKFPNIDVRNSTQHLGPVHREKAEIIRFLTNYYQSFVDVME 100
101 FRDHVYELLNTIDACQCHFDINLNFDFTRSYLDLIVTYTSVILLLSRIED 150
151 RRILIGMYNCAHEMLHGHGDPSFARLGQMVLEYDHPLKKLTEEFGPHTKA 200
201 VSGALLSLHFLFVRRNQGAEQWRSAQLLSLISNPPAMINPANSDTMACEY 250
251 LSVEVMERWIIIGFLLCHGCLNSNSQCQKLWKLCLQGSLYITLIREDVLQ 300
301 VHKVTEDLFSSLKGYGKRVADIKESKEHVIANSGQFHCQRRQFLRMAVKE 350
351 LETVLADEPGLLGPKALFAFMALSFIRDEVTWLVRHTENVTKTKTPEDYA 400
401 DSSIAELLFLLEGIRSLVRRHIKVIQQYHLQYLARFDALVLSDIIQNLSV 450
451 CPEEESIIMSSFVSILSSLNLKQVDNGEKFEFSGLRLDWFRLQAYTSVAK 500
501 APLHLHENPDLAKVMNLIVFHSRMLDSVEKLLVETSDLSTFCFHLRIFEK 550
551 MFAMTLEESAMLRYAIAFPLICAHFVHCTHEMCPEEYPHLKNHGLHHCNS 600
601 FLEELAKQTSNCVLEICAEQRNLSEQLLPKHCATTISKAKNKKTRKQRQT 650
651 PRKGEPERDKPGAESHRKNRSIVTNMDKLHLNLTELALTMNHVYSFSVFE 700
701 HTIFPSEYLSSHLEARLNRAIVWLAGYNATTQEIVRPSELLAGVKAYIGF 750
751 IQSLAQFLGADASRVIRNALLQQTQPLDSCGEQTITTLYTNWYLESLLRQ 800
801 ASSGTIILSPAMQAFVSLPREGEQNFSAEEFSDISEMRALAELLGPYGMK 850
851 FLSENLMWHVTSQIVELKKLVVENMDILVQIRSNFSKPDLMASLLPQLTG 900
901 AENVLKRMTIIGVILSFRAMAQEGLREVFSSHCPFLMGPIECLKEFVTPD 950
951 TDIKVTLSIFELASAAGVGCDIDPALVAAIANLKADTSSPEEEYKVACLL 1000
1001 LIFLAVSLPLLATDPSSFYSIEKDGYNNNIHCLTKAIIQVSAALFTLYNK 1050
1051 NIETHLKEFLVVASVSLLQLGQETDKLKTRNRESISLLMRLVVEESSFLT 1100
1101 LDMLESCFPYVLLRNAYREVSRAFHLN 1127
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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