 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q02789 from www.uniprot.org...
The NucPred score for your sequence is 0.42 (see score help below)
1 MEPPSPQDEGLRKKQPKKPVPEILPRPPRALFCLTLQNPLRKACISIVEW 50
51 KPFETIILLTIFANCVALAVYLPMPEDDNNTLNLGLEKLEYFFLIVFSIE 100
101 AAMKIIAYGFLFHQDAYLRSGWNVLDFIIVFLGVFTVILEQVNIIQTNTA 150
151 PMSSKGAGLDVKALRAFRVLRPLRLVSGVPSLQVVLNSIFKAMLPLFHIA 200
201 LLVLFMVIIYAIIGLELFKGKMHKTCYFIGTDIVATVENEKPSPCARTGS 250
251 GRPCTINGSECRGGWPGPNHGITHFDNFGFSMLTVYQCISMEGWTDVLYW 300
301 VNDAIGNEWPWIYFVTLILLGSFFILNLVLGVLSGEFTKEREKAKSRGTF 350
351 QKLREKQQLEEDLRGYMSWITQGEVMDVDDLREGKLSLDEGGSDTESLYE 400
401 IEGLNKIIQFIRHWRQWNRVFRWKCHDLVKSKVFYWLVILIVALNTLSIA 450
451 SEHHNQPLWLTHLQDVANRVLLTLFTIEMLMKMYGLGLRQYFMSIFNRFD 500
501 CFVVCSGILEILLVESGAMSPLGISVLRCIRLLRLFKITKYWTSLSNLVA 550
551 SLLNSIRSIASLLLLLFLFIIIFALLGMQLFGGRYDFEDTEVRRSNFDNF 600
601 PQALISVFQVLTGEDWNSVMYNGIMAYGGPTYPGVLVCIYFIILFVCGNY 650
651 ILLNVFLAIAVDNLAEAESLTSAQKAKAEERKRRKMSKGLPDKSEEERAT 700
701 VTKKLEQKSKGEGIPTTAKLKIDEFESNVNEVKDPYPSADFPGDDEEDEP 750
751 EIPVSPRPRPLAELQLKEKAVPIPEASSFFIFSPTNKIRVLCHRIVNATW 800
801 FTNFILLFILLSSAALAAEDPIRADSMRNQILEYFDYVFTAVFTVEIVLK 850
851 MTTYGAFLHKGSFCRNYFNILDLLVVAVSLISMGLESSAISVVKILRVLR 900
901 VLRPLRAINRAKGLKHVVQCVFVAIRTIGNIVLVTTLLQFMFACIGVQLF 950
951 KGKFYSCNDLSKMTEEECRGYYYIYKDGDPTQIELRPRQWIHNDFHFDNV 1000
1001 LSAMMSLFTVSTFEGWPQLLYKAIDSNEEDTGPVYNNRVEMAIFFIIYII 1050
1051 LIAFFMMNIFVGFVIVTFQEQGETEYKNCELDKNQRQCVQYALKARPLRC 1100
1101 YIPKNPYQYQVWYVVTSSYFEYLMFALIMLNTICLGMQHYNQSEQMNHIS 1150
1151 DILNVAFTIIFTLEMVLKLIAFKPRGYFGDPWNVFDFLIVIGSIIDVILS 1200
1201 EIDTFLASSGGLYCLGGGCGNVDPDESARISSAFFRLFRVMRLVKLLNRA 1250
1251 EGVRTLLWTFIKSFQALPYVALLIVMLFFIYAVIGMQMFGKIAMVDGTQI 1300
1301 NRNNNFQTFPQAVLLLFRCATGEAWQEILLACSYGKLCDPESDYAPGEEH 1350
1351 TCGTNFAYYYFISFYMLCAFLIINLFVAVIMDNFDYLTRDWSILGPHHLD 1400
1401 EFKAIWAEYDPEAKGRIKHLDVVTLLRRIQPPLGFGKFCPHRVACKRLVG 1450
1451 MNMPLNSDGTVTFNATLFALVRTALKIKTEGNFEQANEELRAIIKKIWKR 1500
1501 TSMKLLDQVIPPIGDDEVTVGKFYATFLIQEHFRKFMKRQEEYYGYRPKK 1550
1551 DTVQIQAGLRTIEEEAAPEIHRAISGDLTAEEELERAMVEAAMEEGIFRR 1600
1601 TGGLFGQVDNFLERTNSLPPVMANQRPLQFAEIEMEELESPVFLEDFPQN 1650
1651 PGTHPLARANTNNANANVAYGNSSHRNNPVFSSICYEREFLGEADMPVTR 1700
1701 EGPLSQPCRASGPHSRSHVDKLKRPMTQRGMPEGQVPPSPCQVTGAKAEH 1750
1751 PVQKEGKGPTSRFLETPNSRNFEEHVPRNSAHRCTAPATAMLIQEALVRG 1800
1801 GLDSLAADANFVMATGQALADACQMEPEEVEVAATELLKQESPEGGAVPW 1850
1851 EP 1852
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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