 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q13698 from www.uniprot.org...
The NucPred score for your sequence is 0.41 (see score help below)
1 MEPSSPQDEGLRKKQPKKPVPEILPRPPRALFCLTLENPLRKACISIVEW 50
51 KPFETIILLTIFANCVALAVYLPMPEDDNNSLNLGLEKLEYFFLIVFSIE 100
101 AAMKIIAYGFLFHQDAYLRSGWNVLDFTIVFLGVFTVILEQVNVIQSHTA 150
151 PMSSKGAGLDVKALRAFRVLRPLRLVSGVPSLQVVLNSIFKAMLPLFHIA 200
201 LLVLFMVIIYAIIGLELFKGKMHKTCYFIGTDIVATVENEEPSPCARTGS 250
251 GRRCTINGSECRGGWPGPNHGITHFDNFGFSMLTVYQCITMEGWTDVLYW 300
301 VNDAIGNEWPWIYFVTLILLGSFFILNLVLGVLSGEFTKEREKAKSRGTF 350
351 QKLREKQQLDEDLRGYMSWITQGEVMDVEDFREGKLSLDEGGSDTESLYE 400
401 IAGLNKIIQFIRHWRQWNRIFRWKCHDIVKSKVFYWLVILIVALNTLSIA 450
451 SEHHNQPLWLTRLQDIANRVLLSLFTTEMLMKMYGLGLRQYFMSIFNRFD 500
501 CFVVCSGILEILLVESGAMTPLGISVLRCIRLLRIFKITKYWTSLSNLVA 550
551 SLLNSIRSIASLLLLLFLFIVIFALLGMQLFGGRYDFEDTEVRRSNFDNF 600
601 PQALISVFQVLTGEDWTSMMYNGIMAYGGPSYPGMLVCIYFIILFVCGNY 650
651 ILLNVFLAIAVDNLAEAESLTSAQKAKAEEKKRRKMSKGLPDKSEEEKST 700
701 MAKKLEQKPKGEGIPTTAKLKIDEFESNVNEVKDPYPSADFPGDDEEDEP 750
751 EIPLSPRPRPLAELQLKEKAVPIPEASSFFIFSPTNKIRVLCHRIVNATW 800
801 FTNFILLFILLSSAALAAEDPIRADSMRNQILKHFDIGFTSVFTVEIVLK 850
851 MTTYGAFLHKGSFCRNYFNMLDLLVVAVSLISMGLESSAISVVKILRVLR 900
901 VLRPLRAINRAKGLKHVVQCMFVAISTIGNIVLVTTLLQFMFACIGVQLF 950
951 KGKFFRCTDLSKMTEEECRGYYYVYKDGDPMQIELRHREWVHSDFHFDNV 1000
1001 LSAMMSLFTVSTFEGWPQLLYKAIDSNAEDVGPIYNNRVEMAIFFIIYII 1050
1051 LIAFFMMNIFVGFVIVTFQEQGETEYKNCELDKNQRQCVQYALKARPLRC 1100
1101 YIPKNPYQYQVWYIVTSSYFEYLMFALIMLNTICLGMQHYNQSEQMNHIS 1150
1151 DILNVAFTIIFTLEMILKLMAFKARGYFGDPWNVFDFLIVIGSIIDVILS 1200
1201 EIDTFLASSGGLYCLGGGCGNVDPDESARISSAFFRLFRVMRLIKLLSRA 1250
1251 EGVRTLLWTFIKSFQALPYVALLIVMLFFIYAVIGMQMFGKIALVDGTQI 1300
1301 NRNNNFQTFPQAVLLLFRCATGEAWQEILLACSYGKLCDPESDYAPGEEY 1350
1351 TCGTNFAYYYFISFYMLCAFLVINLFVAVIMDNFDYLTRDWSILGPHHLD 1400
1401 EFKAIWAEYDPEAKGRIKHLDVVTLLRRIQPPLGFGKFCPHRVACKRLVG 1450
1451 MNMPLNSDGTVTFNATLFALVRTALKIKTEGNFEQANEELRAIIKKIWKR 1500
1501 TSMKLLDQVIPPIGDDEVTVGKFYATFLIQEHFRKFMKRQEEYYGYRPKK 1550
1551 DIVQIQAGLRTIEEEAAPEICRTVSGDLAAEEELERAMVEAAMEEGIFRR 1600
1601 TGGLFGQVDNFLERTNSLPPVMANQRPLQFAEIEMEEMESPVFLEDFPQD 1650
1651 PRTNPLARANTNNANANVAYGNSNHSNSHVFSSVHYEREFPEETETPATR 1700
1701 GRALGQPCRVLGPHSKPCVEMLKGLLTQRAMPRGQAPPAPCQCPRVESSM 1750
1751 PEDRKSSTPGSLHEETPHSRSTRENTSRCSAPATALLIQKALVRGGLGTL 1800
1801 AADANFIMATGQALADACQMEPEEVEIMATELLKGREAPEGMASSLGCLN 1850
1851 LGSSLGSLDQHQGSQETLIPPRL 1873
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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