 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching O14207 from www.uniprot.org...
The NucPred score for your sequence is 0.98 (see score help below)
1 MNSFLLLDHVSDSQDSGSEWSNTHGSLISQRGNEYDGITPATKEKNQNKL 50
51 EILNNETTIGSTKSSVSSLPELNRNVSFEKSDQNLAETKREEENKRTEVF 100
101 KKDAEHEAVVVKRDFRPRRPEQTRPYTYDFLRHQIEFKRIGLVPITVPHG 150
151 FSSDRSITSKSSHKPVNVIVRNRASSRKPPLTSTHRFRRYGAVISDSDDD 200
201 ESNTEQDHSKESNLNTADNDLALSSTIEGKKTSTSKEALESESLLSDSDQ 250
251 SMTNISSNSTVSDLNLKTLKKRLRGVLPPSFLTLQEKKKLENRGVKKKTS 300
301 LHKSVIEGEKIKGVARRKLHPRSTAKLSSELGNEISDSDNSISTPTPTDD 350
351 SRFDTSEFLDSISRDNGWLKEDVVDQLWLPKRSLSALKKSSSLTSENPFQ 400
401 LNVAANAVSTIPVYRTTKTKMKKNRFKYVEVEKLPDLILESYGKKAPKFL 450
451 RVFARSSSHIPKMIRRKRQMDSKKYFSFDKESDRQVIDQVLSDWYSGKHE 500
501 LVQQSHSYKKPSDSKSVGGNIFSVNSKKHSVNINAKTAANNGLSHLQNFS 550
551 EELLKKRKLFSSLFSNNVSYKKSKKLKRTHTVHDKCQKVAKLDHYIRDNI 600
601 ELNSKEREHDCYEGTLAVPQVNTEIRKSSRKQKAQRFIRDDFDTVFFQSS 650
651 SNPNYFTDVNPFWNIGIWSTTFNVITFRPGLSLPNNSFIKTQGLNSILQL 700
701 DIVTHPFKSVYAFSCLFNIQDDVFKTFEKLKDTFETVLENLPYFTNSETV 750
751 DLYNLLSFCSAFILHSQVSMGLVNLASSFLETYALVNDRVSSISGLNRSQ 800
801 LVEKIAVLFQTFQVVFYCEFELGNQQNINKVSWLASDLISKLLSAGQSGL 850
851 LECYRNLRIQASDTTVIDTLFLESWSILNHILFHVYKKKYALWEQVNSFF 900
901 DLQKKELSILEMEKIWYVIMTLNPVFQIGLNGTTHSPGNNSFWPLIIRVS 950
951 ESAFKMHKDGHNVKVVERYLRTVFLRIHFLISEWRWEDVAQILFLIFDFF 1000
1001 SHRKFNDLSSEISEDTPTDFPDFVKSLDRPPNLHVTALDTCFVIYLKVIL 1050
1051 ISISRLRQVDENTNSIKRIVSRLQPLHSRQYTRESPFSIKDFMSLEHTHT 1100
1101 LLICLYWAAPENCRPSLNRIRDIVIVDNSHLKARLISLKAWLHLMKYVIK 1150
1151 EGTDYELAQGMEWFNSILKVTFDEYLALFSNGTSVGEMQLAEYSKHQLEN 1200
1201 ALIVAFHSLQDLIPNSSVYISRINVLVTEQSCRRILKDSHFFPPRVTLEC 1250
1251 ILFLKKFLQYQSNTEPPKVTVVGSTSHDSQDAYFDSDVLDDNTLILEQEK 1300
1301 FERKYEVAQILRTFVSPFLYQTISYLVGNDEDKENYIRILLLPLMECMAI 1350
1351 CASFAVEAKINDWSYYIDFGSESWERIRNTPLKRSLSTTFYSFLISYNDS 1400
1401 FIKKHEEKVLTVWFESLGALDEDHAAQFTILLLQKNLKNPILLNLPISVK 1450
1451 IEEISINYFKSVHLNLLTAVFCNMAKLYADAKTNGFASSQYLQSLFIHYL 1500
1501 SSLLSSMQHSYETNGHSSDTHSLFVINSQRVVGDILQYCSQFANDRNLPA 1550
1551 LRYFMDSTKFPQPPERLEYTALRLRSYARKTLTNSASQNALFSFLKANFD 1600
1601 VALLEQKQTETSNLLRLAMGFHNQNSSSKWDVEISSLRKFCVKELLLSYL 1650
1651 GEGSLAAMFYSLLLLEGLSRTYNSFRRIYLQPYIQQLLCEEISVFVADLE 1700
1701 KYALLYENKRPLLSGRIYYLISSFVAISMTNKTSGLPSSLICNLQNFLAR 1750
1751 IARDFLLELCWNIEGSEFPPPYNTVDCRSKFVKTIQQHCSADWYDNVTNI 1800
1801 VHKSRKKKLVLPSQVEQEEEYWSGLMEVTIQIWKHDKGFLEPELDRFLNV 1850
1851 ICSYAPDMSGLPTKIKCFLDKIGYSWMTEENDFDIVLF 1888
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.) |
Go back to the NucPred Home Page.