 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q9UI33 from www.uniprot.org...
The NucPred score for your sequence is 0.74 (see score help below)
1 MDDRCYPVIFPDERNFRPFTSDSLAAIEKRIAIQKEKKKSKDQTGEVPQP 50
51 RPQLDLKASRKLPKLYGDIPRELIGKPLEDLDPFYRNHKTFMVLNRKRTI 100
101 YRFSAKHALFIFGPFNSIRSLAIRVSVHSLFSMFIIGTVIINCVFMATGP 150
151 AKNSNSNNTDIAECVFTGIYIFEALIKILARGFILDEFSFLRDPWNWLDS 200
201 IVIGIAIVSYIPGITIKLLPLRTFRVFRALKAISVVSRLKVIVGALLRSV 250
251 KKLVNVIILTFFCLSIFALVGQQLFMGSLNLKCISRDCKNISNPEAYDHC 300
301 FEKKENSPEFKMCGIWMGNSACSIQYECKHTKINPDYNYTNFDNFGWSFL 350
351 AMFRLMTQDSWEKLYQQTLRTTGLYSVFFFIVVIFLGSFYLINLTLAVVT 400
401 MAYEEQNKNVAAEIEAKEKMFQEAQQLLKEEKEALVAMGIDRSSLTSLET 450
451 SYFTPKKRKLFGNKKRKSFFLRESGKDQPPGSDSDEDCQKKPQLLEQTKR 500
501 LSQNLSLDHFDEHGDPLQRQRALSAVSILTITMKEQEKSQEPCLPCGENL 550
551 ASKYLVWNCCPQWLCVKKVLRTVMTDPFTELAITICIIINTVFLAMEHHK 600
601 MEASFEKMLNIGNLVFTSIFIAEMCLKIIALDPYHYFRRGWNIFDSIVAL 650
651 LSFADVMNCVLQKRSWPFLRSFRVLRVFKLAKSWPTLNTLIKIIGNSVGA 700
701 LGSLTVVLVIVIFIFSVVGMQLFGRSFNSQKSPKLCNPTGPTVSCLRHWH 750
751 MGDFWHSFLVVFRILCGEWIENMWECMQEANASSSLCVIVFILITVIGKL 800
801 VVLNLFIALLLNSFSNEERNGNLEGEARKTKVQLALDRFRRAFCFVRHTL 850
851 EHFCHKWCRKQNLPQQKEVAGGCAAQSKDIIPLVMEMKRGSETQEELGIL 900
901 TSVPKTLGVRHDWTWLAPLAEEEDDVEFSGEDNAQRITQPEPEQQAYELH 950
951 QENKKPTSQRVQSVEIDMFSEDEPHLTIQDPRKKSDVTSILSECSTIDLQ 1000
1001 DGFGWLPEMVPKKQPERCLPKGFGCCFPCCSVDKRKPPWVIWWNLRKTCY 1050
1051 QIVKHSWFESFIIFVILLSSGALIFEDVHLENQPKIQELLNCTDIIFTHI 1100
1101 FILEMVLKWVAFGFGKYFTSAWCCLDFIIVIVSVTTLINLMELKSFRTLR 1150
1151 ALRPLRALSQFEGMKVVVNALIGAIPAILNVLLVCLIFWLVFCILGVYFF 1200
1201 SGKFGKCINGTDSVINYTIITNKSQCESGNFSWINQKVNFDNVGNAYLAL 1250
1251 LQVATFKGWMDIIYAAVDSTEKEQQPEFESNSLGYIYFVVFIIFGSFFTL 1300
1301 NLFIGVIIDNFNQQQKKLGGQDIFMTEEQKKYYNAMKKLGSKKPQKPIPR 1350
1351 PLNKCQGLVFDIVTSQIFDIIIISLIILNMISMMAESYNQPKAMKSILDH 1400
1401 LNWVFVVIFTLECLIKIFALRQYYFTNGWNLFDCVVVLLSIVSTMISTLE 1450
1451 NQEHIPFPPTLFRIVRLARIGRILRLVRAARGIRTLLFALMMSLPSLFNI 1500
1501 GLLLFLIMFIYAILGMNWFSKVNPESGIDDIFNFKTFASSMLCLFQISTS 1550
1551 AGWDSLLSPMLRSKESCNSSSENCHLPGIATSYFVSYIIISFLIVVNMYI 1600
1601 AVILENFNTATEESEDPLGEDDFDIFYEVWEKFDPEATQFIKYSALSDFA 1650
1651 DALPEPLRVAKPNKYQFLVMDLPMVSEDRLHCMDILFAFTARVLGGSDGL 1700
1701 DSMKAMMEEKFMEANPLKKLYEPIVTTTKRKEEERGAAIIQKAFRKYMMK 1750
1751 VTKGDQGDQNDLENGPHSPLQTLCNGDLSSFGVAKGKVHCD 1791
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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