 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching Q09475 from www.uniprot.org...
The NucPred score for your sequence is 0.73 (see score help below)
1 MSANAAWDDSDSENENVVVEEKPVILPRARQPSIAKSLEKVQILENSVAG 50
51 SEKNASDDDSDASSVMSDDLESLGETTEVLNFSLEQLSMMKKNLAQFPCS 100
101 YKNIISEFADVEIFLISIDSLIVECAAHSYHNWDVAGQSMVLNKQIDRFL 150
151 QQFVDIGGRFKLVVFSDLTTQFAKDTTLSFARSTALAHLANGPHARDLLF 200
201 FTNPTDPEWDKLLNDLTPSFLMISTDNVTQNVCASQEIDLTKQFETIAFD 250
251 VLTRSMSVVLLHSIKVNFVSVEAYYIQPLLVVAPDWQAFLAAHWDNNGTL 300
301 LRNHLSKEIKFNQFETPADLWVKVITDAKATGKGSDTFDTLAAAVILSSL 350
351 ICSRRGAHRIYYPTRTDAKRGLNVIRDRRLILNAAVVLLDKADHANSKLD 400
401 LSDLWDGRMVYSIFDELSANETVLPYRLQDDFAKYHQKAGLTVPLATDTN 450
451 EKLFDPIPEITDPLVDLPILYSVTSPMIKRFVPEIEKMTSQNAVEEGTVQ 500
501 DYADYLKDTSQWRLKPIEETYAQKEEKIEDAWQLKKANRSKQFLMRWYEL 550
551 FANSLEGRGSNLLVDFSRVPKGYAVVEEEVTDEKKTGKGGWSGQKQQKPG 600
601 AGGKKGATKESGKSKKDLILEANKKAKDQKVAESEKVKIKYGCQQGKESV 650
651 IFLNNLYSSLDLPETRALCVYEIAVREGRTIFDQHQGTDKQEVRRNAAID 700
701 LVGHLKDCFVKHWDHLESKQKEQIVDLWVSLGFEAPAGSKPSSEAKQKKL 750
751 NLGINMVYYQLQYGGELIDIQSDPKKDDRVSGFAPDGWQRKMLDSVDRGN 800
801 SALIIAPTSAGKTFVSYYCIEKVLRSSDNDVVVYVAPSKALINQVCGSVY 850
851 ARFRNKSMKRGISLFGTLTQEYSQNAMQCQVLITVPECLQELMLSRTPAV 900
901 QKFVSHIKYVVFDEVHSIGASEESHIWEQLLLLIQCPFLALSATIGNANK 950
951 LHEWLNSSEQAKSAGKRKVELINYGERYSELELSILNINDPHGEDDGAVH 1000
1001 KKAGERAVIPLMPYGVYMPEKLRMFSIPEDQQLTARQVLHLYNMMAEVDD 1050
1051 ATKKEFEPCKFFGQHGTKAVWISRSELRRLENALKERFMEWLSSDEQKIN 1100
1101 SILKILKEPVNTQLSYRARPFNKEKIANDYIVTLVDDLKEKGELPAICFN 1150
1151 DDRHVCEKLAVTLANELERRELEYMETDEFKNKYMIKDESKLVKLAKRKR 1200
1201 DDAEKKKKGDKDEDAGPEKDDDEMDVLAMKKAKLARALERFKLRGRNGGD 1250
1251 PDIYAKMVERMQKGAKTRESTQVLLKLFERGIGYHHAGLNTVERGAVEVL 1300
1301 FRSGNLAVLFSTSTLSLGVNMPCKTVMFGVDTLQLTPLLYRQMSGRAGRR 1350
1351 GFDHSGNVIFMSIPTSKVRRLLTASLSNLQGNPPFTVLFLLRLFAYVHQQ 1400
1401 DILNEEGQKVSTMKQRAFAAKSLLEHSFSIHTRREAQDGILQKQLRMFSA 1450
1451 FSFQLLRHLQLLSPFGEGKNFAEMAIHSSSGANGTLLFIYLMQKKCFHQL 1500
1501 IKSYDTAEQAQLGILEVLANLFTNLRMTPFHERSDNLENVQVTLRGLPSL 1550
1551 LKPYVEEYNSTVTGLYKRFMAASSQDGNLFDPSFAVSGKLDSESVSLTED 1600
1601 FLVAPLFDQYSHDESFLPVIDFNKKDHRGRKIQRNAFAYDFYVHGSRNML 1650
1651 MDVNGLHVSVAWFLLHDFAAILERLAIGVHNMARPQDPLVLVLEELHKNY 1700
1701 DEKFRKAFGMRTRD 1714
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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