 | Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden. |
NucPred
Fetching P46939 from www.uniprot.org...
The NucPred score for your sequence is 0.92 (see score help below)
1 MAKYGEHEASPDNGQNEFSDIIKSRSDEHNDVQKKTFTKWINARFSKSGK 50
51 PPINDMFTDLKDGRKLLDLLEGLTGTSLPKERGSTRVHALNNVNRVLQVL 100
101 HQNNVELVNIGGTDIVDGNHKLTLGLLWSIILHWQVKDVMKDVMSDLQQT 150
151 NSEKILLSWVRQTTRPYSQVNVLNFTTSWTDGLAFNAVLHRHKPDLFSWD 200
201 KVVKMSPIERLEHAFSKAQTYLGIEKLLDPEDVAVQLPDKKSIIMYLTSL 250
251 FEVLPQQVTIDAIREVETLPRKYKKECEEEAINIQSTAPEEEHESPRAET 300
301 PSTVTEVDMDLDSYQIALEEVLTWLLSAEDTFQEQDDISDDVEEVKDQFA 350
351 THEAFMMELTAHQSSVGSVLQAGNQLITQGTLSDEEEFEIQEQMTLLNAR 400
401 WEALRVESMDRQSRLHDVLMELQKKQLQQLSAWLTLTEERIQKMETCPLD 450
451 DDVKSLQKLLEEHKSLQSDLEAEQVKVNSLTHMVVIVDENSGESATAILE 500
501 DQLQKLGERWTAVCRWTEERWNRLQEINILWQELLEEQCLLKAWLTEKEE 550
551 ALNKVQTSNFKDQKELSVSVRRLAILKEDMEMKRQTLDQLSEIGQDVGQL 600
601 LDNSKASKKINSDSEELTQRWDSLVQRLEDSSNQVTQAVAKLGMSQIPQK 650
651 DLLETVRVREQAITKKSKQELPPPPPPKKRQIHVDIEAKKKFDAISAELL 700
701 NWILKWKTAIQTTEIKEYMKMQDTSEMKKKLKALEKEQRERIPRADELNQ 750
751 TGQILVEQMGKEGLPTEEIKNVLEKVSSEWKNVSQHLEDLERKIQLQEDI 800
801 NAYFKQLDELEKVIKTKEEWVKHTSISESSRQSLPSLKDSCQRELTNLLG 850
851 LHPKIEMARASCSALMSQPSAPDFVQRGFDSFLGRYQAVQEAVEDRQQHL 900
901 ENELKGQPGHAYLETLKTLKDVLNDSENKAQVSLNVLNDLAKVEKALQEK 950
951 KTLDEILENQKPALHKLAEETKALEKNVHPDVEKLYKQEFDDVQGKWNKL 1000
1001 KVLVSKDLHLLEEIALTLRAFEADSTVIEKWMDGVKDFLMKQQAAQGDDA 1050
1051 GLQRQLDQCSAFVNEIETIESSLKNMKEIETNLRSGPVAGIKTWVQTRLG 1100
1101 DYQTQLEKLSKEIATQKSRLSESQEKAANLKKDLAEMQEWMTQAEEEYLE 1150
1151 RDFEYKSPEELESAVEEMKRAKEDVLQKEVRVKILKDNIKLLAAKVPSGG 1200
1201 QELTSELNVVLENYQLLCNRIRGKCHTLEEVWSCWIELLHYLDLETTWLN 1250
1251 TLEERMKSTEVLPEKTDAVNEALESLESVLRHPADNRTQIRELGQTLIDG 1300
1301 GILDDIISEKLEAFNSRYEDLSHLAESKQISLEKQLQVLRETDQMLQVLQ 1350
1351 ESLGELDKQLTTYLTDRIDAFQVPQEAQKIQAEISAHELTLEELRRNMRS 1400
1401 QPLTSPESRTARGGSQMDVLQRKLREVSTKFQLFQKPANFEQRMLDCKRV 1450
1451 LDGVKAELHVLDVKDVDPDVIQTHLDKCMKLYKTLSEVKLEVETVIKTGR 1500
1501 HIVQKQQTDNPKGMDEQLTSLKVLYNDLGAQVTEGKQDLERASQLARKMK 1550
1551 KEAASLSEWLSATETELVQKSTSEGLLGDLDTEISWAKNVLKDLEKRKAD 1600
1601 LNTITESSAALQNLIEGSEPILEERLCVLNAGWSRVRTWTEDWCNTLMNH 1650
1651 QNQLEIFDGNVAHISTWLYQAEALLDEIEKKPTSKQEEIVKRLVSELDDA 1700
1701 NLQVENVRDQALILMNARGSSSRELVEPKLAELNRNFEKVSQHIKSAKLL 1750
1751 IAQEPLYQCLVTTETFETGVPFSDLEKLENDIENMLKFVEKHLESSDEDE 1800
1801 KMDEESAQIEEVLQRGEEMLHQPMEDNKKEKIRLQLLLLHTRYNKIKAIP 1850
1851 IQQRKMGQLASGIRSSLLPTDYLVEINKILLCMDDVELSLNVPELNTAIY 1900
1901 EDFSFQEDSLKNIKDQLDKLGEQIAVIHEKQPDVILEASGPEAIQIRDTL 1950
1951 TQLNAKWDRINRMYSDRKGCFDRAMEEWRQFHCDLNDLTQWITEAEELLV 2000
2001 DTCAPGGSLDLEKARIHQQELEVGISSHQPSFAALNRTGDGIVQKLSQAD 2050
2051 GSFLKEKLAGLNQRWDAIVAEVKDRQPRLKGESKQVMKYRHQLDEIICWL 2100
2101 TKAEHAMQKRSTTELGENLQELRDLTQEMEVHAEKLKWLNRTELEMLSDK 2150
2151 SLSLPERDKISESLRTVNMTWNKICREVPTTLKECIQEPSSVSQTRIAAH 2200
2201 PNVQKVVLVSSASDIPVQSHRTSEISIPADLDKTITELADWLVLIDQMLK 2250
2251 SNIVTVGDVEEINKTVSRMKITKADLEQRHPQLDYVFTLAQNLKNKASSS 2300
2301 DMRTAITEKLERVKNQWDGTQHGVELRQQQLEDMIIDSLQWDDHREETEE 2350
2351 LMRKYEARLYILQQARRDPLTKQISDNQILLQELGPGDGIVMAFDNVLQK 2400
2401 LLEEYGSDDTRNVKETTEYLKTSWINLKQSIADRQNALEAEWRTVQASRR 2450
2451 DLENFLKWIQEAETTVNVLVDASHRENALQDSILARELKQQMQDIQAEID 2500
2501 AHNDIFKSIDGNRQKMVKALGNSEEATMLQHRLDDMNQRWNDLKAKSASI 2550
2551 RAHLEASAEKWNRLLMSLEELIKWLNMKDEELKKQMPIGGDVPALQLQYD 2600
2601 HCKALRRELKEKEYSVLNAVDQARVFLADQPIEAPEEPRRNLQSKTELTP 2650
2651 EERAQKIAKAMRKQSSEVKEKWESLNAVTSNWQKQVDKALEKLRDLQGAM 2700
2701 DDLDADMKEAESVRNGWKPVGDLLIDSLQDHIEKIMAFREEIAPINFKVK 2750
2751 TVNDLSSQLSPLDLHPSLKMSRQLDDLNMRWKLLQVSVDDRLKQLQEAHR 2800
2801 DFGPSSQHFLSTSVQLPWQRSISHNKVPYYINHQTQTTCWDHPKMTELFQ 2850
2851 SLADLNNVRFSAYRTAIKIRRLQKALCLDLLELSTTNEIFKQHKLNQNDQ 2900
2901 LLSVPDVINCLTTTYDGLEQMHKDLVNVPLCVDMCLNWLLNVYDTGRTGK 2950
2951 IRVQSLKIGLMSLSKGLLEEKYRYLFKEVAGPTEMCDQRQLGLLLHDAIQ 3000
3001 IPRQLGEVAAFGGSNIEPSVRSCFQQNNNKPEISVKEFIDWMHLEPQSMV 3050
3051 WLPVLHRVAAAETAKHQAKCNICKECPIVGFRYRSLKHFNYDVCQSCFFS 3100
3101 GRTAKGHKLHYPMVEYCIPTTSGEDVRDFTKVLKNKFRSKKYFAKHPRLG 3150
3151 YLPVQTVLEGDNLETPITLISMWPEHYDPSQSPQLFHDDTHSRIEQYATR 3200
3201 LAQMERTNGSFLTDSSSTTGSVEDEHALIQQYCQTLGGESPVSQPQSPAQ 3250
3251 ILKSVEREERGELERIIADLEEEQRNLQVEYEQLKDQHLRRGLPVGSPPE 3300
3301 SIISPHHTSEDSELIAEAKLLRQHKGRLEARMQILEDHNKQLESQLHRLR 3350
3351 QLLEQPESDSRINGVSPWASPQHSALSYSLDPDASGPQFHQAAGEDLLAP 3400
3401 PHDTSTDLTEVMEQIHSTFPSCCPNVPSRPQAM 3433
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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