SBC logo Authors: Amine Heddad, Andrea Krings, Markus Brameier and Bob MacCallum, Stockholm Bioinformatics Center, Stockholm University, Sweden.

NucPred

Fetching O60673 from www.uniprot.org...

The NucPred score for your sequence is 1.00 (see score help below)

   1  MFSVRIVTADYYMASPLQGLDTCQSPLTQAPVKKVPVVRVFGATPAGQKT    50
  51  CLHLHGIFPYLYVPYDGYGQQPESYLSQMAFSIDRALNVALGNPSSTAQH   100
 101  VFKVSLVSGMPFYGYHEKERHFMKIYLYNPTMVKRICELLQSGAIMNKFY   150
 151  QPHEAHIPYLLQLFIDYNLYGMNLINLAAVKFRKARRKSNTLHATGSCKN   200
 201  HLSGNSLADTLFRWEQDEIPSSLILEGVEPQSTCELEVDAVAADILNRLD   250
 251  IEAQIGGNPGLQAIWEDEKQRRRNRNETSQMSQPESQDHRFVPATESEKK   300
 301  FQKRLQEILKQNDFSVTLSGSVDYSDGSQEFSAELTLHSEVLSPEMLQCT   350
 351  PANMVEVHKDKESSKGHTRHKVEEALINEEAILNLMENSQTFQPLTQRLS   400
 401  ESPVFMDSSPDEALVHLLAGLESDGYRGERNRMPSPCRSFGNNKYPQNSD   450
 451  DEENEPQIEKEEMELSLVMSQRWDSNIEEHCAKKRSLCRNTHRSSTEDDD   500
 501  SSSGEEMEWSDNSLLLASLSIPQLDGTADENSDNPLNNENSRTHSSVIAT   550
 551  SKLSVKPSIFHKDAATLEPSSSAKITFQCKHTSALSSHVLNKEDLIEDLS   600
 601  QTNKNTEKGLDNSVTSFTNESTYSMKYPGSLSSTVHSENSHKENSKKEIL   650
 651  PVSSCESSIFDYEEDIPSVTRQVPSRKYTNIRKIEKDSPFIHMHRHPNEN   700
 701  TLGKNSFNFSDLNHSKNKVSSEGNEKGNSTALSSLFPSSFTENCELLSCS   750
 751  GENRTMVHSLNSTADESGLNKLKIRYEEFQEHKTEKPSLSQQAAHYMFFP   800
 801  SVVLSNCLTRPQKLSPVTYKLQPGNKPSRLKLNKRKLAGHQETSTKSSET   850
 851  GSTKDNFIQNNPCNSNPEKDNALASDLTKTTRGAFENKTPTDGFIDCHFG   900
 901  DGTLETEQSFGLYGNKYTLRAKRKVNYETEDSESSFVTHNSKISLPHPME   950
 951  IGESLDGTLKSRKRRKMSKKLPPVIIKYIIINRFRGRKNMLVKLGKIDSK  1000
1001  EKQVILTEEKMELYKKLAPLKDFWPKVPDSPATKYPIYPLTPKKSHRRKS  1050
1051  KHKSAKKKTGKQQRTNNENIKRTLSFRKKRSHAILSPPSPSYNAETEDCD  1100
1101  LNYSDVMSKLGFLSERSTSPINSSPPRCWSPTDPRAEEIMAAAEKEAMLF  1150
1151  KGPNVYKKTVNSRIGKTSRARAQIKKSKAKLANPSIVTKKRNKRNQTNKL  1200
1201  VDDGKKKPRAKQKTNEKGTSRKHTTLKDEKIKSQSGAEVKFVLKHQNVSE  1250
1251  FASSSGGSQLLFKQKDMPLMGSAVDHPLSASLPTGINAQQKLSGCFSSFL  1300
1301  ESKKSVDLQTFPSSRDDLHPSVVCNSIGPGVSKINVQRPHNQSAMFTLKE  1350
1351  STLIQKNIFDLSNHLSQVAQNTQISSGMSSKIEDNANNIQRNYLSSIGKL  1400
1401  SEYRNSLESKLDQAYTPNFLHCKDSQQQIVCIAEQSKHSETCSPGNTASE  1450
1451  ESQMPNNCFVTSLRSPIKQIAWEQKQRGFILDMSNFKPERVKPRSLSEAI  1500
1501  SQTKALSQCKNRNVSTPSAFGEGQSGLAVLKELLQKRQQKAQNANTTQDP  1550
1551  LSNKHQPNKNISGSLEHNKANKRTRSVTSPRKPRTPRSTKQKEKIPKLLK  1600
1601  VDSLNLQNSSQLDNSVSDDSPIFFSDPGFESCYSLEDSLSPEHNYNFDIN  1650
1651  TIGQTGFCSFYSGSQFVPADQNLPQKFLSDAVQDLFPGQAIEKNEFLSHD  1700
1701  NQKCDEDKHHTTDSASWIRSGTLSPEIFEKSTIDSNENRRHNQWKNSFHP  1750
1751  LTTRSNSIMDSFCVQQAEDCLSEKSRLNRSSVSKEVFLSLPQPNNSDWIQ  1800
1801  GHTRKEMGQSLDSANTSFTAILSSPDGELVDVACEDLELYVSRNNDMLTP  1850
1851  TPDSSPRSTSSPSQSKNGSFTPRTANILKPLMSPPSREEIMATLLDHDLS  1900
1901  ETIYQEPFCSNPSDVPEKPREIGGRLLMVETRLANDLAEFEGDFSLEGLR  1950
1951  LWKTAFSAMTQNPRPGSPLRSGQGVVNKGSSNSPKMVEDKKIVIMPCKCA  2000
2001  PSRQLVQVWLQAKEEYERSKKLPKTKPTGVVKSAENFSSSVNPDDKPVVP  2050
2051  PKMDVSPCILPTTAHTKEDVDNSQIALQAPTTGCSQTASESQMLPPVASA  2100
2101  SDPEKDEDDDDNYYISYSSPDSPVIPPWQQPISPDSKALNGDDRPSSPVE  2150
2151  ELPSLAFENFLKPIKDGIQKSPCSEPQEPLVISPINTRARTGKCESLCFH  2200
2201  STPIIQRKLLERLPEAPGLSPLSTEPKTQKLSNKKGSNTDTLRRVLLTQA  2250
2251  KNQFAAVNTPQKETSQIDGPSLNNTYGFKVSIQNLQEAKALHEIQNLTLI  2300
2301  SVELHARTRRDLEPDPEFDPICALFYCISSDTPLPDTEKTELTGVIVIDK  2350
2351  DKTVFSQDIRYQTPLLIRSGITGLEVTYAADEKALFHEIANIIKRYDPDI  2400
2401  LLGYEIQMHSWGYLLQRAAALSIDLCRMISRVPDDKIENRFAAERDEYGS  2450
2451  YTMSEINIVGRITLNLWRIMRNEVALTNYTFENVSFHVLHQRFPLFTFRV  2500
2501  LSDWFDNKTDLYRWKMVDHYVSRVRGNLQMLEQLDLIGKTSEMARLFGIQ  2550
2551  FLHVLTRGSQYRVESMMLRIAKPMNYIPVTPSVQQRSQMRAPQCVPLIME  2600
2601  PESRFYSNSVLVLDFQSLYPSIVIAYNYCFSTCLGHVENLGKYDEFKFGC  2650
2651  TSLRVPPDLLYQVRHDITVSPNGVAFVKPSVRKGVLPRMLEEILKTRFMV  2700
2701  KQSMKAYKQDRALSRMLDARQLGLKLIANVTFGYTSANFSGRMPCIEVGD  2750
2751  SIVHKARETLERAIKLVNDTKKWGARVVYGDTDSMFVLLKGATKEQSFKI  2800
2801  GQEIAEAVTATNPKPVKLKFEKVYLPCVLQTKKRYVGYMYETLDQKDPVF  2850
2851  DAKGIETVRRDSCPAVSKILERSLKLLFETRDISLIKQYVQRQCMKLLEG  2900
2901  KASIQDFIFAKEYRGSFSYKPGACVPALELTRKMLTYDRRSEPQVGERVP  2950
2951  YVIIYGTPGVPLIQLVRRPVEVLQDPTLRLNATYYITKQILPPLARIFSL  3000
3001  IGIDVFSWYHELPRIHKATSSSRSEPEGRKGTISQYFTTLHCPVCDDLTQ  3050
3051  HGICSKCRSQPQHVAVILNQEIRELERQQEQLVKICKNCTGCFDRHIPCV  3100
3101  SLNCPVLFKLSRVNRELSKAPYLRQLLDQF                      3130

Positively and negatively influencing subsequences are coloured according to the following scale:

(non-nuclear) negative ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||| positive (nuclear)

with NucPred



If you find NucPred useful, please cite this paper:
NucPred - Predicting Nuclear Localization of Proteins. Brameier M, Krings A, Maccallum RM. Bioinformatics, 2007. PubMed id: 17332022
The authors also look forward to your comments and suggestions.

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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