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

NucPred

Fetching Q61493 from www.uniprot.org...

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

   1  MFSVRIVTADYYMASPLPGLDTCQSPLTQLPVKKVPVVRVFGATPAGQKT    50
  51  CLHLHGIFPYLYVPYDGYGQQPESYLSQMAFSIDRALNVALGNPSSTAQH   100
 101  VFKVSLVSGMPFYGYHEKERHFMKIYLYNPAMVKRICELLQSGAIMNKCY   150
 151  QPHEAHIPYLLQLFIDYNLYGMNLINLAAVKFRKARRKGNASHATGLFKH   200
 201  QLSGNSPAGTLFRWEEDEIPSSLLLEGVEPLSTCELEVDAVAADILNRLD   250
 251  IEAQIGGNPGLQAIWEDEKQRRRNRNESSQISQPESQDCRFVPATESEKQ   300
 301  FQKRLQEVLKQNDFSVTLSGSVDYSNGSQEFSAELTLHSEILSPEMLPCS   350
 351  PANMIEVHKDTDLSKGNTKHKVEEALINEEAILNLIENSQTFQPLTQRLS   400
 401  ETPVFMGSSPDESLVHLLAGLESDGYQGEKNRMPLPCHSFGESQNPQNSD   450
 451  DEENEPQIEKEEMELSVVMSQRWDSDIEEHCAKKRSLCRNAHRSSTEEDD   500
 501  SSSEEEMEWTDNSLLFANLSIPQLDGTADENSDNPLNNENSRAHSSVIAT   550
 551  SKLSVRPSIFHKDAATLEPPSSAKITFQCKHTSALSSHVLNKDGLTEDLS   600
 601  QPNSTEKGRDNSVTFTKESTYSMKYSGSLSSTVHSDNSHKEICKKDKSLP   650
 651  VSSCESSVFDYEEDIPSVTRQVPSRKYSNMRKIEKDASCIHVNRHISETI   700
 701  LGKNSFNFADLNHSKRKLSSEGNEKGNSTSLSGVFPSSLTENCDLLPSSG   750
 751  ENRSMAHSLESITDESGLNKLKIRYEEFQEHKMEKPSLSQQAAHYMFFPS   800
 801  VVLSNCLTRPQKLSPVTYKLQSGNKPSRLKLNKKKLIGLQETSTKSTETG   850
 851  ATKDSCTHNDLYTGASEKENGLSSDSAKATHGTFENKPPTEHFIDCHFGD   900
 901  GSLEAEQSFGLYGNKYTLRAKRKVNYETEDSESSFVTQNSKISLPHPMEI   950
 951  GENLDGTLKSRKRRKMSKKLPPVIIKYIIINRFRGRKNMLVKLGKIDSKE  1000
1001  KQVILTEEKMELYKKLAPLKDFWPKVPDSPATKYPIYPLTPKKSHRRKSK  1050
1051  HKSAKKKPGKQHRTNSENIKRTLSFRKKRTHAVLSPPSPSYIAETEDCDL  1100
1101  SYSDVMSKLGFLSERSTSPINSSPPRCWSPTDPRAEEIMAAAEKESMLFK  1150
1151  GPNVYNTKTVSPRVGKASRARAQVKKSKARLANSSVVTNKRNKRNQTTKL  1200
1201  VDDGKKKPRAKQKQRANEKSLSRKHAIPADEKMKPHSEAELTPNHQSVSE  1250
1251  LTSSSGAQALSKQKEMSQTGPAVDHPLPPAQPTGISAQQRLSNCFSSFLE  1300
1301  SKKSVDLRTFPSSRDDSHSSVVYSSIGPGISKINIQRSHNQSAMFTRKET  1350
1351  TLIQKSIFDLSNHLSQVAQSTQVCSGIISPKTEESSSTQKNCGSSMGKLN  1400
1401  EYRSSLESKPEQVCAPNFLHCKDSQQQTVSVSEQSKTSETCSPGNAASEE  1450
1451  SQTPNCFVTSLKSPIKQIAWEQKQRGFILDMSNFKPEKVKQRSLSEAISQ  1500
1501  TKALSQCKNQNVSTPSVFGEGQSGLAVLKELLQKRQQKAQSTNVVQDSTS  1550
1551  THQPDKNISVSNEHKKANKRTRPVTSPRKPRTPRRTKPKEQTPRRLKVDP  1600
1601  LNLQTSGHLDNSLSDDSPILFSDPGFESCYSLEDSLSPEHNYNFDINTIG  1650
1651  QTGFCSFYSGSQFVPADQNLPQKFLSDAVQDLFPGQAIDKSELLSHDRQS  1700
1701  CSEEKHHVSDSSPWIRASTLNPELFEKVAMDNNENHRHSQWKNSFHPLTS  1750
1751  HSNSIMESFCVQQAENCLTEKSRLNRSSVSKEVFLSLPQANSSDWIQGHN  1800
1801  RKEADQSLHSANTSFTTILSSPDGELVDAASEDLELYVSRNNDVLTPTPD  1850
1851  SSPRSTSSPLQSKNGSFTPRTAHILKPLMSPPSREEIVATLLDHDLSEAI  1900
1901  YQEPFCSNPSDVPEKPREIGGRLLMVETRLPNDLIEFEGDFSLEGLRLWK  1950
1951  TAFSAMTQNPRPGSPLRNGQAVVNKESSNSHKMVEDKKIVIMPCKYAPSR  2000
2001  QLVQAWLQAKEEYERSKKLPKTELTPVTKSAENVSPSLNPGDTCAVSPQV  2050
2051  DKCPHTLSSSAHTKEEVSKSQIALQTSTTGCSQTLLAAASAAVPEEDEDD  2100
2101  NDNCYVSYSSPDSPGIPPWQQAASPDFRSLNGDDRHSSPGKELCSLAVEN  2150
2151  FLKPIKDGIQKSSCSESWEPQVISPIHARARTGKWDPLCLHSTPVMQRKF  2200
2201  LEKLPEATGLSPLSVEPKTQKLYNKKGSDADGLRRVLLTTQVENQFAAVN  2250
2251  TPKKETSQIDGPSLNNTYGFKVSIQNLQEAKALHEIQNLTLISVELHART  2300
2301  RRDLQPDPEFDPICALFYCISSDTPLPDTEKTELTGVIVIDKDKTVTHQD  2350
2351  IRSQTPLLIRSGITGLEVTYAADEKALFQEITNIIKRYDPDILLGYEIQM  2400
2401  HSWGYLLQRAAALSVDLCQMISRVPDDKIENRFAAERDDYGSDTMSEINI  2450
2451  VGRITLNLWRIMRNEVALTNYTFENVSFHVLHQRFPLFTFRVLSDWFDNK  2500
2501  TDLYRWKMVDHYVSRVRGNLQMLEQLDLIGKTSEMARLFGIQFLHVLTRG  2550
2551  SQYRVESMMLRIAKPMNYIPVTPSIQQRSQMRAPQCVPLIMEPESRFYSN  2600
2601  SVLVLDFQSLYPSIVIAYNYCFSTCLGHVENLGKYDEFKFGCTSLRVPPD  2650
2651  LLYQIRHDVTVSPNGVAFVKPSVRKGVLPRMLEEILKTRLMVKQSMKSYK  2700
2701  QDRALSRMLNARQLGLKLIANVTFGYTAANFSGRMPCIEVGDSIVHKARE  2750
2751  TLERAIKLVNDTKKWGARVVYGDTDSMFVLLKGATKEQSFKIGQEIAEAV  2800
2801  TATNPRPVKLKFEKVYLPCVLQTKKRYVGYMYETLDQKEPVFDAKGIETV  2850
2851  RRDSCPAVSKILERSLKLLFETRDISLIKQYVQRQCMKLVEGKASIQDFI  2900
2901  FAKEYRGSFSYRPGACVPALELTRKMLAYDRRSEPRVGERVPYVIIYGTP  2950
2951  GLPLIQLIRRPAEVLQDPTLRLNATYYITKQILPPLARIFSLIGIDVFSW  3000
3001  YQELPRIQKATSSSRSELEGRKGTISQYFTTLHCPVCDDLTQHGICSKCR  3050
3051  SQPQHVAIILNQEIRELERKQEQLIKICRNCTGSFDRHIPCVSLNCPVLF  3100
3101  KLSRVNRELSKAPYLRQLLDQF                              3122

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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