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

NucPred

Fetching Q12830 from www.uniprot.org...

The NucPred score for your sequence is 0.99 (see score help below)

   1  MRGRRGRPPKQPAAPAAERCAPAPPPPPPPPTSGPIGGLRSRHRGSSRGR    50
  51  WAAAQAEVAPKTRLSSPRGGSSSRRKPPPPPPAPPSTSAPGRGGRGGGGG   100
 101  RTGGGGGGGHLARTTAARRAVNKVVYDDHESEEEEEEEDMVSEEEEEEDG   150
 151  DAEETQDSEDDEEDEMEEDDDDSDYPEEMEDDDDDASYCTESSFRSHSTY   200
 201  SSTPGRRKPRVHRPRSPILEEKDIPPLEFPKSSEDLMVPNEHIMNVIAIY   250
 251  EVLRNFGTVLRLSPFRFEDFCAALVSQEQCTLMAEMHVVLLKAVLREEDT   300
 301  SNTTFGPADLKDSVNSTLYFIDGMTWPEVLRVYCESDKEYHHVLPYQEAE   350
 351  DYPYGPVENKIKVLQFLVDQFLTTNIAREELMSEGVIQYDDHCRVCHKLG   400
 401  DLLCCETCSAVYHLECVKPPLEEVPEDEWQCEVCVAHKVPGVTDCVAEIQ   450
 451  KNKPYIRHEPIGYDRSRRKYWFLNRRLIIEEDTENENEKKIWYYSTKVQL   500
 501  AELIDCLDKDYWEAELCKILEEMREEIHRHMDITEDLTNKARGSNKSFLA   550
 551  AANEEILESIRAKKGDIDNVKSPEETEKDKNETENDSKDAEKNREEFEDQ   600
 601  SLEKDSDDKTPDDDPEQGKSEEPTEVGDKGNSVSANLGDNTTNATSEETS   650
 651  PSEGRSPVGCLSETPDSSNMAEKKVASELPQDVPEEPNKTCESSNTSATT   700
 701  TSIQPNLENSNSSSELNSSQSESAKAADDPENGERESHTPVSIQEEIVGD   750
 751  FKSEKSNGELSESPGAGKGASGSTRIITRLRNPDSKLSQLKSQQVAAAAH   800
 801  EANKLFKEGKEVLVVNSQGEISRLSTKKEVIMKGNINNYFKLGQEGKYRV   850
 851  YHNQYSTNSFALNKHQHREDHDKRRHLAHKFCLTPAGEFKWNGSVHGSKV   900
 901  LTISTLRLTITQLENNIPSSFLHPNWASHRANWIKAVQMCSKPREFALAL   950
 951  AILECAVKPVVMLPIWRESLGHTRLHRMTSIEREEKEKVKKKEKKQEEEE  1000
1001  TMQQATWVKYTFPVKHQVWKQKGEEYRVTGYGGWSWISKTHVYRFVPKLP  1050
1051  GNTNVNYRKSLEGTKNNMDENMDESDKRKCSRSPKKIKIEPDSEKDEVKG  1100
1101  SDAAKGADQNEMDISKITEKKDQDVKELLDSDSDKPCKEEPMEVDDDMKT  1150
1151  ESHVNCQESSQVDVVNVSEGFHLRTSYKKKTKSSKLDGLLERRIKQFTLE  1200
1201  EKQRLEKIKLEGGIKGIGKTSTNSSKNLSESPVITKAKEGCQSDSMRQEQ  1250
1251  SPNANNDQPEDLIQGCSESDSSVLRMSDPSHTTNKLYPKDRVLDDVSIRS  1300
1301  PETKCPKQNSIENDIEEKVSDLASRGQEPSKSKTKGNDFFIDDSKLASAD  1350
1351  DIGTLICKNKKPLIQEESDTIVSSSKSALHSSVPKSTNDRDATPLSRAMD  1400
1401  FEGKLGCDSESNSTLENSSDTVSIQDSSEEDMIVQNSNESISEQFRTREQ  1450
1451  DVEVLEPLKCELVSGESTGNCEDRLPVKGTEANGKKPSQQKKLEERPVNK  1500
1501  CSDQIKLKNTTDKKNNENRESEKKGQRTSTFQINGKDNKPKIYLKGECLK  1550
1551  EISESRVVSGNVEPKVNNINKIIPENDIKSLTVKESAIRPFINGDVIMED  1600
1601  FNERNSSETKSHLLSSSDAEGNYRDSLETLPSTKESDSTQTTTPSASCPE  1650
1651  SNSVNQVEDMEIETSEVKKVTSSPITSEEESNLSNDFIDENGLPINKNEN  1700
1701  VNGESKRKTVITEVTTMTSTVATESKTVIKVEKGDKQTVVSSTENCAKST  1750
1751  VTTTTTTVTKLSTPSTGGSVDIISVKEQSKTVVTTTVTDSLTTTGGTLVT  1800
1801  SMTVSKEYSTRDKVKLMKFSRPKKTRSGTALPSYRKFVTKSSKKSIFVLP  1850
1851  NDDLKKLARKGGIREVPYFNYNAKPALDIWPYPSPRPTFGITWRYRLQTV  1900
1901  KSLAGVSLMLRLLWASLRWDDMAAKAPPGGGTTRTETSETEITTTEIIKR  1950
1951  RDVGPYGIRSEYCIRKIICPIGVPETPKETPTPQRKGLRSSALRPKRPET  2000
2001  PKQTGPVIIETWVAEEELELWEIRAFAERVEKEKAQAVEQQAKKRLEQQK  2050
2051  PTVIATSTTSPTSSTTSTISPAQKVMVAPISGSVTTGTKMVLTTKVGSPA  2100
2101  TVTFQQNKNFHQTFATWVKQGQSNSGVVQVQQKVLGIIPSSTGTSQQTFT  2150
2151  SFQPRTATVTIRPNTSGSGGTTSNSQVITGPQIRPGMTVIRTPLQQSTLG  2200
2201  KAIIRTPVMVQPGAPQQVMTQIIRGQPVSTAVSAPNTVSSTPGQKSLTSA  2250
2251  TSTSNIQSSASQPPRPQQGQVKLTMAQLTQLTQGHGGNQGLTVVIQGQGQ  2300
2301  TTGQLQLIPQGVTVLPGPGQQLMQAAMPNGTVQRFLFTPLATTATTASTT  2350
2351  TTTVSTTAAGTGEQRQSKLSPQMQVHQDKTLPPAQSSSVGPAEAQPQTAQ  2400
2401  PSAQPQPQTQPQSPAQPEVQTQPEVQTQTTVSSHVPSEAQPTHAQSSKPQ  2450
2451  VAAQSQPQSNVQGQSPVRVQSPSQTRIRPSTPSQLSPGQQSQVQTTTSQP  2500
2501  IPIQPHTSLQIPSQGQPQSQPQVQSSTQTLSSGQTLNQVTVSSPSRPQLQ  2550
2551  IQQPQPQVIAVPQLQQQVQVLSQIQSQVVAQIQAQQSGVPQQIKLQLPIQ  2600
2601  IQQSSAVQTHQIQNVVTVQAASVQEQLQRVQQLRDQQQKKKQQQIEIKRE  2650
2651  HTLQASNQSEIIQKQVVMKHNAVIEHLKQKKSMTPAEREENQRMIVCNQV  2700
2701  MKYILDKIDKEEKQAAKKRKREESVEQKRSKQNATKLSALLFKHKEQLRA  2750
2751  EILKKRALLDKDLQIEVQEELKRDLKIKKEKDLMQLAQATAVAAPCPPVT  2800
2801  PAPPAPPAPPPSPPPPPAVQHTGLLSTPTLPAASQKRKREEEKDSSSKSK  2850
2851  KKKMISTTSKETKKDTKLYCICKTPYDESKFYIGCDRCQNWYHGRCVGIL  2900
2901  QSEAELIDEYVCPQCQSTEDAMTVLTPLTEKDYEGLKRVLRSLQAHKMAW  2950
2951  PFLEPVDPNDAPDYYGVIKEPMDLATMEERVQRRYYEKLTEFVADMTKIF  3000
3001  DNCRYYNPSDSPFYQCAEVLESFFVQKLKGFKASRSHNNKLQSTAS      3046

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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