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

NucPred

Fetching P15989 from www.uniprot.org...

The NucPred score for your sequence is 0.44 (see score help below)

   1  MRKHRHLPLAAILGLLLSGFCSVGAQQQAAVRNVAVADIIFLVDSSWSIG    50
  51  KEHFQLVREFLYDVVKALDVGGNDFRFALVQFSGNPHTEFQLNTYPSNQD   100
 101  VLSHIANMPYMGGGSKTGKGLEYLIENHLTKAAGSRASEGVPQVIIVLTD   150
 151  GQSQDDVALPSSVLKSAHVNMIAVGVQDAVEGELKEIASRPFDTHLFNLE   200
 201  NFTALHGIVGDLVASVRTSMTPEQAGAKGLVKDITAQESADLIFLIDGSD   250
 251  NIGSVNFQAIRDFLVNLIESLRVGAQQIHIGVVQYSDQPRTEFALNSYST   300
 301  KADVLDAVKALSFRGGKEANTGAALEYVVENLFTQAGGSRIEEAVPQILV   350
 351  LISGGESSDDIREGLLAVKQASIFSFSIGVLNADSAELQQIATDGSFAFT   400
 401  ALDIRNLAALRELLLPNIVGVAQRLILLEAPTIVTEVIEVNKKDIVFLID   450
 451  GSTALGTGPFNSIRDFVAKIVQRLEVGPDLIQVAVAQYADTVRPEFYFNT   500
 501  HQNRKDVMANVKKMKLMGGTALNTGSALDFVRNNFFTSAAGCRMEEGVLP   550
 551  MLVLITGGKSMDAVEQAAAEMKRNRIVILAVGSRNADVAELQEIAHERDF   600
 601  VFQPNDFRLQFMQAILPEVLSPIRTLSGGMVIHETPSVQVTKRDIIFLLD   650
 651  GSLNVGNANFPFVRDFVVTLVNYLDVGTDKIRVGLVQFSDTPKTEFSLYS   700
 701  YQTKSDIIQRLGQLRPKGGSVLNTGSALNFVLSNHFTEAGGSRINEQVPQ   750
 751  VLVLVTAGRSAVPFLQVSNDLARAGVLTFAVGVRNADKAELEQIAFNPKM   800
 801  VYFMDDFSDLTTLPQELKKPITTIVSGGVEEVPLAPTESKKDILFLIDGS   850
 851  ANLLGSFPAVRDFIHKVISDLNVGPDATRVAVAQFSDNIQIEFDFAELPS   900
 901  KQDMLLKVKRMRLKTGKQLNIGVALDEVMRRLFVKEAGSRIEEGIPQFLV   950
 951  LLAAGRSTDEVERPSGALKEAGVVTFAIKAKNADLSELERIAYAPQFILN  1000
1001  VESLPRISELQANIVNLLKTIQFQPTVVERGEKKDVVFLIDGSDGVRRGF  1050
1051  PLLKTFVERVVESLDIGRDKVRVAIVQYSNAIQPEFLLDAYEDKADLVSA  1100
1101  IQALTIMGGSPLNTGAALDYLIKNVFTVSSGSRIAEGVPQFLILLTADRS  1150
1151  QDDVRRPSVVLKTSGTVPFGIGIGNADLTELQTISFLPDFAISVPDFSQL  1200
1201  DSVQQAVSNRVIRLTKKEIESLAPDLVFTSPSPVGVKRDVVFLVDGSRYA  1250
1251  AQEFYLIRDLIERIVNNLDVGFDTTRISVVQFSEHPHVEFLLNAHSTKDE  1300
1301  VQGAVRRLRPRGGQQVNVGEALEFVAKTIFTRPSGSRIEEGVPQFLVILS  1350
1351  SRKSDDDLEFPSVQVKQVGVAPMVIAKNMDPEEMVQISLSPDYVFQVSSF  1400
1401  QELPSLEQKLLAPIETLTADQIRQLLGDVTTIPDVSGEEKDVVFLIDSSD  1450
1451  SVRSDGLAHIRDFISRIVQQLDVGPNKVRIGVVQFSNNVFPEFYLRTHKS  1500
1501  KNAVLQAIRRLRLRGGYPVNAGKALDYVVKNYFIKSAGSRIEDGVPQHLV  1550
1551  VILGDQSQDDVNRPANVISSTSIQPLGVGARNVDRNQLQVITNDPGRVLV  1600
1601  VQDFTGLPTLERKVQNILEELTVPTTEGPVYPGPEGKKQADIVFLLDGSI  1650
1651  NLGRDNFQEVLQFVYSIVDAIYEDGDSIQVGLAQYNSDVTDEFFLKDYSS  1700
1701  KPEILDAINKVIYKGGRVANTGAAIKHLQAKHFVKEAGSRIDQRVPQIAF  1750
1751  IITGGKSSDDGQGASMEVAQKGVKVFAVGVRNIDLEEVSKLASESATSFR  1800
1801  VSTAQELSELNEQVLVTLAAAMKEKLCPGTTDVTRDCDLDVILGFDVSDV  1850
1851  GAGQNIFNSQRGLESRVEAVLNRITQMQKISCTGSRAPSVRVAIMAQSRG  1900
1901  GPVEGLDFSEYQPELFERFQGMRTRGPYFLTAETLKSYQNKFRSAPSGST  1950
1951  KVVIHFTDGTDDYLDQMKTASADLRRQGVHALLFVGLDRVKNFEEVMQLE  2000
2001  FGRGFTYNRPLRVNLLDLDFELAEQLDNIAERTCCGVPCKCSGQRGDRGL  2050
2051  PGPIGPKGATGEIGYGGYPGDEGGPGERGPPGVNGTQGFQGCPGHRGTKG  2100
2101  SRGFPGEKGELGEMGLDGIDGEEGDKGLPGFSGEKGFSGRRGSKGAKGER  2150
2151  GERGDRGLRGDPGESGADNTQRGTRGQKGEIGQMGEPGPAGQRGQDGGVG  2200
2201  RKGMAGRRGPIGVKGTKGALGQPGPAGEQGMRGPQGPPGQIGTPGIRGEQ  2250
2251  GIPGPRAGGGQPGAPGERGRIGPLGRKGEPGNPGPRGPNGQQGPRGEMGD  2300
2301  DGRDGIGGPGPKGRKGERGFVGYPGPKGGPGDRGGAGGPGPKGNRGRRGN  2350
2351  AGNPGTPGQKGEIGYPGPSGLKGDKGPSISQCALVQNIKDKCPCCYGPKE  2400
2401  CPVFPTELAFAIDTSSGVGRDVFNRMKQTVLRVVSNLTIAESNCPRGARV  2450
2451  ALVTYNNEVTTEIRFADARKKSSLLQQIQNFQATLTTKPRSLETAMSFVA  2500
2501  RNTFKRARSGFLMRKVAVFFSNGETRASPQLNDAVLKLYDAGVTPVFLTS  2550
2551  RQDAVLERALEINNTAVGHAIVLPTSGSQLNDTIRRLLTCHVCLDVCEPD  2600
2601  PICGYGSQRPVFRDRRAAPTDVDTDIAFIMDSSASTTPLQFNEMKKYISH  2650
2651  LVSNMEISSEPKISQHHARVAVLQQAPYDHETNSSFPPVKTEFSLTDYGS  2700
2701  KEKIINYLHNQMTQLYGTMAMGSAVEHTVAHVFESAPNPRDLKVIVLMIT  2750
2751  GKMEKQELEYLREAVIDAKCKGYLFVILGIGKNVDVKNIYSLASEPNDVF  2800
2801  FKLVSKPGELHEEPLLRFGRLLPSFIRSDFAFYLSPEIRKQCKWLQADQT  2850
2851  PKSPGHTGQKAVYIAPNGTVTQTISTTTTLSTTFKPAASTSAHAKTTTAS  2900
2901  TTAQTRATERPTESTTVQVNATVQSQGSTAANTKATSRTTTSTTAAAASG  2950
2951  RRRQGAKMNDIQITDVTENSARLRWASPEPHNAYVFDLAITLAHDHSLVL  3000
3001  KQNVTGTERVIGGLRSGQKYLVFITGYLKSQPKVTYTGTFSTKTPAQPKV  3050
3051  ALANMMLNAEPLEGPENVMDICLLQKEEGTCRDFVLKWHYDLKTKSCARF  3100
3101  WYGGCGGNENRFNTQKECEKACSPGNISPGVVTTIGT               3137

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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