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

NucPred

Fetching Q9M3G7 from www.uniprot.org...

The NucPred score for your sequence is 0.89 (see score help below)

   1  MKLQNPDKKTLREGFSQESSVVALDSGVLAMSGLKCDGKFPVKDVLMEEG    50
  51  GDKVRKIQVSGGNISLVVDFSGARTSSNNFFESNASCVNENLVKGNGYRE   100
 101  DETQEFLVGNLVWVMTKYKKWWPGEVVDFKADAKESFMVRSIGQSHLVSW   150
 151  FASSKLKPFKESFEQVLNQRNDNGFFDALQKAMSLLSNSLKLDMTCSCIA   200
 201  DGNGIVSAQNITTRKNKPLILREFSVDRLEPKEFVTQLKNIAKCVLNAGV   250
 251  LESTVMQSQLSAFYTLFGHKQIPMAQLHENEGRKSFTAKMSDSKFIGSPS   300
 301  ICAGNSRKRFRKEWFRKFVSEVDNVSARDDLVNVPPSDLISKLKLLAVGY   350
 351  NCSEETENIGLFEWFFSKFRISVYHDENAYKMQLANMAGFKDLMLATNAN   400
 401  RGTVQKTLKSKKIGKSKMEPLNGVSVADTEQKTFELQISKKSNIESLNGV   450
 451  SVADTEQKTFELQILEKSNIESLNGVSTPNIDHEASKSNNSGKTKINHII   500
 501  GHSNFPSSVAKVQLAKDFQDKLLVQAPDRKAMTADTLSRPAAILVPDLNS   550
 551  GGNALGTAEFDHMQRPETLIQHNVCPQEEKTPRSTILNFQVTAHQGVSGT   600
 601  QFVSSQPTSYKHFTSADLFTYSGKKKRGRKRKNAEELPIVAHASATTGIP   650
 651  DLNGTNTEPTLVLPQVEPTQRRRRRKKEESPNGLTRGITILFLKFSSQVS   700
 701  MPSRDDLTSTFSAFGPLDSSETHVSEEFSGAQVAFVSSADAIEAVKSLEK   750
 751  ANPFGETLVNFRLQQKLITVQRNIAPRMPVISHVSPVPKPNNIPTSMDAM   800
 801  RQNLLMMTAMLEKSGDSLSRETKAKLKSEITGLLEKDGVKLLNTWLEGER   850
 851  SITFCRFLSQNTAKLKLDEIPNAETWPFLVKLLLQCVSMEVSGSKRRMPK   900
 901  PTFAKTLRVVVQRTEETKFPGVQFPLLSMAKTLFTHVHDILSNTPSFQSE   950
 951  YGTILRHLLEIKEYRFQMRKRTYSSLVLLYMERAETGFCEKNSGQHSQKE  1000
1001  EAFRYILTLQSLLENSPGDFPDDLREEIVNGLIHIFSSVRDEGKLSRKLI  1050
1051  ECVNTFLLKDGPNLGSLSLEIHNAVEQFVFRCWLTTHDKNLKEILVSYGR  1100
1101  LQLNLTRDSSESSSLVEQLLDVVTRELDLGSSSSSASWGDTTKDEKLGAL  1150
1151  SSYQNSLVELAAHVFYRACVNTSRPSLSEKRARRQHIAMRMVDALTEGKW  1200
1201  LWCAAFGCLVRNYCARINMDLLIYWFEAICTNFQRLLEDASMRRSYDGLL  1250
1251  WTLRSLQGLSSGLSLPDITMDISKSSASSSELDRGWQSIWSSLIHGLATF  1300
1301  SSMSVIVDAVLVLLGSIISSNHITVKILPQEVWDHQLFRHIPSEPALYFI  1350
1351  ACYFSRMGCQGNLQDDLHLRRNLLRAVCAPLSWKVRLTLDERMVQLLPAA  1400
1401  AFSLCAGFKVSLPLPKEHLPTPSQWDVCEQIDDVDRERNFGLFECSVEAL  1450
1451  TRICSNSSKISGCQVPDVVQLPLVLRDPLLHDMDIYFLSIIPEVKEKGPL  1500
1501  SDIFMGCALLCHFMHGSYITRKGKGSSSFFLKACQYLLEGLDHAVESVSK  1550
1551  SLNDLQRRGSLGFGSDFNEKGSIIVSLRSFTQSPVFSNRRDQNLLGASYD  1600
1601  FVIHSLENLLRSFAKVYEEYTEHAWNTHSDTVPSKSLAPDSPEVGRIVDM  1650
1651  DLDLAEDTKERDIIAAGGKAVPGLPVSMGNWKLGMVSLISCFSPVLQFPT  1700
1701  WDVLYNLLEKESDPKVLENILYHLCKLSCLTSIPKVDDLVIFLDGMLSTQ  1750
1751  VKMKRNCLNIVTALHVLLHTLSSSRRDSSGVEKNCGLSLKEAESFQVFVQ  1800
1801  LGAMVNKVSEFGLLGWFGRVKLINCICDLVLLNPQTGQTMIERLLLMLSD  1850
1851  SDYRVRFVLARQIGILFQTWDGHEALFQDICSSFGIKLVTSSKEKLVTAK  1900
1901  DVLAVGPQPRQKMETVIITLMHLAYHSENIELQAVFMMCAVSAKDPCQRE  1950
1951  LIIAALDNLSAQLHYPSRFKYLEELLGPILFHWIASGVSLAGLIETSQLF  2000
2001  IPNAEPKYFIHFCSHWLLPALLLHEDHTNLDWVAKMAGQPVVVLVKENFV  2050
2051  PIFSICMGLHCSKTSECDKGAMVLQNSILYVGETSENERDKLIKQNMVSI  2100
2101  VSFILSCASSSPEPPVPTFSRDTISLAVQTVVDGFLENTDYPKNAAITDR  2150
2151  INIFRPDRVFMFITEMHYRMSAACHHRHTRHHLAALEELTILLGHRALVP  2200
2201  SSLNYIFNLVGQFIGYPSLQDQCCSIASCLLDLFKSNPAKEIVSVLGDQL  2250
2251  QFLVSKLVTCCIDAEADTKISGAKSSQLVNLLHKLVVSSDSSLNEDIRDL  2300
2301  EPLPDLKYFQVIRESHIRICEAYSPRNHLLKVEHSTFLIYIFLEILSLSN  2350
2351  FLFLSCSTIQQCSRRSNYLPPRFLSRSLQALHNKLIASEVSQEDTNGETA  2400
2401  ETFWQSDDEIVNAVWTLVRVSASDEADSMRLLVSDFLSRIGIRDPHTVVF  2450
2451  HLPGNLVSMHGLQGFGHNTGSKVRSLTENGISDETLITLLNFLKKYLLDD  2500
2501  SVKIIDVTSQTLRGILSTERGQQALSSFDSCERALIEVHGRGVNLDIVEK  2550
2551  ILLDSQKQFKAEKFSLETPEVWSTDNKNFDRWICQLVYCMIALCEDVPIR  2600
2601  LCQNIALLKAEISELLFPSVVVSLAGRIGMDINLHDLITSQVKEHIFTDS  2650
2651  NKLTKSKQVMLNTLNELRMCYVLERSIFSGQTKREKNSRSCSTAAKIRDV  2700
2701  ESGSNGMAASITTNWEKVYWLSIDYLVVAGSAVVCGAYLTASMYVEYWCE  2750
2751  EKFGNLSLGDPDFSYHDKLPDHVEILVSAITRINEPDSLYGVIHSNKLSA  2800
2801  QIITFEHEGNWTRALEYYDLQARSQKMVVPSSLSENLEVEQFQPTTSARH  2850
2851  SVFGEGEVQRQPFKGLIRSLQQTGCMHVLDLYCRGLTSREGCFQYDPEFI  2900
2901  ELQYEAAWRAGKWDFSLLYPQTHCQPLQHAKNNNYHESLHCCLRALQEGD  2950
2951  YDGFYGKLKDTKKELVLSISRASEESTEFIYSTVVKLQILHHLGLVWDLR  3000
3001  WTTSSHQSVHGYLVKQMACVDPVIPTMDQLSWLNKDWNSIITQTQLHMTL  3050
3051  LEPFIAFRRVLLQILGCEKCTMQHLLQSASLLRKGTRFSHAAASLHEFKF  3100
3101  LCARSNGQQPVPDWLGKLEEAKLLHAQGRHEVSISLANYILHNYQLKEEA  3150
3151  SDIYRVIGKWLAETRSSNSRTILEKYLRPAVSLAEEQSSKICKRLVDRQS  3200
3201  QTWFHLAHYADALFKSYEERLSSSEWQAALRLRKHKTKELEVFIKRFKSS  3250
3251  KKAEQSDYSLKIQDLQKQLTMDKEEAEKLQVDRDNFLKLALEGYKRCLEI  3300
3301  GDKYDVRVVFRQVSMWFSLASQKNVIDNMLSTIKEVQSYKFIPLVYQIAS  3350
3351  RLGSSKDESGSNSFQSALVSLIRKMAIDHPYHTILQLLALANGDRIKDNQ  3400
3401  RSRNSFVVDMDKKLAAEHLLQDVSHYHGPMIRQMKQLVDIYIKLAELETR  3450
3451  REDTNRKVALPREIRSVKQLELVPVVTATIPVDRSCQYNEGSFPFFRGLS  3500
3501  DSVTVMNGINAPKVVECFGSDGQKYKQLAKSGNDDLRQDAVMEQFFGLVN  3550
3551  TFLHNNRDTWKRRLAVRTYKVIPFTPSAGVLEWVDGTIPLGDYLIGSSRS  3600
3601  EGAHGRYGIGNWKYPKCREHMSSAKDKRKAFVDVCTNFRPVMHYFFLEKF  3650
3651  LQPADWFVKRLAYTRSVAASSMVGYIVGLGDRHAMNILIDQATAEVVHID  3700
3701  LGVAFEQGLMLKTPERVPFRLTRDIIDGMGITGVEGVFRRCCEETLSVMR  3750
3751  TNKEALLTIVEVFIHDPLYKWALSPLKALQRQKETEDYDGMNLEGLQEEF  3800
3801  EGNKDATRALMRVKQKLDGYEGGEMRSIHGQAQQLIQDAIDTDRLSHMFP  3850
3851  GWGAWM                                              3856

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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