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

NucPred

Fetching Q5VT06 from www.uniprot.org...

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

   1  MRSSKSKEVPLPNPRNSQSKDTVQADITTSWDALSQTKAALRHIENKLEV    50
  51  APTSTAVCDSVMDTKKSSTSATRKISRKDGRYLDDSWVNAPISKSTKSRK   100
 101  EKSRSPLRATTLESNVKKNNRVEFREPLVSYREIHGAPSNFSSSHLESKH   150
 151  VYCVDVNEEKTESGNWMIGSREERNIRSCDFESSQSSVINDTVVRFLNDR   200
 201  PAIDALQNSECLIRMGASMRTEEEMPNRTKGSENNLKLSVNNMAHDTDPK   250
 251  ALRLTDSSPSSTSTSNSQRLDILKRRQHDVKLEKLKERIRKQWEHSEETN   300
 301  GRGQKLGHIDHPVMVVNVDNSVTAKVRKVATAPPAPAYKGFNPSETKIRT   350
 351  PDGKVWQEAEFQNMSRELYRDLALHFADDISIKEKPAEKSKEKKVVKPVR   400
 401  KVQKVAQLSSTECRTGSSHLISTSSWRDGQKLVKKILGPAPRMEPKEQRT   450
 451  ASSDRGGRERTAKSGGHIGRAESDPRLDVLHRHLQRNSERSRSKSRSENN   500
 501  IKKLASSLPDNKQEENTALNKDFLPIEIRGILDDLQLDSTAHTAKQDTVE   550
 551  LQNQKSSAPVHAPRSHSPVKRKPDKITANEDPPVISKRRHYDTDEVRQYI   600
 601  VRQQEERKRKQNEEKKAQKEATEQKNKRLQELYRKQKEAFTKVKNVPPSE   650
 651  PSATRRLQETYSKLLLEKTLLEEPSHQHVTQETQAKPGYQPSGESDKENK   700
 701  VQERPPSASSSSDMSLSEPPQPLARKDLMESTWMQPERLSPQVHHSQPQP   750
 751  FAGTAGSLLSHLLSLEHVGILHKDFESILPTRKNHNMASRPLTFTPQPYV   800
 801  TSPAAYTDALLKPSASQYKSKLDRIEALKATAASLSSRIESEAKKLAGAS   850
 851  INYGSAWNTEYDVQQAPQEDGPWTKAVTPPVKDDNEDVFSARIQKMLGSC   900
 901  VSHATFDDDLPGVGNLSEFKKLPEMIRPQSAISSFRVRSPGPKPEGLLAQ   950
 951  LCKRQTDSSSSDMQACSQDKAKISLGSSIDSVSEGPLLSEGSLSEEEGDQ  1000
1001  DGQPLLKVAEILKEKEFCPGERNSYEPIKEFQKEAEKFLPLFGHIGGTQS  1050
1051  KGPWEELAKGSPHSVINIFTKSYQLYGKGFEDKLDRGTSTSRPLNATATP  1100
1101  LSGVSYEDDFVSSPGTGTSTEKKSTLEPHSTLSPQEDHSNRKSAYDPSSV  1150
1151  DVTSQHSSGAQSAASSRSSTSSKGKKGKKEKTEWLDSFTGNVQNSLLDEE  1200
1201  KAERGSHQGKKSGTSSKLSVKDFEQTLDTDSTLEDLSGHSVSVSSDKGRS  1250
1251  QKTPTSPLSPSSQKSLQFDVAGTSSERSKSSVMPPTITGFKPNAPLTDLN  1300
1301  PAASRTTTENMAPIPGSKRFSPAGLHHRMAAELSYLNAIEESVRQLSDVE  1350
1351  RVRGISLAQQESVSLAQIIKAQQQRHERDLALLKLKAEQEALESQRQLEE  1400
1401  TRNKAAQVHAESLQQVVQSQREVTEVLQEATCKIAAQQSETARLTTDAAR  1450
1451  QICEMAELTRTHISDAVVASGAPLAILYDHQRQHLPDFVKQLRTRTETDR  1500
1501  KSPSVSLSQSKEGTLDSKHQKYSASYDSYSESSGYKNHDRRSSSGSSRQE  1550
1551  SPSVPSCKENEKKLNGEKIESSIDEQVQTAADDSLRSDSVPSLPDEKDST  1600
1601  SIATEYSLKFDESMTEDEIEEQSFRSLLPSESHRRFNMEKRRGHHDDSDE  1650
1651  EASPEKTTLSTAKELNMPFSGGQDSFSKFTMEMVRQYMKEEEMRAAHQSS  1700
1701  LLRLREKALKEKTKAELAWLEHQKKHLRDKGEDDKMPPLRKKQRGLLLRL  1750
1751  QQEKAEIKRLQEANKAARKERQLILKQQEEIEKIRQTTIKLQEKLKSAGE  1800
1801  SKLDSHSDDDTKDNKATSPGPTDLETRSPSPISISSSETSSIMQKLKKMR  1850
1851  SRMDEKFLTKREQKLMQRRQHAEELLEWKRRLDAEEAEIRQMEKQALAAW  1900
1901  DKELIKPKTPKKELEDQRTEQKEIASEEESPVPLYSHLNSESSIPEELGS  1950
1951  PAVEYVPSESIGQEQPGSPDHSILTEEMICSQELESSTSPSKHSLPKSCT  2000
2001  SVSKQESSKGSHRTGGQCHLPIKSHQHCYSWSDESLSMTQSETTSDQSDI  2050
2051  EGRIRALKDELRKRKSVVNQLKKEQKKRQKERLKAQEASLIKQLESYDEF  2100
2101  IKKTEAELSQDLETSPTAKPQIKTLSSASEKPKIKPLTPLHRSETAKNWK  2150
2151  SLTESERSRGSLESIAEHVDASLSGSERSVSERSLSAYAKRVNEWDSRTE  2200
2201  DFQTPSPVLRSSRKIREESGDSLENVPALHLLKELNATSRILDMSDGKVG  2250
2251  ESSKKSEIKEIEYTKLKKSKIEDAFSKEGKSDVLLKLVLEQGDSSEILSK  2300
2301  KDLPLDSENVQKDLVGLAIENLHKSEEMLKERQSDQDMNHSPNIQSGKDI  2350
2351  HEQKNTKEKDLSWSEHLFAPKEIPYSEDFEVSSFKKEISAELYKDDFEVS  2400
2401  SLLSLRKDSQSCRDKPQPMRSSTSGATSFGSNEEISECLSEKSLSIHSNV  2450
2451  HSDRLLELKSPTELMKSKERSDVEHEQQVTESPSLASVPTADELFDFHIG  2500
2501  DRVLIGNVQPGILRFKGETSFAKGFWAGVELDKPEGNNNGTYDGIAYFEC  2550
2551  KEKHGIFAPPQKISHIPENFDDYVDINEDEDCYSDERYQCYNQEQNDTEG  2600
2601  PKDREKDVSEYFYEKSLPSVNDIEASVNRSRSLKIETDNVQDISGVLEAH  2650
2651  VHQQSSVDSQISSKENKDLISDATEKVSIAAEDDTLDNTFSEELEKQQQF  2700
2701  TEEEDNLYAEASEKLCTPLLDLLTREKNQLEAQLKSSLNEEKKSKQQLEK  2750
2751  ISLLTDSLLKVFVKDTVNQLQQIKKTRDEKIQLSNQELLGDDQKKVTPQD  2800
2801  LSQNVEEQSPSISGCFLSSELEDEKEEISSPDMCPRPESPVFGASGQEEL  2850
2851  AKRLAELELSREFLSALGDDQDWFDEDFGLSSSHKIQKNKAEETIVPLMA  2900
2901  EPKRVTQQPCETLLAVPHTAEEVEILVHNAAEELWKWKELGHDLHSISIP  2950
2951  TKLLGCASKGLDIESTSKRVYKQAVFDLTKEIFEEIFAEDPNLNQPVWMK  3000
3001  PCRINSSYFRRVKNPNNLDEIKSFIASEVLKLFSLKKEPNHKTDWQKMMK  3050
3051  FGRKKRDRVDHILVQELHEEEAQWVNYDEDELCVKMQLADGIFETLIKDT  3100
3101  IDVLNQISEKQGRMLLV                                   3117

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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