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

NucPred

Fetching Q10122 from www.uniprot.org...

The NucPred score for your sequence is 0.80 (see score help below)

   1  MPEEEFEWNESKLNALSEILTQWAPHDELKLENVILEAKMAWIHESTDAV    50
  51  ALEKEFEKLLNTSFDPLMDYEEHQRVKNGVCSPAKSTKLDWITKWANIAE   100
 101  KLSISHINCSKAPHKTINFQRLQILPNMTSILAIAHTEADNVILIYGHTK   150
 151  EGIDFQTVLRHSYSNIPDGSLQTIHFLTKNLLDLYSQLFSRKITAEFKTR   200
 201  HFRCLPSFWLQYDVLAPIFEQFRAPRAPRIPLDEATSNWANRKWENYQYL   250
 251  TYLNDITGRVRGEVHNHPIFPWVCDFSEENGGFRQLNRTKYRLCKGDDQL   300
 301  REMYSREPSHHVPELLSDIGYMVYRARVEPKDNLCRHVRRKWVPEEYPST   350
 351  MSRMYQWTPDECIPEFYDDPSIFNSCHPDMADLRFPEFVSSPQEFIEWHR   400
 401  KMLEHEEVSMNLHRWIDLVFGFNLAIDNSKNALNLHLCFVEKNRRGLRTT   450
 451  GMVQLFNRPHPIRMPLNYDPKLDNYHLKMESFGFGMTSEHRKEPEVEPDE   500
 501  DSHYQIYQKIKKVRRLRHNTYFSSMVSAMEVMAQIVLAPHLAGRFDDPDH   550
 551  IRRCIQLYSYRIPANYRRLFDFLFNTEQDFPDCDEFSFFVSVRLNIPTKI   600
 601  LNFSEEFGKCVSLHILRKLDVIPPFSKRSQLIILKEVESLKKSIRLCDHM   650
 651  EQCVVAAFQQLLEDEEACIQSVHRLMPVITRSLSQSALEDLINPMIELIQ   700
 701  CETSVKLLDRRFLMHVSICYGTHTFLDLFLPPIVEACASMNCDRSVVAKE   750
 751  SIMWLAKRYGPVICAKFISSNVLRIMASCYEAFEMVGLEQQPKAVFNVVL   800
 801  QGDETCSRIESLLSEIVLTYSVTFITVQFLPFCVDLIEQFHKRSSVQLEP   850
 851  GLVSVFRIVELSIRSMSDHQLMNYLEEFIIQKVIYRVLTILLDASFQFSS   900
 901  MRVRIIIICKVCQLLHSITQKIGTENTRIYANQPFKLMFSTFSEIYETDE   950
 951  ELRINLRKRPSENTLFEVPLWMVEDVVDKFAKEWGVPFLSSFCDDPAFLI  1000
1001  PFVSNSSSSSSPPASIAHSPPSAFTAYSLGGMSSGNRLFSLSASSPVNSV  1050
1051  NSLGGLSFCDSGSLSAVWCARVSAAVCGVDNYRFDHLSLCNYTGHQEKIR  1100
1101  KLAAISNENSFVSASSDKTVKLWSIKPELDEIGCQWTYQKHTRPVHDITI  1150
1151  LADNSIASTDGVLHVWDPFRTTLLAQMEWDSKEGSGGNIMRVENVDRHIL  1200
1201  SAICSLHSTVKLFDSRVGGWTCELKVSPGPGLTRAITVRDKGNKMAVALS  1250
1251  NGTLAILDARNGKINALAQTNSTHTVSVNWLSDTRLLVCDADECGIFLET  1300
1301  NPRAHIVRKLQDPVSAACLTDNSLVTLQNGTILRVYRNSGELQIETKIRP  1350
1351  DELPGTPTAVLPLPLNCSYLIGSSHGAIRLMC                    1382

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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