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

NucPred

Fetching Q5SZK8 from www.uniprot.org...

The NucPred score for your sequence is 0.53 (see score help below)

   1  MHSAGTPGLSSRRTGNSTSFQPGPPPPPRLLLLLLLLLSLVSRVPAQPAA    50
  51  FGRALLSPGLAGAAGVPAEEAIVLANRGLRVPFGREVWLDPLHDLVLQVQ   100
 101  PGDRCAVSVLDNDALAQRPGRLSPKRFPCDFGPGEVRYSHLGARSPSRDR   150
 151  VRLQLRYDAPGGAVVLPLVLEVEVVFTQLEVVTRNLPLVVEELLGTSNAL   200
 201  DARSLEFAFQPETEECRVGILSGLGALPRYGELLHYPQVPGGAREGGAPE   250
 251  TLLMDCKAFQELGVRYRHTAASRSPNRDWIPMVVELRSRGAPVGSPALKR   300
 301  EHFQVLVRIRGGAENTAPKPSFVAMMMMEVDQFVLTALTPDMLAAEDAES   350
 351  PSDLLIFNLTSPFQPGQGYLVSTDDRSLPLSSFTQRDLRLLKIAYQPPSE   400
 401  DSDQERLFELELEVVDLEGAASDPFAFMVVVKPMNTMAPVVTRNTGLILY   450
 451  EGQSRPLTGPAGSGPQNLVISDEDDLEAVRLEVVAGLRHGHLVILGASSG   500
 501  SSAPKSFTVAELAAGQVVYQHDDRDGSLSDNLVLRMVDGGGRHQVQFLFP   550
 551  ITLVPVDDQPPVLNANTGLTLAEGETVPILPLSLSATDMDSDDSLLLFVL   600
 601  ESPFLTTGHLLLRQTHPPHEKQELLRGLWRKEGAFYERTVTEWQQQDITE   650
 651  GRLFYRHSGPHSPGPVTDQFTFRVQDNHDPPNQSGLQRFVIRIHPVDRLP   700
 701  PELGSGCPLRMVVQESQLTPLRKKWLRYTDLDTDDRELRYTVTQPPTDTD   750
 751  ENHLPAPLGTLVLTDNPSVVVTHFTQAQINHHKIAYRPPGQELGVATRVA   800
 801  QFQFQVEDRAGNVAPGTFTLYLHPVDNQPPEILNTGFTIQEKGHHILSET   850
 851  ELHVNDVDTDVAHISFTLTQAPKHGHMRVSGQILHVGGLFHLEDIKQGRV   900
 901  SYAHNGDKSLTDSCSLEVSDRHHVVPITLRVNVRPVDDEVPILSHPTGTL   950
 951  ESYLDVLENGATEITANVIKGTNEETDDLMLTFLLEDPPLYGEILVNGIP  1000
1001  AEQFTQRDILEGSVVYTHTSGEIGLLPKADSFNLSLSDMSQEWRIGGNTI  1050
1051  QGVTIWVTILPVDSQAPEIFVGEQLIVMEGDKSVITSVHISAEDVDSLND  1100
1101  DILCTIVIQPTSGYVENISPAPGSEKSRAGIAISAFNLKDLRQGHINYVQ  1150
1151  SVHKGVEPVEDRFVFRCSDGINFSERQFFPIVIIPTNDEQPEMFMREFMV  1200
1201  MEGMSLVIDTPILNAADADVPLDDLTFTITQFPTHGHIMNQLINGTVLVE  1250
1251  SFTLDQIIESSSIIYEHDDSETQEDSFVIKLTDGKHSVEKTVLIIVIPVD  1300
1301  DETPRMTINNGLEIEIGDTKIINNKILMATDLDSEDKSLVYIIRYGPGHG  1350
1351  LLQRRKPTGAFENITLGMNFTQDEVDRNLIQYVHLGQEGIRDLIKFDVTD  1400
1401  GINPLIDRYFYVSIGSIDIVFPDVISKGVSLKEGGKVTLTTDLLSTSDLN  1450
1451  SPDENLVFTITRAPMRGHLECTDQPGVSITSFTQLQLAGNKIYYIHTADD  1500
1501  EVKMDSFEFQVTDGRNPVFRTFRISISDVDNKKPVVTIHKLVVSESENKL  1550
1551  ITPFELTVEDRDTPDKLLKFTITQVPIHGHLLFNNTRPVMVFTKQDLNEN  1600
1601  LISYKHDGTESSEDSFSFTVTDGTHTDFYVFPDTVFETRRPQVMKIQVLA  1650
1651  VDNSVPQIAVNKGASTLRTLATGHLGFMITSKILKVEDRDSLHISLRFIV  1700
1701  TEAPQHGYLLNLDKGNHSITQFTQADIDDMKICYVLREGANATSDMFYFA  1750
1751  VEDGGGNKLTYQNFRLNWAWISFEKEYYLVNEDSKFLDVVLKRRGYLGET  1800
1801  SFISIGTRDRTAEKDKDFKGKAQKQVQFNPGQTRATWRVRILSDGEHEQS  1850
1851  ETFQVVLSEPVLAALEFPTVATVEIVDPGDEPTVFIPQSKYSVEEDVGEL  1900
1901  FIPIRRSGDVSQELMVVCYTQQGTATGTVPTSVLSYSDYISRPEDHTSVV  1950
1951  RFDKDEREKLCRIVIIDDSLYEEEETFHVLLSMPMGGRIGSEFPGAQVTI  2000
2001  VPDKDDEPIFYFGDVEYSVDESAGYVEVQVWRTGTDLSKSSSVTVRSRKT  2050
2051  DPPSADAGTDYVGISRNLDFAPGVNMQPVRVVILDDLGQPALEGIEKFEL  2100
2101  VLRMPMNAALGEPSKATVSINDSVSDLPKMQFKERIYTGSESDGQIVTMI  2150
2151  HRTGDVQYRSSVRCYTRQGSAQVMMDFEERPNTDTSIITFLPGETEKPCI  2200
2201  LELMDDVLYEEVEELRLVLGTPQSNSPFGAAVGEQNETLIRIRDDADKTV  2250
2251  IKFGETKFSVTEPKEPGESVVIRIPVIRQGDTSKVSIVRVHTKDGSATSG  2300
2301  EDYHPVSEEIEFKEGETQHVVEIEVTFDGVREMREAFTVHLKPDENMIAE  2350
2351  MQLTKAIVYIEEMSSMADVTFPSVPQIVSLLMYDDTSKAKESAEPMSGYP  2400
2401  VICITACNPKYSDYDKTGSICASENINDTLTRYRWLISAPAGPDGVTSPM  2450
2451  REVDFDTFFTSSKMVTLDSIYFQPGSRVQCAARAVNTNGDEGLELMSPIV  2500
2501  TISREEGLCQPRVPGVVGAEPFSAKLRYTGPEDADYTNLIKLTVTMPHID  2550
2551  GMLPVISTRELSNFELTLSPDGTRVGNHKCSNLLDYTEVKTHYGFLTDAT  2600
2601  KNPEIIGETYPYQYSLSIRGSTTLRFYRNLNLEACLWEFVSYYDMSELLA  2650
2651  DCGGTIGTDGQVLNLVQSYVTLRVPLYVSYVFHSPVGVGGWQHFDLKSEL  2700
2701  RLTFVYDTAILWNDGIGSPPEAELQGSLYPTSMRIGDEGRLAVHFKTEAQ  2750
2751  FHGLFVLSHPASFTSSVIMSADHPGLTFSLRLIRSEPTYNQPVQQWSFVS  2800
2801  DFAVRDYSGTYTVKLVPCTAPSHQEYRLPVTCNPREPVTFDLDIRFQQVS  2850
2851  DPVAAEFSLNTQMYLLSKKSLWLSDGSMGFGQESDVAFAEGDIIYGRVMV  2900
2901  DPVQNLGDSFYCSIEKVFLCTGADGYVPKYSPMNAEYGCLADSPSLLYRF  2950
2951  KIVDKAQPETQATSFGNVLFNAKLAVDDPEAILLVNQPGSDGFKVDSTPL  3000
3001  FQVALGREWYIHTIYTVRSKDNANRGIGKRSVEYHSLVSQGKPQSTTKSR  3050
3051  KKREIRSTPSLAWEIGAENSRGTNIQHIALDRTKRQIPHGRAPPDGILPW  3100
3101  ELNSPSSAVSLVTVVGGTTVGLLTICLTVIAVLMCRGKESFRGKDAPKGS  3150
3151  SSSEPMVPPQSHHNDSSEV                                 3169

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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