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

NucPred

Fetching Q6ZTR5 from www.uniprot.org...

The NucPred score for your sequence is 0.67 (see score help below)

   1  MNTQKGSLTINVHRGSLAMSIQRGSLVPRDMDSSGRDMQLRVIPAEVKFL    50
  51  DTMAGRVYRLPITVHNICRWNQKIRFKEPVKPQFKLMLTSLDKELASGLQ   100
 101  MTAMVEYHPDKDEDTFDRLLISIENKTTEIPLIGLIPSCQLEIESVVNFG   150
 151  TLVANSKVYSKEITITNHGKAPGIFKAEYHGQLPILIFPTSGIVDAKSSM   200
 201  VIKVDFCADQPRIVDEEAIVILQGQPEMLLSIKAHVVEQIIELLSMSSDR   250
 251  RLECIHFGPVFFGSSKIKHARVYNNSPEPINWVAIIQDDAVGEELGTDIQ   300
 301  QRTDIALNNLTYIRKIKNIDTTIIISCLPNEGTLQPYQKTVITFCFTPKL   350
 351  MAVGKKDIGPSYRQDYALFLRFESVGSKDGFLRDDDYKTIKSERFQKVEL   400
 401  ALTGTGLPVLLQFDPGPVLNFKPCFMGERSEIQCIIKNQCELLPVTYHFK   450
 451  KTANFEIDPEKGKITGGGMVDVMCSFVPHQLGVFKVKQMIEIIGLVAEED   500
 501  LQSLSVKSFHHVYLAFNSICKASTKKVVMKFDPGILPSIRNPTGKFVVKD   550
 551  LAKRKNYAPVAMLQSAMTRTHNHRSCEEPVKDMLLAFPNDRAATIRSKDH   600
 601  HKHFRPIFTKVPRFNYVNHDFAYTTFEKQQKKLHENYYAMYLKYLRSVRL   650
 651  QKKQAERERMYSYDDTDIGLEPGSGLKSPSLSEAEIEEELSSAANSIRAN   700
 701  RLLTTRGIASQEEESVRRKVLKGLKSEPSTPQEKHDCSLMLTPKQIHQVI   750
 751  VGPSVLNFGNICVNSPNTHLLHVINMLPMHVLLQLDTDLEELQKTNQFSY   800
 801  VILPTSSTYISMVFDSPTIGKFWKSFTFTVNNVPSGHILVVAVVQPVTLE   850
 851  LSSNELVLRPRGFFMKTCFRGTVRLYNRQNCCAQFQWQPVNTGRGIAFSI   900
 901  CPAKGTVEAYSSLECEVTWQQGFSSPEEGEFILHVFQGNALKLKCVAHLG   950
 951  RTKVLLLQPRILFSNCPQGLTTWRKAILQNVGQNHAYFKVCSQSLLPIIN  1000
1001  IIPSQGIVPFGGITVLNISCKPTVAEKFDTRAKVSIRHANVIDLRIGGSA  1050
1051  EIADVEINPDVFNFSGAYIGGTQIIPFVIKNKGITRARVEFNLKDFPDFS  1100
1101  MDLKDKSEEFKDPAVPYIYSLELEENTSLECSITFSPKEVTVVEFIIQVQ  1150
1151  INFFESSKLYTKYLSSSPSNPKTVPLIRPCYVQATALQSPLNLSSTKFVF  1200
1201  EIPLHEMNPNNKVTKTQNLVLYNITKHHVTWTLDLSNTGKLFKDGTFKFS  1250
1251  VLNGILRPNEKYNVSISFCPNRPGTYTADIPMLLNYIPVCYKILHLTGEV  1300
1301  KSPELLFDPPFIFFTPVPLDITTVMDINILPQNYFRNSTLCVQIPTVRLL  1350
1351  DGEEIHPLSVKFPKGRVIPGSHSGINNKLTCHLSFKSSKPVSFFTNLLFC  1400
1401  DDRKNWFSLPVTATAENCILTIYPYMAIHLDKQNIILKNDKDEYLKKTRD  1450
1451  GVLPPYQDAKPPSPASIKKTYTTSKFNDAEPAKGNLFIGVEVLPENLHLD  1500
1501  ESETSEEDHGSLEKEKYEQFLSLEEGTKAHYFFEKVVNAAQTWFSLFGWP  1550
1551  EGPHSFSIPETIRRDVYKMQFYSSTSPPQKFSRQNDFSKYNKTIYDVLLH  1600
1601  LSGKMPPGINSSQSLPVDNHEKRVIQLHLQHSSLLDFLNAQGGCISHVLP  1650
1651  EFLLEPEDYKRWIEIMSSTNTMPVSSCTPKKKCSIVIEMSKFEAWSKRAW  1700
1701  TDVFLQIYKVLVLSRVVPYCSNNMPPICVQNTPKVNPCFASSNIYSDSER  1750
1751  ILLSWMNINYENTRHVIWKNCHKDVIPSERWIVNFDKDLSDGLVFATQLG  1800
1801  AYCPFLIESHFINMYTRPKSPEEYLHNCLIIVNTLYEIDFDVEIQATDIC  1850
1851  DPNPILMLMLCVYMYERLPTYLPKKVVSFECTLHDTVLNKILLKNSSSRN  1900
1901  LVYNARIVGRDAADFSLSQKGNVVTISPRNEINVTLKFTSRFIRPAEASL  1950
1951  LLISKPKNAVRGITMTFALKGKVLDFKAIDIIKCESPCYQFQEVTVNVKN  2000
2001  PFHTAGDFSVILVESSTFVSSPTKLTESRQYPKHDDDMSSSGSDTDQGCS  2050
2051  DSPNVLHTSIKSTFIREFFCSMHTVHLGVKGTSSLELRFLPFNMHVRYCV  2100
2101  IILSNKKIGQLIYVAEGKGMTPLPSSCLPMNTSSSPVYYSTTREEGPNKK  2150
2151  YPVLYLKCKPYQILYVDLKLPMTNEAKEKALAFAAQQQMSSIEYERRLIT  2200
2201  GTLESSSIRVAIALLGLTKIETLMLFRISKLRKPKTVSYTTEVSLPKYFY  2250
2251  IPEKISIPWIPEPQVIKLSKAKASDGSVPLPLQFLPLQSGRYPCKILLKS  2300
2301  RYDVRAYYVEGIVNEEQPEAKFEFETPAFEALTQNIPIKNQTNDKWTFQV  2350
2351  TIEGEWFYGPVDLHVGPDEIVEYPLTFKPIFECVITGKLILQNEVDGREH  2400
2401  IFDIKGVGKKPSALEHITVECQVGNVTQKHITLPHFTNTALTFKVTADLP  2450
2451  IVWGNPQITVYPYKEILYLIHVRPWKRGILKGTITFSTTRRCTTRRKHDD  2500
2501  YEEDTDQDQALSCLDSITEQSSILDDADTYGNFNNLRFWYNLEIHSTPGP  2550
2551  PIEIMEMTCIALDSTCIEIPLSNPKDRGLHLEVQLTSAALNGDNEIILSP  2600
2601  LQCTKYIVWYSPATTGYSDESIIFQPEMAEEFWYLLKLTIELPKPTTMPE  2650
2651  IQCDLGKHVTQIIPLVNCTHETLKLQVTNSNPENFVLDINRKSQLIISPH  2700
2701  STTELPVLFYPSALGRADHQACINFYCTQFTEWKFYLSGVGLFPQPLDTE  2750
2751  RITTRIGLQSTIVIPFKNPTMEDVLIDIILTSVEHPRNLVMDHCWDSFIY  2800
2801  ESSAFRFSSPSEIQGIALPPKGNIDISLLFIPQIMKLHKTMVIIEMTKAN  2850
2851  GKYWPIDNFDELDIKFKSIVGIDSEEIQAIHWIYPIVGLPQAPPPKSPPV  2900
2901  VIQCQSRKRAEEKVEIILNAGFFGFSLTPDLTEVLVIPKRNSHNFCEDPN  2950
2951  EIPKIHEFEYEIQFESEAMKSKLESCVALYMIEKSYDIMAKRITFIFNLV  3000
3001  FTPKKPLRSHITLKIECVTEGIWKFPIMLIATEPDTDAVIDIEGVGLFKE  3050
3051  SVFELRLKSQTRNPEPFTAHFLPGSDLEFFVKPQAGELLPFNTNGTLITV  3100
3101  GFKPKMYCRKYKATLVIQTEEMYWKYEINGLTPTTVPPKNAKAKIDATHK  3150
3151  THDNMPVRPHNFVRENTKLIRTGVSSTIKGAPLVKNQ               3187

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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